WO2018004464A1

WO2018004464A1 - Large scale machine learning-based chiller plants modeling, optimization and diagnosis

Info

Publication number: WO2018004464A1
Application number: PCT/SG2017/050324
Authority: WO
Inventors: Kok Soon Chai; Choon Hoo LAI
Original assignee: Kirkham Group Pte Ltd
Current assignee: Kirkham Group Pte Ltd
Priority date: 2016-06-29
Filing date: 2017-06-29
Publication date: 2018-01-04
Anticipated expiration: 2018-12-29
Also published as: SG11201810279QA

Abstract

The invention relates to a data driven, or a hybrid rule-based and data driven Energy/Building Management System, such as for chiller plants, which has ability to learn from the data and evaluate performance. According to the invention, a computer-implemented method trains prediction models for each equipment model and chiller plant model using baseline data, predicts a parameter for each equipment model and chiller plant model using baseline data, computes differential parameter of each equipment based on the predicted and actual parameters of each equipment, computes differential parameter of the chiller plant based on the predicted and actual parameters of the chiller plant, compute a differential parameter resulting from chiller plant optimization, by subtracting the differential parameters of the various equipment from the differential parameter of the chiller plant,ascertaining a presence of abnormality in the differential parameter resulting from chiller plant optimization and generating a notification if the differential parameter resulting from chiller plant optimization is ascertained abnormal.

Description

Large Scale Machine Learning-based Chiller Plants Modeling, Optimization and

Diagnosis

Field of Invention

Embodiments of the invention relate to energy or management system (EMS/BMS) for buildings and chiller plants, particularly to a data driven, or a hybrid rule-based and data driven EMS/BMS, and method and system for modeling, optimizing and evaluating chiller plant and chiller plant equipment.

Background

Description of Related Art Chiller plant optimization is one of the most crucial tasks to smart building systems, as the energy consumption of a chiller plant comprises over 40% of the total energy consumption of a modern building. Poor efficiency is commonly observed in existing chiller plant systems, due to the excessive overhead and technical challenges faced in manual tuning. In practice, a large number of chiller plants and buildings are optimized during the first few months in operation, when experienced engineers spend huge efforts on fine-tuning the chiller plants to achieve near-optimal performance. However, the efficiency of these chiller plants deteriorates quickly when the engineers with expertise leave the projects, such that the configuration of the chiller plant does not adapt well to the varying environmental and equipment conditions. A fully optimized chiller plant may run at excellent efficiency during office hours, but performs poorly at nights/weekends/public holidays. Moreover, even veterans in the industry may not always make correct decisions on chiller plant optimization. The extremely high complexity of chiller plants often leads to ineffectiveness of conventional tuning tricks used by the engineers. The best engineers may only tune the chiller plant system in a trial-and-error fashion, trying to understand the chiller plants with their experience and sometimes simple heuristics.

A chiller plant consumes 30-40% of power consumption in a building. Chiller plant power is calculated by the summation of the total power consumption of chiller, chilled and condenser water pumps and cooling towers. Sophisticated tradeoff of comfort and equipment operating conditions etc. are required to minimize the total power consumption of the chiller plant over a long period of time. The existing method relies on a data driven model that accurately models and optimizes a type of chiller plant for a short period of time. This method requires time consuming effort to develop new models for different types of chiller

l plant. The model is also a best fit for the data for a relatively short period of time, but it loses predicting accuracy over new data set after a period of chiller plant operation.

Reference is made to [1 ] and [2]. In [1 ], Nguyen developed Table 1 to describe the existing HVAC optimization process that is divided into Preprocessing, Running Optimization and Post-processing. In [2], neural network was applied to air conditioning and chiller plants systems.

Table 1 - General HVAC optimization process

[1 ], [2] and other existing systems focus on model development and system optimization for one specific chiller plant and for relatively short period of time. For any of the existing systems to be suitable as an industrial solution, it will face major challenges to meet "scalability, adaptability and continuous learning" requirements.

Summary The invention can be implemented, but not limited to as an energy or management system (EMS/BMS) for buildings and chiller plants. In particular, the invention is to transform existing EMS/BMS from rule-based to a data driven, or a hybrid rule-based and data driven EMS/BMS. Under rule-based EMS/BMS, control engineers develop a set of rules based on domain expertise in advance during the design stage. The rule-based EMS/BMS use predetermined rules to control HVAC equipment with set points. During operation and maintenance stage, the field engineers and technicians update the set points to improve energy efficiency, comfort or maintenance. However, the rule-based EMS/BMS lacks the ability to improve by itself with experience because it lacks the ability to learn from the data. The invention provides the EMS/BMS with ability for autonomous learning and control. The invention is related to the application of a data driven, machine learning-based control system (may be referred to as "Learning-based Energy Optimization system" or "LEO system") that uses machine learning model for diagnostics and energy efficiency optimization of chiller plants. The system reads and processes measurement and verification (M&V) sensor data for chiller plant such as chilled water and condenser temperatures and flow rates, equipment power, and learns to represent the equipment in the chiller plants to predict equipment and chiller plant power.

One aspect of the invention covers the application of a method and system to use data driven model and sub-models, e.g. neural networks with inputs layers, multiple hidden layers, and output layers, to represent the actual equipment and predict equipment and chiller plant power, performance and efficiency. The system learns to represent the chiller plant and equipment high level characteristics from relative accuracy of the predicted values. It further learns to represent detailed equipment characteristics such as but not limited to equipment efficiency, performance etc. to provide data driven, detailed and actionable diagnostics information for further analysis, diagnostics or energy efficiency optimization. Another aspect of the invention covers the application of the data driven and deep learning with model and sub-models to predict and evaluate efficiency and performance of the system and various equipment. The model and sub-models are trained to predict system and equipment power and performance using trained data, X_train, and evaluated with a set of accuracy matrix, M_acc. The model and sub-models are evaluated with a set of cross validation data, X_cr0Ss, for accuracy evaluation. By evaluating and meeting certain accuracy requirements in M_acc, the model and sub-models are used as the baseline for predicting power, performance and efficiency from the M&V data, X_test, of the equipment and of chiller plant in the future. The model and sub-models predict the performance values of the system and equipment to be used for comparison for the actual performance values. The system refers to the accuracy matrix and variance in performance of system and subsystems between the training X_train, cross validation X_cross and testing x_tes_t data sets to conclude performance evaluation of the system and subsystem over time.

Another aspect of the invention covers a method and system that refers to the performance matrix of equipment for large scale chiller plant comparisons and cross learning. Another aspect of the invention covers a data driven "universal" model that generalizes the system and equipment performance, power and efficiency using the measurement and verification sensor data of the chiller plants that the model is trained on, as well as the cross validation data and testing data that the model have not been trained on. This includes a method and system to achieve the best trade-off for optimizing the generalization of any chiller plants. It includes a method to model the chiller plant life cycle for continuous and automated learning, modelling and optimization.

According to one aspect of the invention, a computer-implemented method is provided and comprises: training a plurality of prediction models using first baseline data, the prediction models being for chiller plant and a plurality of equipment comprising cooling tower (CT), condenser water pump (CWP), chiller, chilled water pump (CHWP); computing, using the prediction models, a plurality of predicted parameters of the plurality of equipment and the chiller plant using test data; computing a plurality of differential parameters of the plurality of equipment based on the predicted parameters of the plurality of equipment and a plurality of actual parameters of the plurality of equipment; computing a differential parameter of the chiller plant based on the predicted parameter of the chiller plant and an actual parameter of the chiller plant;

computing a differential parameter resulting from chiller plant optimization, by subtracting the differential parameters of the plurality of equipment from the differential parameter of the chiller plant; ascertaining a presence of abnormality in the differential parameter resulting from chiller plant optimization; and if the presence of abnormality in the differential parameter resulting from chiller plant optimization is ascertained, generating a first notification which identifies a request for human intervention.

The computer-implemented method may further comprise: ascertaining a presence of abnormality in any one of the plurality of equipment based on the differential parameters of the plurality of equipment; and if the presence of abnormality in any one of the differential parameters of the plurality of equipment is ascertained, performing at least one of the following steps: generating a second notification which identifies the presence of abnormality in the any one of the plurality of equipment, and training one of the prediction models, which corresponds to the any one of the plurality of equipment, using second baseline data. In the above computer-implemented method, the parameters may be power, or a combination of power, flow and temperature, depending on the prediction parameters of the prediction models being used.

According to another aspect of the invention, a system is provided and comprises at least one computing unit; and at least one memory storage for storing computer-executable instructions that, when executed by the at least one computing unit, cause performance of operations comprising any one of the computer-implemented methods described in the above paragraphs.

Brief Description of Drawings

Embodiments of the invention are disclosed hereinafter with reference to the drawings, in which:

Figure 1 shows a system architecture of the LEO system;

Figure 2A shows a machine learning life cycle that addresses the challenges of scalability, adaptability and learning;

Figure 2B shows a basic concept of generalization of model for chiller plants, and four phases required to build a model that achieves universal fitting of chiller plant data;

Figure 3A shows a prediction model for chiller plant;

Figure 3B shows prediction models for chiller plant equipment e.g. cooling tower, condenser water pump, chiller model, chilled water pump, using an equipment performance decomposition-based approach with increasing levels of abstraction;

Figure 3C shows one embodiment of prediction models for chiller plant equipment, e.g. cooling tower, condenser water pump, chiller model, chilled water pump;

Figure 3D shows another embodiment of prediction models for chiller plant equipment, e.g. cooling tower, condenser water pump, chiller model, chilled water pump; Figure 3E shows one example of a prediction model for cooling tower;

Figure 3F shows one example of a prediction model for condenser water pump;

Figure 3G shows one example of a prediction model for chilled water pump;

Figure 3H shows another embodiment of prediction models for chiller plant equipment, based on Figures 3C, 3E to 3G;

Figure 4 shows a prediction process using the equipment performance decomposition-based approach;

Figure 5 shows that the LEO system provides visualization data with targeted areas for human intervention and automatic optimization as compared to existing HVAC Big Data Tools;

Figure 6 shows that LEO system performs machine and statistics machine learning to deliver two types of results;

Figure 7 shows an implementation of block 415 to compare predicted and actual value of condenser pump power to detect abnormality; Figure 8 shows introduction of high variances to training data for cooling tower by the invention;

Figure 9 shows a comparison of predicted total chiller plant power, actual total chiller plant power and a deviation therebetween;

Figure 10 shows evaluation of chiller performance in which baseline data is provided during baseline time period 1 to 17 April while test data is provided during test period 18 March to 23 May;

Figure 1 1 shows a representation of optimization options, e.g. CWP flow and CT approach, and the corresponding power requirement; and

Figure 12 shows a schematic representation of a data driven, or a hybrid rule-based and data driven EMS/BMS.

Detailed Description of Invention

In the following description, numerous specific details are set forth in order to provide a thorough understanding of various illustrative embodiments of the invention. It will be understood, however, to one skilled in the art, that embodiments of the invention may be practiced without some or all of these specific details. In other instances, well known process operations have not been described in detail in order not to unnecessarily obscure pertinent aspects of embodiments being described. In the drawings, like reference numerals refer to same or similar functionalities or features throughout the several views.

Embodiments described in the context of one of the methods or devices or systems are analogously valid for the other methods or devices or systems. Similarly, embodiments described in the context of a method are analogously valid for a system or device, and vice versa.

Features that are described in the context of an embodiment may correspondingly be applicable to the same or similar features in the other embodiments. Features that are described in the context of an embodiment may correspondingly be applicable to the other embodiments, even if not explicitly described in these other embodiments. Furthermore, additions and/or combinations and/or alternatives as described for a feature in the context of an embodiment may correspondingly be applicable to the same or similar feature in the other embodiments.

As used herein, the articles "a", "an" and "the" as used with regard to a feature or element include a reference to one or more of the features or elements.

As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.

As used herein, the term "each other" denotes a reciprocal relation between two or more objects, depending on the number of objects involved.

As used herein, the term "coupled" and related terms are used in an operational sense and are not necessarily limited to a direct physical connection or coupling. Thus, for example, two devices may be coupled directly, or via one or more intermediary devices. As another example, devices may be coupled in such a way that data or information may be passed therebetween without sharing physical connection with each other. Based on the present disclosure, a person of ordinary skill in the art will appreciate a variety of ways in which coupling exists in accordance with the aforementioned definition.

As used herein, the terms "first," "second," and "third," etc. are used merely as labels, and are not intended to impose numerical requirements on their objects.

As used herein, the term "power" includes references to "power consumption" and may be interchangeably used; the terms "model" and "sub-model" include references to "prediction models" and "machine learning models", and may be interchangeably used; the terms "cooling load" includes references to "cooling tonnage" and may be interchangeably used.

As used herein, the term "deviation", depending on context, refers to absolute difference between values, or a difference between one of a set of values and some fixed value, usually the mean of the set, and therefore may be interchangeably used with the term "difference".

Figure 1 shows the system architecture of LEO system that receives real time M&V (measurement and verification) data from chiller plant, or historical data from different sources to trigger continuous machine learning life cycles. A machine learning life cycle includes preprocessing, optimization and post-processing.

Figure 2A shows the novel machine learning life cycle that addresses the challenges of scalability, adaptability and learning. The machine learning life cycle is divided into two major phases, namely i) model training phase and ii) large scale machine learning-based prediction and optimization phase.

Model training phase (in blocks 201 a and 201 b of Figure 2A)

The machine learning phase involves the development of a "universal model" that generalizes by fitting accurately to the measurement and verification data of a smaller sample of chiller plants. In particularly, the model comprises of sub-models that represent performance, power and efficiency of equipment such as chillers, chilled water and condenser water pumps, cooling towers. The equipment list can be extended to airside equipment such as air handling units and fan coil units etc. The main objective in the training phase is to achieve the best fit for a relative small sample of chiller plants with the M&V data t-Phase, while satisfying the best fit for large scale of chiller plants' M&V data that the model is not trained to fit in the training phase. In summary, the "universal model", is trained using the X_t-phase data, but it is expected to predict performance, power, performance and efficiency of future M&V data X_mi-Phase without reprogramming. Deep neural network and multi-level regression model would likely be the best way to build the universal model. In Ref [3], Wei describes a data driven method to model a chiller plant. Wei applies BFGS (broydene- fletcheregoldfarbeshanno) method to a one-output-unit MLP (multi-layer perceptron) to train a network to represent the prediction model. Monfet and Lee also applied neural network to represent prediction models that predict single output. These models are black box approaches that predict an output, e.g. chiller plant power or efficiency with multiple inputs. The invention applies a novel deep learning approach that decomposes a chiller plant model to multiple sub-models e.g. equipment models (see Figure 3B), to form a final prediction model.

Other preprocessing tasks will also be performed in the model training phase [4],[5],[6].

Large scale machine learning phase (in blocks 202 to 208 of Figure 2A).

The LEO system with "universal model" is ready for large scale machine learning in this phase. All the tasks performed in the large scale machine learning phase are automated, and LEO system will only prompt for user intervention if it detects abnormal sensor data that is beyond uncertainty levels. The large scale learning phase is divided into 5 tasks: a) Automatic model training and fine tuning for a specific chiller plant (in block 202). The LEO receives sensor data from different M&V data sources, X_m|. Phase, where ml-phase refers to machine learning phase, and automatically trains features and its weights to accurately fit the data, X_mi-Phase- LEO system shall initiate a set of performance and accuracy diagnostics for sensor data accuracy and uncertainty. Figure 2B shows the novel framework that LEO system applies for the development of the prediction model for the chiller plant. It further applies the novel equipment decomposition-based model development approach to improve the speed and accuracy of model training. b) Optimization (in block 203). It shall trigger an optimization program to determine the optimum values of certain set points, SP_mi-_Phase that minimize total chiller plant power consumption. Block 203 may be performed using existing methods.

c) Efficiency evaluation and sensitivity analysis (in block 204). LEO system will evaluate the actual efficiency of the chiller plant and uncertainties of the sensor data to determine the fitness of the universal model. It decides to trigger a bottom up, localized model retraining that will evaluate performance and efficiency of individual equipment. Equations 5 and 6 are applied as a novel differential equipment performance approach to evaluate the equipment, and chiller plant efficiency.

d) Story Development from the raw data (in block 205). LEO system will apply the differential equipment performance approach to develop a story with time line and descriptions on the changes of equipment and chiller plant performance and energy efficiency. LEO recursively splits the raw data into different time periods based on the differential equipment performance approach.

e) Retrain the model (in block 206). The invention applies the concept of differential equipment performance to evaluate i) the effectiveness of energy efficiency optimization algorithm, ii) the deviation of equipment performance and efficiency and decide how to retrain the universal model to improve fitness to the M&V data, and manage uncertainty.

f) Localized machine learning in 207. The task is started once the model's fitness to the M&V data is in question. The localized machine learning is a localized, sub-model training approach that trains for fitness of the data to individual equipment,

g) Stop training 208. The task is started once the uncertainty of the data accuracy exceeds certain level, and the sensor data is not fit for input to the model for optimization. A user is informed to fix the sensor data before machine learning can resume optimization.

In summary, the major novelties are equipment decomposition-based model development approach in block 202, differential equipment performance approach in block 204, and methods to evaluate the effectiveness of optimization algorithm and deviation of equipment performance in block 205.

Method of Generalization for Chiller Plant Figure 2B shows the basic concept of generalization of model for chiller plants. The

"universal model" is a multi-model based on a hybrid of deep neural network supported by continuous machine learning life cycles. The novelty is in model development process as well as the continuous machine learning life cycles. Figure 2B shows that 4 phases are required to build a model that achieves universal fitting of chiller plant data.

Phase 1 - Actual Model Development

Chiller plant M&V data can be modeled as inputs x .. x_n to be associated with one or more outputs y (see Figure 3A). The model would learn a set of weights w^ . -w™ and compute their outputs f(x,w). There are multiple requirements to be met to achieve a universal model. The first requirement is an optimization problem to minimize total chiller plant power vs total chiller plant cooling tonnage, i.e.

Min {

R_total }. The total power consumption of various chiller plant equipment is modelled by P total

P + P + P + P + P . Figures 3A and 3B show a novel equipment performance chwp chiller cwp ct air

decomposition-based approach to represent a prediction model that will meet the second requirement. The third requirement is a Phase 3 requirement that would be met by modeling special features that detect changes in the life cycle of a chiller plant. Figure 3A and 3B show a model and sub-models for learning to represent chiller plants with a similar architecture with different level of abstractions. The total equipment power of a chiller plant is P = P + P + P + P + P . Formally, the sub-models total chwp chiller cwp ct air

are trained to represent the f(x) : R^D→ R^L, where D is the size of input vector X and L is the size of the output vector f(x). In particularly, the f(x)_Chw_P for block 309 in Figure 3B represents the power and efficiency for chilled water pumps with data sets of flow rates and other features as inputs for training. The f(x)_cwp block 307 in figure 3B also represents the power and efficiency for condenser pumps using features such as flow rates as inputs.

The f(x)_ct and f(x)_Chiiier that represent the power and efficiency of cooling towers and chillers are significantly more difficult to be modelled. The motivation of the algorithm is to learn to model the power and efficiency of the cooling towers and chillers to trade off better ratios of total power over total cooling tonnage.

For chillers, chiller power consumption f(x)_Chiiier for block 308 in figure 3B can be modelled by independent parameters stated in the chiller manufacturing datasheet such as chilled water supply set point, condenser supply temperature, usgpm/rt (flow rate) for chilled water and condenser water, and the cooling tonnage supplied by the chiller. These parameters are used to train with the chiller's power consumption to obtain the f(x)_Chiiier- For cooling tower, the problem is constructed such that given some noisy observations of a dependent variable at certain values of the independent variables {wet bulb, dry bulb, rh, cooling load and may other variables}, what is the best estimate of the dependent variable at a new value, f(x)_ct for block 306 in figure 3B.

The chiller plant power consumption f(x)chiiierpiam is modelled by independent variables such as weather (such as wet bulb, relative humidity (RH) and dry bulb), cooling load, and as in Figure 3A.

Phase 2 - Equipment Decomposition-based Model Development

The novel LEO modelling is based on a divide and conquer-approach that decomposes a chiller plant power prediction problem to sub-models for better efficiency and useful statistical inference. The following is the original mathematical problem.

P + P + P + P + P (equation 1 ) chwp chiller cwp ct air

E = E + E + E + E + E (equation 2) total chwp chiller cwp ct air

_ Pchwp Pchiller Pcwp Pet Pair

Rtotal Rtotal Rtotal R total R total

Find the Min {P / R }, and the Min {P } (equation 3)

total total total P _lolai refers to total equipment power, P_chwp refers to chilled water pump power, P_chin_er refers to chiller power, P_cwp refers to condenser water pump power, P_c, refers to cooling tower power, P_air refers to air side equipment power.

R total refers to total cooling load.

E total refers to equipment efficiency, E_chwp refers to chilled water pump efficiency, E_chiiier refers to chiller efficiency, E_cwp refers to condenser water pump efficiency, E_c, refers to cooling tower efficiency, E_air refers to air-side equipment efficiency. The novelty of the decomposition approach is the architecture levels of abstractions with multiple layers for chiller plants. The first motivation for the modelling with architecture level of abstractions is to learn to represent the chiller plants equipment powers, and ultimately infer equipment and chiller efficiency from the representation. The second motivation is to construct the architecture levels of abstractions that will generalize to represent power for any chiller plant without reprogramming. Figure 3A describes the basic representation system of a chiller plant from data. Figure 3B shows a specific representation learning method for a chiller plant's sensor data that is based on increasing levels of abstraction. It starts with low level abstractions such as flow rates, temperatures and powers, and progresses to project features to power and performance of specific equipment types. The projected features to specific equipment types are used to train to predict powers of the equipment types.

Figure 3B describes geometrical connections between representation learning that receives measurement and verification (M&V) data of a chiller plant and transforms the M&V data into equipment related subset features. Many existing feature engineering methods can be applied to transform full features to subset features but they do not describe the specific geometrical connections for chiller plants. The advantages of the representation learning method described in figure 3B are the faster speed of training subset features (blocks 302- 305) to equipment specific output values (blocks 306-309), and the availability of the middle levels of abstraction for further equipment specific performance evaluation. The training speed is particularly important for adaptive control systems, e.g. for HVAC or manufacturing, that make real time control and decision makings. Features engineering, representation learning from data etc. may be based on existing methods, but the geometrical connections for the representation learning are novel.

A set of prediction models are provided for a chiller plant and a plurality of equipment comprising cooling tower, condenser water pump, chiller and chilled water pump. These models are configured to train or machine learn from baseline data and thereafter predict parameters, e.g. power, flow and/or temperature, for their respective chiller plant or equipment during test period.

Figure 3A shows a prediction model for chiller plant, e.g. chiller plant model 320, which is configured to predict chiller plant power based on weather and cooling load (RT). Weather and cooling load are independent variables.

Figure 3C illustrates a prediction model for various chiller plant equipment (hereinafter may be referred to as "equipment").

In Figure 3C, a first prediction model 306 is configured to predict a condenser water temperature into chiller (cwshdr); a second prediction model 307 configured to predict a condenser water flow in/out chiller (cwfhdr); a third prediction model 309 is configured to predict a chilled water flow in/out chiller (chfhdr); and a fourth prediction model 308 is configured to predict a chiller power (chkw) based on the condenser water temperature into chiller (cwshdr), the condenser water flow in/out chiller (cwfhdr), the chilled water flow in/out chiller (chfhdr), a cooling load and a chiller set-point (chsp).

Reference is made to Figure 3D which shows one embodiment of the prediction model of Figure 3C. In Figure 3D, the first prediction model 306 includes a first and a second sub-model.

The first sub-model 306a, e.g. cooling tower model (CT), which is for cooling tower equipment is configured to predict cooling tower power ("ctkw"), e.g. in kilowatts, based on VSD (variable speed drive) speed of cooling tower fan ("ct_speed"). The second sub-model 306b, e.g. condenser water temperature model (CWTM), which is for condenser water pump equipment, is configured to predict condenser water temperature into chillers ("cwshdr") based on weather and VSD (variable speed drive) speed of cooling tower fan ("ct_speed").

In Figure 3D, the second prediction model 307 includes a third and a fourth submodel. The third sub-model 307a, e.g. condenser water flow model (CWFM), which is for condenser water pump equipment, is configured to predict condenser water flow in/out of chillers ("cwfhdr") based on VSD speed of condenser water pump ("cwp speed"). The fourth sub-model 307b, e.g. condenser water pump model (CWP), which is for condenser water pump equipment, is configured to predict condenser water pump power ("cwpkw") based on VSD speed of condenser water pump ("cwp speed").

In Figure 3D, the third prediction model 309 includes a fifth and a sixth sub-model. The fifth sub-model 309a, e.g. chilled water pump model (CHWP), which is for chilled water pump equipment, is configured to predict chilled water pump power ("chwpkw") based on VSD speed of chilled water pump ("chwp speed"). The sixth sub-model 309b, e.g. chilled water flow sub- model (CHFM), which is for chilled water pump equipment, is configured to predict chilled water flow in/out of chillers ("chfhdr") based on VSD speed of chilled water pump ("chwp speed").

In Figure 3D, the fourth prediction model 308, e.g. chiller model 308, which is for chiller equipment, is configured to predict chiller power ("chkw") based on condenser water flow in/out of chillers ("cwfhdr"), condenser water temperature into chillers ("cwshdr"), chilled water flow in/out of chillers ("chfhdr"), cooling load and chilled water set point ("chsp"). Cooling load and chilled water set point ("chsp") are independent variables.

The set of prediction models may further comprise a fifth prediction model 310, e.g chiller plant equipment model (see Figure 3B), which is configured to predict total equipment power based on cooling tower power ("ctkw"), condenser water pump power ("cwpkw"), chiller power ("chkw") and chilled water pump power ("chwpkw") from prediction models 306, 307, 308, 309. Reference is made to Figure 3E which shows another example of the first prediction model of Figure 3C. The first prediction model 306 of Figure 3E is configured to predict condenser water temperature into chiller (cwshdr) based on weather data, cooling load, cooling tower power (ctkw). Reference is made to Figure 3F which shows another example of the second prediction model of Figure 3C. The second prediction model 307 of Figure 3E is configured to predict condenser water flow in/out chiller (cwfhdr) based on condenser water pump power (cwpkw).

Reference is made to Figure 3G which shows another example of the third prediction model of Figure 3C.The third prediction model 309 of Figure 3E is configured to predict chilled water flow in/out chiller (chfhdr) based on chilled water pump power (chwpkw).

In some embodiments, any or all of the first, second, third prediction models of Figure 3D may be replaced by the respective model of Figures 3E, 3F and 3G, and the predicted parameters would be modified according to the replacement models as described in relation to Figures 3E, 3F and 3G.

In some other embodiments, that all of the first, second, third prediction models of Figures 3E, 3F and 3G may be combined with the fourth prediction model 308 to arrive at Figure 3H which shows another embodiment of the prediction model of Figure 3C. The predicted parameters would be modified according to the replacement models as described in relation to Figures 3E, 3F and 3G.

Phase 3 - Time based model training using baseline, test and cross-validation data

Performance evaluation process for a chiller plant to meet phase 3's requirement in Figure 2B is explained below. Performance evaluation is based on the concept of comparing the actual present power (Pow_present) versus the actual historical power (Pow_historicai) at similar conditions. The conditions are the reverse projection of the actual power consumption to the prediction features in blocks 306-309 in figure 3B. This can be achieved using a couple of methods, e.g. deep learning models that estimate the relationship between dependent variables (blocks 306-309), e.g. powers, and independent variables, or features (blocks 302-309), e.g. flow rates, temperatures and powers. The chiller plant level performance comparing a period of present time t(pres,t) = {t(pres,1 ) .... T(pres,n)} to a period of historical time t(his,t) = {t(his,1 )... t(his,m)}. Equipment performance decomposition-based approach in Figure 3B builds a prediction model that estimates the equipment and plant level dependent variables, e.g. power consumption, using some given independent variables. a) Equipment evaluation.

Differential power is computed by comparing∑Pow_pre_Sent and∑Pow_hi_Storicai using the following equation:

DeltaPoWdiff.totai

-∑Pow_historicai (equation 5)

A negative differential power can be used to infer a reduction in power consumption by equipment with similar independent variables in blocks 306-309 in figure 3B. A positive differential power can be used to infer an increase in power consumption by equipment. Referring to power consumption of total equipment, P = p + p + p + P + P , the DeltaPoWequip.diff, where _equi_P can be total chwp chiller cwp ct air

described by = {chiller plant, chillers, chilled water pumps, condenser water pumps, cooling towers, air-side equipment}.

b) Performance Evaluation.

The performance evaluation can be done for different levels of abstraction as stated by block 31 1 in figure 3B. By training prediction models in figure 3B, the DeltaPow_equip,diff, where _equi_P = { chillers, chilled water pumps, condenser water pumps, cooling towers, air-side equipment}. The change in power consumption for all equipment is computed.

c) Energy efficiency computation.

The energy efficiency of equipment and chiller plant can be computed by dividing the summation of power over summation of cooling tonnage. There are two major reasons contributing to the change of total chiller plant power consumption for a chiller plant: i) change of equipment efficiency due to aging, wear and tear, change of water distribution efficiency, etc., ii) improvement due to chiller plant optimization [7][8]. Accordingly, a differential power or change in power consumption contributed by chiller plant optimization, DeltaPow_opt,di„, may be computed by the following equation : DeltaPow_opt,diff = DeltaPow_chi||_erP|_ant,diff - (DeltaPow_chin_er,diff + DeltaPow_chwp,diff +

DeltaPow_{cwPi d}i_ff + DeltaPow_ct_,di_ff + Uncertainty) (equation 6)

In equation 6, uncertainty is contributed by the errors in the modelling, and deviations of the data. In certain embodiments, this uncertainty is not addressed and may be considered negligible.

Figure 4 shows a prediction process which uses equipment performance decomposition-based approach. While Figure 4 shows the process being applied to equipment, the process may be applied to chiller plant in a similar manner. In block 401 , an operator decides what the objective of the prediction process is.

There may be two different objectives in the prediction process - i) objective 1 - Continuous chiller plant representation learning and automatic optimization, ii) objective 2 - Story telling from the M&V data for diagnostics.

In block 403, for objective 1 , LEO divides raw or existing M&V data from sensors into different time periods, and classifies them as baseline data, cross-validation data and test data. For objective 2, LEO classifies the M&V data into a baseline, a cross validation and a number of (or at least one) test periods. Accordingly, data from baseline time period may be referred to as "baseline data"; data from cross-validation time period may be referred to as "cross-validation data"; data from test time period may be referred to as "test data". It is to be appreciated that the cross-validation time period may be a subset of the baseline time period. It is to be appreciated that the baseline time period and the test time period may or may not be mutually exclusive.

In block 405, prediction models for equipment 306a, 306b, 307a, 307b, 308, 309a, 309b are trained to predict equipment power using baseline data. Training may be performed by neural network model, Gaussian process, or other suitable methods, to produce trained prediction models, e.g. cooling tower model 306a to predict cooling tower power, condenser water pump model 306b, 307a, 307b to predict condenser water pump power, chilled water pump model 309a, 309b to predict chilled water pump power, chiller model 308 to predict chiller power (see blocks 407a, 407b, 407c, 407d). Similarly, prediction model 320 for chiller plant is trained using baseline data to produce a trained chiller plant model 320 to predict chiller plant power.

In blocks 409a, 409b and 409c, the trained prediction models, after training in block 405, are used to predict power for each equipment and for the selected time periods of block 403. As illustrated in block 409a, using baseline data, a predicted power is computed for each equipment. As illustrated in block 409b, using cross-validation data, a predicted power is computed for each equipment. As illustrated in block 409c, using test data, a predicted power is computed for each equipment. Similarly, predicted power is computed for chiller plant separately, using baseline, cross-validation and test data. Blocks 41 1 a, 41 1 b and 41 1 c analyze the uncertainties of the various data sets to evaluate the confidence levels of the sensor data for the prediction models.

The predicted power computed using baseline data (see block 409a) may be compared with the predicted power computed using cross-validation data (see block 409b) to compute a deviation. For example, for cooling tower, predicted power computed using baseline data is compared with predicted power computed using cross-validation data to compute a deviation therebetween. Similar comparison and/or computation of deviation is also performed for each of the remaining equipment i.e. chilled water pump, condenser water pump, chiller, as well as for chiller plant.

In block 413, model accuracy or fitness e.g. in terms of resolving bias and overfitting, is analysed for each equipment based on the above-computed deviation between predicted power using baseline data and predicted power using cross-validation data. For example, for cooling tower, if the computed deviation falls within a predetermined threshold or limit, accuracy of the cooling tower model is validated. However, if the computed deviation exceeds the predetermined threshold or limit, the cooling tower model is not accurate or is unfit and may require re-training of the cooling tower model. If re-training of the cooling tower model is required, the process proceeds or returns to block 403 where a different baseline data is to be selected and training of the cooling tower model takes place based on the selected different baseline data.

The same procedure of comparison against a respective predetermined threshold is separately applied to each of the remaining equipment, i.e. chilled water pump, condenser water pump, chiller, as well as for chiller plant to ascertain accuracy of their respective prediction models. If any of the remaining equipment is ascertained inaccurate or unfit, the process proceeds or returns to block 403 where a different baseline is to be selected and training of the respective model takes place using the selected different baseline data. Ref [9] describes the application of cross-validation for accuracy estimation and model selection.

In block 415, a presence or absence of abnormality in equipment is ascertained based on a change in performance of each equipment. Change in equipment performance is computed using differential approach of equations 5 and 6 and using predicted and actual power for test period. For example, for condenser water pump, a predicted power computed from test data is compared with actual power during test period to compute a differential power. Similar computation is performed to compute separate differential powers for the remaining equipment, e.g. chilled water pump, condenser water pump, chiller, as well as for chiller plant.

To ascertain presence of abnormality in equipment, the computed differential power of an equipment as computed in block 415 is compared against a respective predetermined threshold. If the computed differential power exceeds the predetermined threshold, a notification is generated and provided to an operator to notify a presence of abnormality in the equipment and/or prompt the operator to perform manual check on the abnormal equipment. Further, the process may proceed or return to block 403 to re-train the particular abnormal equipment model based on a different baseline data. The foregoing comparison and/or notification steps are similarly performed for each equipment.

For example, Figure 7 shows an implementation of block 415 to compare predicted and actual value of condenser pump power to detect abnormality in condenser pump. To assess the deviation of predictive values vs the actual values, formulas such as mean error square, or many other formulas to measure the errors can be applied. A high deviation can be inferred as an increase in condenser power consumption for similar flow rates. As shown in Figure 7, similar power is observed on 22^nd March and 28^th March, however, flow rate on 22^nd March is higher while flow rate on 28^th March is lower. An increased deviation of around 24%, from baseline value, on 28^th March infers a presence of abnormality in the condenser water pump. By using scatter diagram, or scatter plot, the plot uses condenser flow rates to determine power consumption of the condenser pumps.

Figure 10 shows another implementation of block 415 to compare predicted and actual value of chiller power to detect abnormality in chiller. In Figure 10, baseline time period is from 1 to 17 April and baseline data is represented by line 1001 ; test period is from 18 March to 23 May and line 1002 represents a percentage deviation between predicted and actual power during the test period. The percentage deviation may be computed by dividing an absolute difference between actual power and predicted power by actual power, and expressing the quotient in percentage. Change in equipment efficiency may also be evaluated as efficiency may be derived from change in equipment performance, e.g. computed as a quotient of power or differential power over cooling tonnage.

To achieve objective 2, the LEO system may perform recursive searches to identify the change in equipment performance (block 415) to infer the root causes for the change of chiller plant efficiency. The recursive searches may be performed by splitting the raw data into multiple time periods, repeating the computations of block 415, and comparing equipment performance and/or efficiency over these multiple time periods.

Block 417 evaluates the effectiveness of chiller plant optimization. To evaluate the effectiveness, e.g. improvement or decline, of chiller plant optimization, a differential power or change in power consumption contributed by chiller plant optimization is computed. To this end, the differential powers of total equipment and of chiller plant are computed.

To compute differential powers of total equipment, the differential powers computed in block 415 for each equipment are added up or summed to compute or obtain a summation which is referred to as "total equipment differential powers".

To compute differential power of the chiller plant, a predicted power computed from test data, using the chiller plant model 320, is compared with actual power of chiller plant during test period to compute a difference therebetween.

Applying Equation 6 and assuming the uncertainties component in Equation 6 as well as air-side equipment are not addressed, a differential power resulting from chiller plant optimization is computed by subtracting the total equipment differential powers from the differential power of the chiller plant, and may be used to evaluate the effectiveness of chiller plant optimization.

From block 417, the total change of equipment performance and/or efficiency may be used to isolate the results of optimum control strategies, and the change of sensor accuracies.

For example, if the computed differential power resulting from chiller plant optimization implies an improvement in chiller plant operation, e.g. the computed differential power resulting from chiller plant optimization is a negative value or becomes an increasingly negative value over a period of time, this shows that power consumption has decreased, chiller plant optimization has been effective and therefore human intervention may not be required. However, if the computed differential power resulting from chiller plant optimization implies a decline in chiller plant operation, e.g. breaches a predetermined threshold, a presence of abnormality is ascertained and a notification is generated and provided to an operator to request human intervention, e.g. manual check on physical equipment and/or optimization strategies. This predetermined threshold may be defined as: the computed differential power resulting from chiller plant optimization is a positive value greater than a predetermined value, or becomes an increasingly positive value over a period of time, etc. If abnormality in the computed differential power resulting from chiller plant optimization is ascertained present, the process may proceed to block 405 to re-train the equipment models.

When the computed differential power resulting from chiller plant optimization (block 417) is considered together with the differential powers of each or total equipment (block 415), an improvement in their differential powers implies that chiller plant optimization has been effective and therefore human intervention may not be required; conversely, a decline in their differential powers implies presence of abnormality in chiller plant optimization and/or equipment performance and therefore a notification may be generated and provided to an operator to request human intervention. If the differential powers show a conflicting trend, e.g. an improvement in computed differential power resulting from chiller plant optimization and a decline in differential power of any equipment, or vice versa, this may imply a presence of abnormality in the particular equipment and/or chiller plant optimization, and therefore a notification may be generated to identify the particular abnormality and provided to an operator to request human intervention. In view of the foregoing description, the results from block 415 and 417 assist the operator in identifying presence of abnormality in chiller plant optimization and/or equipment and root cause(s) of the abnormality.

For example, a representation of optimization strategies or options, e.g. optimize CWP flow and CT approach, and the corresponding power requirement, may be provided as shown in Figure 1 1 . For CT approach, operator may determine, from the representation and based on real-time weather and cooling tonnage, the CT temperature from 1 to 5 degrees Celsius which will be most efficient, i.e. minimize power. For CWP approach, operator may determine, from the representation and based on real-time weather and cooling tonnage, the CWP flow which will be most efficient, i.e. minimize power. Accordingly, the LEO system is able to learn from the data and perform autonomous control to achieve the most optimized set points.

Equipment performance decomposition-based approach described in Figure 4 can be further reconfigured into many different processes for, but not limited to, performance comparison. One or more time periods of data may be set as baselines for training prediction models as shown in Figures 3B, 3C 3D or 3H, and one or more periods of the data is subset for comparison. Equipment and chiller plant performance comparison can be achieved by applying differential equations 5 and 6, and summation for one or more baselines and other periods of the data.

Figure 9 shows an evaluation of chiller plant performance based on a comparison of predicted total chiller plant power 901 and actual total chiller plant power 902 to evaluate the value of improvement. Figure 9 also shows a percentage deviation 903 of actual from predicted total chiller plant power. This deviation 903 refers to the differential power of the chiller plant as mentioned in blocks 415 and 417.

The foregoing paragraphs describing the process of Figure 4 are based on the prediction models of Figure 3D, where the power parameters are predicted and compared. It is to be appreciated that if the process illustrated in Figure 4 is based on the prediction models of Figure 3H, or based on the prediction models of Figure 3D being replaced, in part, by any of the prediction models of Figure 3E, 3F and 3F, the foregoing computation, comparison and/or analysis steps would be suitably modified. Particularly, as the first prediction model 306 of Figure 3E predicts a temperature parameter, the second prediction model 307 of Figure 3F predicts a flow parameter, the third prediction model 309 of Figure 3G predicts a flow parameter while the fourth prediction model 308 for chiller predicts a power parameter, the steps described in relation to blocks 41 1 , 413, 415 and/or 417 will be performed based on differential flow parameters, differential temperature and/or differential power. Alternatively, flow and/or temperature parameters may be converted to power parameter as known to person skilled in the art during performance of the steps of blocks 41 1 , 413, 415 and/or 417.

Data Enrichment of Equipment

The lack of generality in the data is an important problem of existing systems, which does not attract attention in almost all existing studies on chiller plants. Simple data modelling over the existing chiller plant data may result in useless model with high generalization error. In an extreme case, a chiller plant always runs at a fixed configuration, e.g., fixed VSD speed for pumps and fans. By training data from this chiller plant, the result ing data model is only applicable to the current configuration, and does not generate meaningful prediction for any other configuration. Figure 8 plots the data distribution over cooling tower speed (CT Speed) and cooling tower power, collected in fully controlled chiller plant with a fixed VSD configuration setting (denoted as original data 801 ) and random VSD configuration (denoted as rich data 802) respectively. The cooling tower fan is mainly operated at the speed between 20% to 40% of the maximum speed. The results show that data model using fixed VSD configuration does not have much generalization capability when other configurations are used by the chiller plant whereas the invention enables training based on a wider data variation.

Comparing LEO to the existing HVAC big data tools

Existing tools such as Lucid and Green Koncepts apply big data technologies to generate nice visualization charts and data. However, better classification of energy data is not sufficient and it still requires intensive and real time analysis to translate data into actions. Energy management systems must advance from providing charts and data to automatic optimization and targeted diagnostics information. The users of energy management systems are technicians and facility managers who do not have time and expertise to develop advanced machine learning techniques to translate data and charts to actions that improve energy efficiency.

Figure 5 shows that the LEO system provides visualization data with targeted areas for human intervention and automatic optimization as compared to existing HVAC Big Data Tools.

Figure 6 shows that LEO system performs machine and statistics machine learning to deliver 2 types of results: i) Automatic optimization that results in improved energy efficiency, ii) Provide some very specific actionable information for users to take specific actions. For example, LEO system suggests that the energy efficiency of the chiller 3 is gradually going down by x.xx% since it was serviced 3 months ago. LEO system predicts that restoring the energy efficiency of the chiller to its original conditions would result in a saving of $xxx per months.

Hybrid Rule-based and Data Driven Energy/Building Management System

Figure 12 shows an implementation, but not limited to, an energy or management system (EMS/BMS) for buildings and chiller plants. In particularly, the invention transforms the existing EMS/BMS from rule-based to a data driven, or a hybrid rule-based and data driven EMS/BMS. The system architecture includes a real time control system 1201 for in- premise control, and a non real time cloud-based system 1202 for data management, machine learning and remote diagnostics. The cloud-based system 1202 or server includes at least one cloud-based computing unit, a memory storage for storing data, e.g. baseline data, cross-validation data and test data, and computer-executable instructions, communication module for receiving and/or transmitting data to and/or from the in premise real time control system 1201 , input/output device, e.g. display unit, and other appropriate components. The cloud-based computing unit is communicably coupled to the memory storage, communication module, and input/output device, and configured to implement machine learning and/or diagnostics.

The in-premise control system 1201 includes sensors and/or actuators 1203 which are communicably coupled to chiller plant equipment for sensing data parameters, e.g. measure speed, flow, temperature and/or power, of equipment and/or for controlling equipment.

The in-premise control system 1201 further includes energy/building management system 1204 using direct digital controllers (DDCs) or programmable logic controllers (PLCs) with rules for chiller plant, air handling units (AHUs), fan coil units (FCUs), etc. The energy/building management system 1204 may include at least one computing unit which is communicably coupled to the sensors and actuator 1203, directly or indirectly by communication module.

The in-premise control 1201 further includes the LEO system 1204 (see also Figure 1 ) which is configured to perform real-time learning, control, optimization and diagnostics. The LEO system 1204 includes at least one memory storage, and at least one computing unit communicably coupled thereto. The memory storage is configured to store data (e.g. baseline data, cross-validation data, test data), prediction models, and computer-executable instructions that, when executed by the at least one computing unit, cause performance of operations as described in the present disclosure and in relation to blocks 401 to 417. The computing unit of the LEO system may be communicably coupled to the sensors and/or actuators 1203, directly or indirectly, to receive measured data transmitted by the sensors and/or actuators 1203.

The LEO system 1204 is communicably coupled to the cloud server 1202 for data transmission therebetween, and to at least an input/output device, e.g. display unit and data entry unit, to provide a user interface. The user interface or display unit is configured to display notifications and/or diagnostics from the LEO system 1205, and allow viewing of reports generated by the LEO system 1205. The user interface 1206 or input unit is further configured to allow an operator, e.g. building and/or database manager, provide instructions to control equipment and/or chiller plant. Other embodiments will be apparent to those skilled in the art from consideration of the specification and practice of the invention. Furthermore, certain terminology has been used for the purposes of descriptive clarity, and not to limit the disclosed embodiments of the invention. The embodiments and features described above should be considered exemplary.

List of References

[1 ] Nguyen, A. T.; Reiter, S.; Rigo, P. A review on simulation-based optimization methods applied to building performance analysis. Applied Energy 1 13 (2014) 1043-1058.

[2] Applications of artificial neural networks for refrigeration, air-conditioning and heat pump systems— A review, RENEWABLE AND SUSTAINABLE ENERGY REVIEWS ^■ FEBRUARY 2012.

[3] Xiupeng Wei, Guanglin Xu, Andrew Kusiak, "Modeling and optimization of a chiller plant", Energy, 2014, Elsevier.

[4] Vakiloroaya, V., Q. P. Ha, and B. Samali. "Energy-efficient HVAC systems: Simulation- empirical modelling and gradient optimization." Automation in Construction 31 (2013): 176- 185.

[5] D. Monfet, R. Zmeureanu, Ongoing commissioning of water-cooled electric chillers using benchmarking models, Applied Energy 92 (2012) 99-108.

[6] Lee, T.-S.; Liao, K.-Y.; Lu, W.-C. Evaluation of the suitability of empirically-based models for predicting energy performance of centrifugal water chillers with variable chilled water flow. Appl. Energy 2012, 93, 583-595.

[7] Hartman, Thomas. "All-variable speed centrifugal chiller plants." ASHRAE journal 43.9 (2001 ): 43.

[8] Yu, F. W., and K. T. Chan. "Optimization of water-cooled chiller system with load-based speed control." Applied Energy 85.10 (2008): 931 -950.

[9] Kohavi, Ron. "A study of cross-validation and bootstrap for accuracy estimation and model selection." Ijcai. Vol. 14. No. 2. 1995.

Claims

1 . A computer-implemented method comprising:

training a plurality of prediction models using first baseline data, the prediction models being for chiller plant and a plurality of equipment comprising cooling tower (CT), condenser water pump (CWP), chiller, chilled water pump (CHWP);

computing, using the prediction models, a plurality of predicted parameters of the plurality of equipment and the chiller plant using test data;

computing a plurality of differential parameters of the plurality of equipment based on the predicted parameters of the plurality of equipment and a plurality of actual parameters of the plurality of equipment;

computing a differential parameter of the chiller plant based on the predicted parameter of the chiller plant and an actual parameter of the chiller plant;

2. The method of claim 1 , further comprising: ascertaining a presence of abnormality in any one of the plurality of equipment based on the differential parameters of the plurality of equipment; and if the presence of abnormality in any one of the differential parameters of the plurality of equipment is ascertained, performing at least one of the following steps: generating a second notification which identifies the presence of abnormality in the any one of the plurality of equipment, and training one of the prediction models, which corresponds to the any one of the plurality of equipment, using second baseline data.

3. The method of any one of claims 1 to 2, further comprising:

computing a first plurality of deviations between the predicted parameters of the plurality of equipment using the baseline data and the predicted parameters of the plurality of equipment using cross-validated data;

computing a second deviation between the predicted parameter of the chiller plant using the baseline data and the predicted parameter of the chiller plant using cross- validation data; and

ascertaining accuracy of the prediction models based on the first deviations and the second deviation.

4. The method of claim 3, further comprising:

if any one of the prediction models is ascertained inaccurate, training the any one of the prediction models using baseline data different from the first baseline data.

5. The method of any one of claims 1 to 4, wherein the prediction models include:

a first prediction model configured to predict a condenser water temperature into chiller (cwshdr);

a second prediction model configured to predict a condenser water flow in/out chiller (cwfhdr);

a third prediction model configured to predict a chilled water flow in/out chiller (chfhdr); and

a fourth prediction model configured to predict chiller power (chkw) based on the condenser water temperature into chiller (cwshdr), the condenser water flow in/out chiller (cwfhdr), the chilled water flow in/out chiller (chfhdr), a cooling load and a chiller set-point (chsp).

6. The method of claim 5, wherein

the first prediction model includes a first sub-model configured to predict a cooling tower power (ctkw) based on a VSD (variable speed drive) cooling tower fan (ct_speed), and a second sub-model configured to predict the condenser water temperature into chiller (cwshdr) based on the VSD speed of cooling tower fan (ct_speed) and a weather data; the second prediction model includes a third sub-model configured to predict the condenser water flow in/out chiller (cwfhdr) based on a VSD speed of condenser water pump (cwp speed), and a fourth sub-model configured to predict condenser water pump power (cwpkw) based on the VSD speed of condenser water pump (cwp speed); and

the third prediction model includes a fifth sub-model configured to predict a chilled water pump power (chpkw) based on a VSD speed of chilled water pump speed (chwp speed); and a sixth sub-model configured to predict the chilled water flow in/out chiller (chfhdr) based on the VSD speed of chilled water pump speed (chwp speed).

7. The method of any of claims 1 to 6, wherein the parameters include power.

8. The method of claim 5, wherein

the first prediction model is configured to predict the condenser water temperature into chiller (cwshdr) based on a weather data, a cooling load, a cooling tower power (ctkw); the second prediction model is configured to predict the condenser water flow in/out chiller (cwfhdr) based on a condenser water pump power (cwpkw); and

the third prediction model is configured to predict a chilled water flow in/out chiller (chfhdr) based on a chilled water pump power (chwpkw).

9. The method of any one of claims 1 to 5 and 8, wherein the parameters include power, flow and temperature.

10. A system comprising:

at least one computing unit; and

at least one memory storage for storing computer-executable instructions that, when executed by the at least one computing unit, cause performance of operations comprising: training a plurality of prediction models using first baseline data, the prediction models being for chiller plant and a plurality of equipment comprising cooling tower (CT), condenser water pump (CWP), chiller, chilled water pump (CHWP);

1 1 . The system of claim 10, wherein the operations further comprising: ascertaining a presence of abnormality in any one of the plurality of equipment based on the differential parameters of the plurality of equipment; and if the presence of abnormality in any one of the differential parameters of the plurality of equipment is ascertained, performing at least one of the following steps: generating a second notification which identifies the presence of abnormality in the any one of the plurality of equipment, and training one of the prediction models, which corresponds to the any one of the plurality of equipment, using second baseline data.

12. The system of any one of claims 10 to 1 1 , wherein the operations further comprising: computing a first plurality of deviations between the predicted parameters of the plurality of equipment using the baseline data and the predicted parameters of the plurality of equipment using cross-validated data;

13. The system of claim 12, wherein the operations further comprising:

14. The system of any one of claims 10 to 13, wherein the prediction models include:

a second prediction model configured to predict a condenser water flow in/out chiller (cwfhdr); a third prediction model configured to predict a chilled water flow in/out chiller

(chfhdr); and

15. The system of claim 14, wherein

the first prediction model includes a first sub-model configured to predict a cooling tower power (ctkw) based on a VSD (variable speed drive) cooling tower fan (ct_speed), and a second sub-model configured to predict the condenser water temperature into chiller (cwshdr) based on the VSD speed of cooling tower fan (ct_speed) and a weather data;

the second prediction model includes a third sub-model configured to predict the condenser water flow in/out chiller (cwfhdr) based on a VSD speed of condenser water pump (cwp speed), and a fourth sub-model configured to predict condenser water pump power (cwpkw) based on the VSD speed of condenser water pump (cwp speed); and

the third prediction model includes a fifth sub-model configured to predict a chilled water pump power (chpkw) based on a VSD speed of chilled water pump speed

(chwp speed); and a sixth sub-model configured to predict the chilled water flow in/out chiller (chfhdr) based on the VSD speed of chilled water pump speed (chwp speed).

16. The system of any of claims 10 to 15, wherein the parameters include power.

17. The system of claim 14, wherein

18. The system of any one of claims 10 to 14 and 17, wherein the parameters include power, flow and temperature.

19. The system of any one of claims 10 to 18, further comprising:

a plurality of sensors located at the plurality of equipment and communicably coupled to the at least one computing unit, the sensors being configured to measure at least the actual power of the plurality of equipment and transmit data comprising the actual power of the plurality of equipment to the at least one computing unit.

20. The system of any one of claims 10 to 19, further comprising:

a display unit configured to display the first and/or the second notification.