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WO2025019030A1 - Prédiction d'occupation de bâtiment de bureau à partir de données de consommation de ressources de bâtiment pour une gestion d'énergie optimisée - Google Patents

Prédiction d'occupation de bâtiment de bureau à partir de données de consommation de ressources de bâtiment pour une gestion d'énergie optimisée Download PDF

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Publication number
WO2025019030A1
WO2025019030A1 PCT/US2023/084704 US2023084704W WO2025019030A1 WO 2025019030 A1 WO2025019030 A1 WO 2025019030A1 US 2023084704 W US2023084704 W US 2023084704W WO 2025019030 A1 WO2025019030 A1 WO 2025019030A1
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WO
WIPO (PCT)
Prior art keywords
building
meters
occupancy
meter
consumption
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/US2023/084704
Other languages
English (en)
Inventor
Olga LOSITSKY
Arindam Dasgupta
Rafael GRIFFO GOES
Georgia Olympia Brikis
Marco FECHTIG
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Siemens Schweiz AG
Siemens Industry Inc
Original Assignee
Siemens Schweiz AG
Siemens Industry Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Siemens Schweiz AG, Siemens Industry Inc filed Critical Siemens Schweiz AG
Publication of WO2025019030A1 publication Critical patent/WO2025019030A1/fr
Anticipated expiration legal-status Critical
Pending legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B15/00—Systems controlled by a computer
    • G05B15/02—Systems controlled by a computer electric
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/20—Pc systems
    • G05B2219/26—Pc applications
    • G05B2219/2642—Domotique, domestic, home control, automation, smart house

Definitions

  • This application relates to building technology. More particularly, this application relates to a controller for optimized energy management of a building and improved building operations efficiency.
  • System and method for building resource control based on building occupancy retrieves resource consumption data from various resource consumption meters (e.g., electricity meters, natural gas, fuel oil and water meters installed in the building). From among all resource consumption meters in the building, an algorithm determines correlation scores of the meters related to correlation with building occupancy based on analysis of weighted time series regressors, such as day of week, time of day and recorded temperature, and the best meters are selected by the correlation score. To predict building occupancy for a particular target day, a prediction engine analyzes historical data from the best meters to derive a normalized occupancy value relevant to the target day (e.g., second Tuesday of the month).
  • resource consumption meters e.g., electricity meters, natural gas, fuel oil and water meters installed in the building.
  • an algorithm determines correlation scores of the meters related to correlation with building occupancy based on analysis of weighted time series regressors, such as day of week, time of day and recorded temperature, and the best meters are selected by the correlation score.
  • a prediction engine analyzes historical data from the
  • the real occupancy value prediction is estimated for the target day by conversion of the normalized occupancy value by a standardized factor derived from minimum and maximum building occupancy data for the target time period.
  • An operation scheduler generates a schedule for an energy resource controller based on the occupancy value prediction, which is optimized for energy consumption.
  • FIG. 1 illustrates a framework example of a controller for building energy loads which optimizes a scheduler for energy management in accordance with embodiments of this disclosure.
  • FIG. 2 illustrates an example of mapping occupancy to building operation modes according to embodiments of this disclosure.
  • FIG. 3 illustrates an example of time series variables and consumption data for a meter to be correlated in accordance with embodiments of this disclosure.
  • FIG. 4 an example of a computing environment within which embodiments of the disclosure may be implemented.
  • FIG. 5 illustrates an example of a computer-implemented method for controlling building energy loads according to building occupancy in accordance with embodiments of this disclosure.
  • Methods and systems are disclosed to determine building occupancy for energy management in building operations, such as optimizing a schedule of controlled energy delivery to equipment for comfort of occupants, such as HVAC and lighting during periods of normal occupancy, while minimizing energy consumption during low occupancy periods.
  • patterns of building occupancy are predicted solely based on reading data of resource consumption meters data rather than reliance on various occupancy-related sensor readings throughout the building.
  • accurate predictions are possible with this approach, which is an improvement for determining actual building utilization over building operation scheduling according to standard workday profiles, eliminating need for large scale sensor installations.
  • the predicted patterns provide pro-active insights for automatically adjusting building operation modes. For example, the HVAC system output and other services can be reduced or shut down for areas, floors, zones predicted to be low or zero occupancy for specific days and situations.
  • the method according to the disclosed embodiments uses resource consumption data that is always available in building control systems to infer office building occupancy and predict when resources will be needed.
  • resource consumption data that is always available in building control systems to infer office building occupancy and predict when resources will be needed.
  • FIG. 1 illustrates a framework example of a controller for building energy loads which optimizes a scheduler for energy management in accordance with embodiments of this disclosure.
  • Framework 100 is used to control operations of building load controllers 160, such as cooling, heating and lighting, according to an operations scheduler module 141.
  • framework 100 includes a meter score engine 121, a prediction engine 131, an operation scheduler 141, and controller interface 151 to provide control instructions to building load controllers 160 for optimized operation modes for the building energy loads correlated to estimates of building occupancy.
  • Prediction engine 131 is trained to learn occupancy of a building based on patterns extracted from historical data, particularly the consumption data 101 from meters with the highest scores, identified by the meter score engine 121.
  • the combination of the meter score engine 121 and the Prediction engine 131 may be implemented as a deep learning or a neural network based model.
  • the problem to be solved is that actual historical occupancy data is not easily retrievable for a building to derive accurate predictions.
  • the proposed framework relies on consumption meter data for estimating building occupancy during different time period categories such as day of the week, weekend, holiday, and hours of the day. Once a strong (and repeatable) estimation of building occupancy is determined, then the building load controllers 160 can be programmed to a schedule having a profile similar to that shown in FIG. 2 according to the estimated occupancy.
  • meter score engine 121 For each consumption meter, meter score engine 121 combines correlations with temporal data into a single measure that reflects how likely the meter is to predict occupancy. Each resource consumption meter is graded by meter score engine 121 with a meter score derived from a weighted average of correlations between meter consumption data 101 and the time series variables 102. In an embodiment, the meter score engine 121 considers correlations between consumption data of a meter and three time series variables, corresponding to expected work days, expected work hours and historical daily temperature, which can be expressed by the following equation:
  • operation scheduler 141 Given predictions of building occupancy derived by prediction engine 131 for various time periods (e.g., day of week, workday, weekend, holiday, workhours, etc.), operation scheduler 141 is programmed to control operation modes for the building energy loads with improved correlation to actual building occupancy, rather than rough estimates based on standards of work schedules that have become increasingly fluid.
  • the operation mode schedule is optimized to control energy resource load for energy conservation balanced by comfort and needs of building occupants. For example, heating and cooling loads are scheduled to operate at full capacity for periods of predicted building occupancy and shut down for periods of vacancy. Moreover, operation mode for building energy loads for entire zones and floors can be closed shut off during periods of predicted low occupancy.
  • Controller interface 151 delivers the schedule information to each building load controller 160 (e.g., programmable relays, starters, switches, etc.) configured to directly control energy delivery to the various loads in the building.
  • An estimation of building occupancy for a time period is determined (531) using a weighted average of normalized consumption data from one or more best meters.
  • the estimation may be determined by deriving (533) a normalized consumption value for each of the best meters based on historic consumption data of the respective best meters, in which prediction for a target time period is based on consumption data (101) collected for a similar day and time in the past.
  • the estimation of building occupancy may be determined by deriving (535) a final occupancy prediction value by combining the normalized consumption values of the best meters as a weighted average, with the weighting according to respective meter scores for the best meters, and de-normalizing the weighted average by a scalar value derived from minimum and maximum number of people in the building for the target time period.
  • a schedule of operation mode of building energy loads for the target time period is generated (541) based on the estimation of building occupancy.
  • the schedule is optimized for energy conservation.
  • the building energy loads are controlled (551) according to the schedule.
  • any operation, element, component, data, or the like described herein as being based on another operation, element, component, data, or the like can be additionally based on one or more other operations, elements, components, data, or the like. Accordingly, the phrase “based on,” or variants thereof, should be interpreted as “based at least in part on.”

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  • Engineering & Computer Science (AREA)
  • General Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

Un dispositif de commande commande des charges d'énergie de bâtiment en fonction d'une occupation de bâtiment à l'aide d'un moteur de score de compteur (121), d'un moteur de prédiction (131) et d'un programmateur d'opération (141). Le moteur de score de compteur (121) détermine (521) un score de compteur pour chaque compteur d'une pluralité de compteurs de consommation. Le score est déterminé par un algorithme qui combine des corrélations entre les données de consommation de compteur (101) et des variables de série chronologique (102) associées à des périodes d'occupation attendues et à la température quotidienne. Un moteur de prédiction (131) détermine (531) une estimation d'occupation de bâtiment pendant une période cible à l'aide de données de consommation (101) provenant d'un ou plusieurs meilleurs compteurs. Le programmateur d'opérations génère (541) un programme de mode de fonctionnement de charges d'énergie de bâtiment pour la période cible en fonction de l'estimation d'occupation de bâtiment, optimisées pour la conservation d'énergie. Des dispositifs de commande (551) commandent des charges d'énergie de bâtiment en fonction du programme.
PCT/US2023/084704 2023-07-18 2023-12-19 Prédiction d'occupation de bâtiment de bureau à partir de données de consommation de ressources de bâtiment pour une gestion d'énergie optimisée Pending WO2025019030A1 (fr)

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
US202363514150P 2023-07-18 2023-07-18
US63/514,150 2023-07-18
US2023031536 2023-08-30
USPCT/US2023/031536 2023-08-30

Publications (1)

Publication Number Publication Date
WO2025019030A1 true WO2025019030A1 (fr) 2025-01-23

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Family Applications (1)

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PCT/US2023/084704 Pending WO2025019030A1 (fr) 2023-07-18 2023-12-19 Prédiction d'occupation de bâtiment de bureau à partir de données de consommation de ressources de bâtiment pour une gestion d'énergie optimisée

Country Status (1)

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WO (1) WO2025019030A1 (fr)

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2015013677A2 (fr) * 2013-07-26 2015-01-29 The Trustees Of Columbia University In The City Of New York Système d'optimisation de la totalité d'une propriété pour améliorer l'efficacité énergétique et obtenir des bâtiments intelligents
EP2911018A1 (fr) * 2014-02-24 2015-08-26 Siemens Schweiz AG Système d'automatisation de construction avec un modèl de prédiction
US20160100233A1 (en) * 2010-09-14 2016-04-07 Google Inc. Occupancy pattern detection, estimation and prediction
US20210199320A1 (en) * 2019-12-31 2021-07-01 Lennox Industries Inc. Error correction for predictive schedules for a thermostat

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160100233A1 (en) * 2010-09-14 2016-04-07 Google Inc. Occupancy pattern detection, estimation and prediction
WO2015013677A2 (fr) * 2013-07-26 2015-01-29 The Trustees Of Columbia University In The City Of New York Système d'optimisation de la totalité d'une propriété pour améliorer l'efficacité énergétique et obtenir des bâtiments intelligents
EP2911018A1 (fr) * 2014-02-24 2015-08-26 Siemens Schweiz AG Système d'automatisation de construction avec un modèl de prédiction
US20210199320A1 (en) * 2019-12-31 2021-07-01 Lennox Industries Inc. Error correction for predictive schedules for a thermostat

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