CN113468157B - Similar building selection method and device based on energy consumption analysis - Google Patents
Similar building selection method and device based on energy consumption analysis Download PDFInfo
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Abstract
The method comprises the steps of firstly carrying out primary screening on target buildings according to daily dimension historical energy consumption data of the target buildings by adopting a box line method, then carrying out further screening on the primarily screened target buildings by adopting different screening methods according to different item classification results and statistical indexes of the target buildings to obtain target building candidate sets, and finally screening out similar buildings from the target building candidate sets by adopting preset judgment rules on the target buildings in the target building candidate sets. According to the method, the target building is processed and screened from multiple dimensions and multiple layers, so that the obtained historical energy consumption data of the similar building can more accurately predict the future energy consumption condition of the building to be predicted, and the prediction efficiency is improved.
Description
Technical Field
The invention relates to the technical field of similar building screening, in particular to a similar building selecting method and device based on energy consumption analysis.
Background
When the existing building makes future energy consumption quota, the regression algorithm in machine learning is used for predicting the energy consumption according to the historical energy consumption condition of the building.
However, in a building which has just been put into use or has a short use time, since the historical energy consumption data of the building itself is not available or is small, the energy consumption prediction cannot be performed by the energy consumption data of the building itself, and therefore, it is necessary to use the historical energy consumption of the building similar to the structure and the operation mode thereof as a reference and use the historical energy consumption data thereof as training data.
The traditional similar building selection method is carried out according to personal experience, the selected building is not representative, the rated deviation predicted by referring to the historical energy consumption is larger, and the reliability is lower.
Disclosure of Invention
In order to solve the problem of prediction of energy consumption of a building with shorter service time in the prior art, the invention provides a similar building selection method and device based on energy consumption analysis. The specific technical scheme is as follows:
the similar building selecting method based on energy consumption analysis provided by the embodiment of the invention comprises the following steps:
acquiring a target building to be screened in a certain area, and cleaning daily dimension historical energy consumption data of the target building by using a box line graph method to remove abnormal values;
Classifying the cleaned historical energy consumption data according to different sub-items, and respectively calculating corresponding sub-item statistical indexes aiming at the different sub-items;
Screening the target building by adopting a preset screening method according to different sub-term statistical indexes to obtain a target building candidate set;
And screening similar buildings from the target building candidate set according to the judging attribute of the building to be predicted by adopting a preset judging rule, and predicting the future energy use condition of the building to be predicted by using the historical energy use data of the similar buildings.
Further, the step of classifying the cleaned historical energy consumption data according to different terms and calculating corresponding term statistics indexes for different terms respectively includes the steps of:
Classifying the cleaned historical energy consumption data into a rigid sub-term and an elastic sub-term;
The rigidity index calculated on the rigidity sub-term comprises: average fluctuation, historical energy consumption data amount and rigid single square meter energy consumption;
The elasticity index calculated for the elasticity sub-term includes: maximum mutual information coefficient of average temperature and historical energy consumption, historical energy consumption data amount and elastic single square meter energy consumption.
Further, the method for screening the target building by adopting a preset screening method according to different sub-term statistical indexes to obtain a target building candidate set comprises the following steps:
obtaining a subentry statistical index of the target building, and judging whether the target building subentry belongs to a rigid subentry or an elastic subentry;
judging whether the data volume of the target building meets the preset first quantity or not if the target building belongs to the rigid sub-term, judging whether the target building is abnormal or not by adopting a box line method if the data volume of the target building meets the preset first quantity, and removing the abnormal target building;
If the target building belongs to the elastic sub-term, judging whether the data volume of the target building meets a preset second volume or not; if the target building meets the preset second quantity, calculating a statistical index of the maximum mutual information coefficient of the average temperature and the historical energy consumption of the target building, judging whether the maximum mutual information coefficient is smaller than a preset threshold value, and if so, removing the target building to obtain a target building candidate set.
Further, the method also comprises the step of removing buildings with abnormal energy consumption and daily average energy consumption in a single square meter from the screened target buildings by using a box line method to obtain a target building candidate set.
Further, the screening similar buildings from the target building candidate set according to the preset judging rule and the judging attribute of the building to be predicted includes:
inquiring target buildings which are the same as the building area to be predicted from the target building candidate set, and judging whether the target buildings in the target building candidate set belong to similar buildings of the building to be predicted by adopting the preset judging rule;
Or querying the target buildings which are the same as the climate zone and the microclimate zone of the building to be predicted from the target building candidate set, and judging whether the target buildings in the target building candidate set belong to similar buildings of the building to be predicted by adopting the preset judging rule.
Further, whether the target building is similar to the building to be predicted or not is judged from a plurality of dimensions of the starting service time, the building area level and the energy consumption prediction characteristics of the building to be predicted of the target building.
Further, judging whether the target building is similar to the building to be predicted according to the energy consumption prediction characteristic of the building to be predicted, specifically including:
Acquiring historical energy consumption data of the building to be predicted;
Processing the historical energy consumption data of the building to be predicted to obtain energy consumption prediction characteristics of the building to be predicted, wherein the energy consumption prediction characteristics comprise temperature, humidity and time;
calculating the feature weights of the energy consumption prediction features of the target building and the building to be predicted in the classifier respectively;
Judging whether the sequencing and coefficients of the feature weights of the target building and the building to be predicted meet preset requirements, and if so, judging that the target building belongs to a similar building of the building to be predicted.
A second aspect of the present invention provides a similar building selection device based on energy consumption analysis, comprising:
The cleaning module is used for acquiring target buildings to be screened in a certain area, cleaning the daily dimension historical energy consumption data of the target buildings by using a box diagram method, and removing abnormal values;
the classification calculation module is used for classifying the cleaned historical energy consumption data according to different terms and calculating corresponding term statistics indexes aiming at the different terms respectively;
The screening module is used for screening the target building by adopting a preset screening method according to different subentry statistical indexes to obtain a target building candidate set;
and the similar building judging module is used for screening similar buildings from the target building candidate set according to the judging attribute of the building to be predicted by adopting a preset judging rule, and predicting the future energy use condition of the building to be predicted by using the historical energy use data of the similar buildings.
A third aspect of the present invention also provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, causes the processor to process the energy consumption analysis based similar building selection method of any one of claims 1 to 7.
A fourth aspect of the present invention provides an electronic device comprising:
A processor; and
A memory arranged to store computer executable instructions which when executed cause the processor to perform the energy consumption analysis based similar building selection method of any one of claims 1 to 7.
The method comprises the steps of firstly carrying out primary screening on target buildings according to daily dimension historical energy consumption data of the target buildings by adopting a box line method, then carrying out further screening on the primarily screened target buildings by adopting different screening methods according to different item classification results and statistical indexes of the target buildings to obtain target building candidate sets, and finally screening out similar buildings from the target building candidate sets by adopting preset judgment rules on the target buildings in the target building candidate sets. According to the method, the target building is processed and screened from multiple dimensions and multiple layers, so that the obtained historical energy consumption data of similar buildings can more accurately predict the future energy consumption condition of the building to be predicted, and the prediction efficiency and the reliability of the prediction result are improved.
Drawings
FIG. 1 is a flow chart of a similar building selection method based on energy consumption analysis according to the present invention;
FIG. 2 is a flow chart of the present invention for cleaning data and calculating and storing statistical indicators;
FIG. 3 is a flow chart of obtaining a candidate set according to an embodiment of the invention;
FIG. 4 is a flow chart of an embodiment of the present invention for determining whether a building is similar.
Detailed Description
The present invention is described below with reference to the drawings, but is not intended to limit the scope of the present invention.
Referring to fig. 1, a flow chart of a similar building selection method based on energy consumption analysis according to the present invention includes: s1: and acquiring target buildings to be screened in a certain area, and cleaning the daily dimension historical energy consumption data of the target buildings by using a box line graph method to remove abnormal values.
The building to be predicted is defined as a building with small historical energy consumption data, and other similar buildings with large historical energy consumption data need to be found out to serve as references for predicting the future energy consumption of the building.
The above target building is defined as a building for screening out a building that can be used for a building to be predicted as a reference.
Traversing all target buildings, cleaning the daily dimension historical energy consumption data of the target building sub-items by using a box line method, and removing abnormal values. In the embodiment of the invention, the abnormal value is data which obviously does not accord with the normal distribution rule, the data can influence the overall data distribution, the box line method determines the boundary through the range of one-fourth and three-quarter bit numbers and 1.5 times, and the data outside the boundary is the abnormal value.
S2: classifying the cleaned historical energy consumption data according to different sub-items, and respectively calculating corresponding sub-item statistical indexes aiming at the different sub-items.
Referring to fig. 2, a flow chart of cleaning data and calculating and storing statistical indicators is shown in an embodiment of the present invention.
Calculating various indexes of the target building by using the cleaned historical energy consumption data, and if the indexes are rigid branches, calculating: daily fluctuation (average value of relative error of energy consumption of day after day and day before energy consumption), rigid historical energy consumption data amount and rigid single square meter energy consumption (total annual energy consumption divided by building area); if the elastic sub-term is adopted, calculating: maximum mutual information coefficient of average temperature and historical energy consumption, elastic historical energy consumption data amount and elastic single square meter energy consumption.
The above rigid sub-term: lower temperature dependence, less annual fluctuations, such as: elevator sub-items and lighting sub-items. Elastic sub-term: higher temperature dependence, and higher annual fluctuation, such as: air-conditioning terminal sub-items and heating station sub-items.
And finally, storing the results of statistical indexes such as daily average fluctuation, historical energy consumption data quantity, single square meter energy consumption, maximum mutual information coefficient of average temperature and historical energy consumption and the like of each target building into a database.
S3: and screening the target building by adopting a preset screening method according to different sub-term statistical indexes to obtain a target building candidate set.
Referring to fig. 3, a flowchart of the method for obtaining a candidate set according to an embodiment of the present invention includes the following specific steps:
A1: and obtaining the subentry statistical index of the target building, and judging whether the target building belongs to a rigid subentry or an elastic subentry.
The term statistics index refers to different statistics indexes corresponding to different terms, for example, the statistics indexes corresponding to the rigid building comprise daily average fluctuation, historical energy consumption data amount and rigid single-square meter energy consumption; the corresponding statistical indexes of the elastic building comprise the maximum mutual information coefficient of the average temperature and the historical energy consumption, the historical energy consumption data quantity and the elastic single square meter energy consumption.
A2: if the target building belongs to the rigid sub-term, judging whether the data volume of the target building meets the preset first quantity, if so, judging whether the target building is abnormal by adopting a box line method, and removing the abnormal target building.
A3: if the target building belongs to the elastic sub-term, judging whether the data volume of the target building meets a preset second volume or not; if the target building meets the preset second quantity, calculating a statistical index of the maximum mutual information coefficient of the average temperature and the historical energy consumption of the target building, judging whether the maximum mutual information coefficient is smaller than a preset threshold value, and if so, removing the target building to obtain a target building candidate set.
From the global perspective, obtaining the statistical index results of all target buildings stored in the database, and removing the target buildings with abnormal statistical indexes from all target buildings, specifically, if the target buildings belong to rigid branches, assuming that the first number is 180, removing the buildings with data quantity smaller than 180, removing the buildings with abnormal single-square-meter energy consumption by using a box line graph method, and removing the buildings with abnormal daily average fluctuation by using the box line graph method. Assuming that the second number is 300 and the preset threshold value is 0.3, buildings with data volume smaller than 300 pieces and buildings with average temperature and maximum mutual information coefficient of historical energy consumption smaller than 0.3 are removed.
In an optional implementation manner of the embodiment of the invention, the method further comprises traversing all areas from the aspect of areas, obtaining statistical indexes of all the rest target buildings in the areas, removing buildings with abnormal single-square-meter energy consumption in the areas by using a box line graph method, and removing buildings with abnormal daily average energy consumption in the areas by using the box line graph method. The final target building candidate set is saved to a database.
S4: and screening similar buildings from the target building candidate set according to the judging attribute of the building to be predicted by adopting a preset judging rule, and predicting the future energy use condition of the building to be predicted by using the historical energy use data of the similar buildings.
Referring to fig. 4, in an embodiment of the present invention, whether the building is similar is determined, in the embodiment of the present invention, the step of screening similar buildings from the target building candidate set according to the determination attribute of the building to be predicted by using a preset determination rule includes:
inquiring target buildings which are the same as the building area to be predicted from the target building candidate set, and judging whether the target buildings in the target building candidate set belong to similar buildings of the building to be predicted by adopting the preset judging rule.
That is, if a target building in the target building candidate set and a building to be predicted belong to the same area, it can be judged whether the target building and the building to be predicted belong to similar buildings by:
firstly, judging that the starting use time of the target building meets the requirement, and putting the target building into use after 2015;
and/or judging whether the area grades of the target building and the building to be predicted belong to the same grade or not, if the building areas of the building to be predicted and the target building are 50000 square meters, and the like. In the embodiment of the invention, the building surface is divided into four grades, 1 grade: less than 50000 square meters; 2 stages: 50000-75000 square meters; 3 stages: 75000-100000 square meters; 4 stages: 100000 square meters or more.
And/or if there is little historical energy consumption data in the building to be predicted, calculating the specific gravity of the target building and the feature of the building to be predicted in the classifier according to the features (temperature, humidity, time and the like) to be used in the energy consumption prediction, wherein the feature weights between the two buildings are required to be ranked the same, and the weight coefficients are similar.
In an optional implementation manner of the embodiment of the present invention, the selecting, by using a preset judging rule, a similar building from the target building candidate set according to the judging attribute of the building to be predicted includes:
Inquiring target buildings which are the same as the climate zone and the microclimate zone of the building to be predicted from the target building candidate set, and judging whether the target buildings in the target building candidate set belong to similar buildings of the building to be predicted by adopting the preset judging rule.
The method comprises the steps of firstly carrying out primary screening on target buildings according to daily dimension historical energy consumption data of the target buildings by adopting a box line method, then carrying out further screening on the primarily screened target buildings by adopting different screening methods according to different item classification results and statistical indexes of the target buildings to obtain target building candidate sets, and finally screening out similar buildings from the target building candidate sets by adopting preset judgment rules on the target buildings in the target building candidate sets. According to the method, the target building is processed and screened from multiple dimensions and multiple layers, so that the obtained historical energy consumption data of the similar building can more accurately predict the future energy consumption condition of the building to be predicted, and the prediction efficiency is improved.
A second aspect of the present invention provides a similar building selection device based on energy consumption analysis, comprising:
The cleaning module is used for acquiring target buildings to be screened in a certain area, cleaning the daily dimension historical energy consumption data of the target buildings by using a box diagram method, and removing abnormal values;
the classification calculation module is used for classifying the cleaned historical energy consumption data according to different terms and calculating corresponding term statistics indexes aiming at the different terms respectively;
The screening module is used for screening the target building by adopting a preset screening method according to different subentry statistical indexes to obtain a target building candidate set;
and the similar building judging module is used for screening similar buildings from the target building candidate set according to the judging attribute of the building to be predicted by adopting a preset judging rule, and predicting the future energy use condition of the building to be predicted by using the historical energy use data of the similar buildings.
The third aspect of the present invention also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, causes the processor to process a similar building selection method based on energy consumption analysis.
A fourth aspect of the present invention provides an electronic device comprising:
A processor; and
A memory arranged to store computer executable instructions that when executed cause the processor to perform a similar building selection method based on energy consumption analysis.
While the invention has been described in detail in the foregoing general description and specific examples, it will be apparent to those skilled in the art that modifications and improvements can be made thereto. Accordingly, such modifications or improvements may be made without departing from the spirit of the invention and are intended to be within the scope of the invention as claimed.
Claims (10)
1. A similar building selecting method based on energy consumption analysis is characterized by comprising the following steps:
acquiring a target building to be screened in a certain area, and cleaning daily dimension historical energy consumption data of the target building by using a box line graph method to remove abnormal values;
Classifying the cleaned historical energy consumption data according to different sub-items, and respectively calculating corresponding sub-item statistical indexes aiming at the different sub-items;
Screening the target building by adopting a preset screening method according to different sub-term statistical indexes to obtain a target building candidate set;
and screening out similar buildings from the target building candidate set by adopting a preset judging rule according to judging attributes of the buildings to be predicted, and predicting future energy consumption conditions of the buildings to be predicted according to historical energy consumption data of the similar buildings, wherein the future energy consumption conditions are future energy consumption quota of the buildings to be predicted.
2. The energy consumption analysis-based similar building selection method according to claim 1, wherein the classifying the cleaned historical energy consumption data according to different terms and calculating corresponding term statistics indexes for the different terms respectively comprises the steps of:
Classifying the cleaned historical energy consumption data into a rigid sub-term and an elastic sub-term;
The rigidity index calculated on the rigidity sub-term comprises: average fluctuation, historical energy consumption data amount and rigid single square meter energy consumption;
The elasticity index calculated for the elasticity sub-term includes: maximum mutual information coefficient of average temperature and historical energy consumption, historical energy consumption data amount and elastic single square meter energy consumption.
3. The method for selecting similar buildings based on energy consumption analysis according to claim 1, wherein the step of selecting the target building by a preset screening method according to different sub-term statistical indexes to obtain a target building candidate set comprises the following steps:
Obtaining a subentry statistical index of the target building, and judging whether the target building belongs to a rigid subentry or an elastic subentry;
judging whether the data volume of the target building meets the preset first quantity or not if the target building belongs to the rigid sub-term, judging whether the target building is abnormal or not by adopting a box line method if the data volume of the target building meets the preset first quantity, and removing the abnormal target building;
If the target building belongs to the elastic sub-term, judging whether the data volume of the target building meets a preset second volume or not; if the target building meets the preset second quantity, calculating a statistical index of the maximum mutual information coefficient of the average temperature and the historical energy consumption of the target building, judging whether the maximum mutual information coefficient is smaller than a preset threshold value, and if so, removing the target building to obtain a target building candidate set.
4. The similar building selection method based on energy consumption analysis according to claim 3, further comprising removing buildings with abnormal energy consumption and daily average energy consumption in a single square meter from the screened target building by using a box line method to obtain a target building candidate set.
5. The method for selecting similar buildings based on energy consumption analysis according to claim 1, wherein the selecting similar buildings from the target building candidate set according to the judging attribute of the building to be predicted by adopting a preset judging rule comprises:
inquiring target buildings which are the same as the building area to be predicted from the target building candidate set, and judging whether the target buildings in the target building candidate set belong to similar buildings of the building to be predicted by adopting the preset judging rule;
And/or inquiring the target buildings which are the same as the climate zone and the microclimate zone of the building to be predicted from the target building candidate set, and judging whether the target buildings in the target building candidate set belong to similar buildings of the building to be predicted by adopting the preset judging rule.
6. The energy consumption analysis-based similar building selection method according to claim 1, wherein whether the target building is similar to the building to be predicted is determined from a plurality of dimensions of a start-up use time of the target building, a building area level, and an energy consumption prediction characteristic of the building to be predicted, respectively.
7. The energy consumption analysis-based similar building selection method according to claim 6, wherein determining whether the target building is similar to the building to be predicted according to the energy consumption prediction characteristics of the building to be predicted specifically comprises:
Acquiring historical energy consumption data of the building to be predicted;
Processing the historical energy consumption data of the building to be predicted to obtain energy consumption prediction characteristics of the building to be predicted, wherein the energy consumption prediction characteristics comprise temperature, humidity and time;
calculating the feature weights of the energy consumption prediction features of the target building and the building to be predicted in the classifier respectively;
Judging whether the sequencing and coefficients of the feature weights of the target building and the building to be predicted meet preset requirements, and if so, judging that the target building belongs to a similar building of the building to be predicted.
8. A similar building selection device based on energy consumption analysis, comprising:
The cleaning module is used for acquiring target buildings to be screened in a certain area, cleaning the daily dimension historical energy consumption data of the target buildings by using a box diagram method, and removing abnormal values;
the classification calculation module is used for classifying the cleaned historical energy consumption data according to different terms and calculating corresponding term statistics indexes aiming at the different terms respectively;
The screening module is used for screening the target building by adopting a preset screening method according to different subentry statistical indexes to obtain a target building candidate set;
the similar building judging module is used for screening similar buildings from the target building candidate set according to preset judging rules and predicting future energy consumption conditions of the similar buildings according to the historical energy consumption data of the similar buildings, wherein the future energy consumption conditions are future energy consumption rates of the buildings to be predicted.
9. A computer readable storage medium having stored thereon a computer program which, when executed by a processor, causes the processor to process the energy consumption analysis based similar building selection method of any of claims 1-7.
10. An electronic device, the electronic device comprising:
A processor; and
A memory arranged to store computer executable instructions which when executed cause the processor to perform the energy consumption analysis based similar building selection method of any one of claims 1 to 7.
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| CN116255705B (en) * | 2022-12-30 | 2025-09-12 | 中关村芯海择优科技有限公司 | Method, device, electronic device and storage medium for determining building spatiotemporal temperature |
| CN116757534B (en) * | 2023-06-15 | 2024-03-15 | 中国标准化研究院 | A reliability analysis method for smart refrigerators based on neural training network |
| CN117290797B (en) * | 2023-11-24 | 2024-02-02 | 国网山东省电力公司济宁供电公司 | Building energy consumption prediction method, system, device and medium |
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