Demand Forecasting for a Mixed-Use Building Using Agent-Schedule Information with a Data-Driven Model

There is great interest in data-driven modelling for the forecasting of building energy consumption while using machine learning (ML) modelling. However, little research considers classification-based ML models. This paper compares the regression and classification ML models for daily electricity an...
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Autor*in:	Zihao Li [verfasserIn] Daniel Friedrich [verfasserIn] Gareth P. Harrison [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	data driven buildings thermal demand electricity demand demand prediction

Übergeordnetes Werk:	In: Energies - MDPI AG, 2008, 13(2020), 4, p 780
Übergeordnetes Werk:	volume:13 ; year:2020 ; number:4, p 780

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/en13040780

Katalog-ID:	DOAJ025367625

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allfields	10.3390/en13040780 doi (DE-627)DOAJ025367625 (DE-599)DOAJcaf65dd8775941c78f6bdf8cc6d84605 DE-627 ger DE-627 rakwb eng Zihao Li verfasserin aut Demand Forecasting for a Mixed-Use Building Using Agent-Schedule Information with a Data-Driven Model 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier There is great interest in data-driven modelling for the forecasting of building energy consumption while using machine learning (ML) modelling. However, little research considers classification-based ML models. This paper compares the regression and classification ML models for daily electricity and thermal load modelling in a large, mixed-use, university building. The independent feature variables of the model include outdoor temperature, historical energy consumption data sets, and several types of ‘agent schedules’ that provide proxy information that is based on broad classes of activity undertaken by the building’s inhabitants. The case study compares four different ML models testing three different feature sets with a genetic algorithm (GA) used to optimize the feature sets for those ML models without an embedded feature selection process. The results show that the regression models perform significantly better than classification models for the prediction of electricity demand and slightly better for the prediction of heat demand. The GA feature selection improves the performance of all models and demonstrates that historical heat demand, temperature, and the ‘agent schedules’, which derive from large occupancy fluctuations in the building, are the main factors influencing the heat demand prediction. For electricity demand prediction, feature selection picks almost all ‘agent schedule’ features that are available and the historical electricity demand. Historical heat demand is not picked as a feature for electricity demand prediction by the GA feature selection and vice versa. However, the exclusion of historical heat/electricity demand from the selected features significantly reduces the performance of the demand prediction. data driven buildings thermal demand electricity demand demand prediction Technology T Daniel Friedrich verfasserin aut Gareth P. Harrison verfasserin aut In Energies MDPI AG, 2008 13(2020), 4, p 780 (DE-627)572083742 (DE-600)2437446-5 19961073 nnns volume:13 year:2020 number:4, p 780 https://doi.org/10.3390/en13040780 kostenfrei https://doaj.org/article/caf65dd8775941c78f6bdf8cc6d84605 kostenfrei https://www.mdpi.com/1996-1073/13/4/780 kostenfrei https://doaj.org/toc/1996-1073 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2020 4, p 780
spelling	10.3390/en13040780 doi (DE-627)DOAJ025367625 (DE-599)DOAJcaf65dd8775941c78f6bdf8cc6d84605 DE-627 ger DE-627 rakwb eng Zihao Li verfasserin aut Demand Forecasting for a Mixed-Use Building Using Agent-Schedule Information with a Data-Driven Model 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier There is great interest in data-driven modelling for the forecasting of building energy consumption while using machine learning (ML) modelling. However, little research considers classification-based ML models. This paper compares the regression and classification ML models for daily electricity and thermal load modelling in a large, mixed-use, university building. The independent feature variables of the model include outdoor temperature, historical energy consumption data sets, and several types of ‘agent schedules’ that provide proxy information that is based on broad classes of activity undertaken by the building’s inhabitants. The case study compares four different ML models testing three different feature sets with a genetic algorithm (GA) used to optimize the feature sets for those ML models without an embedded feature selection process. The results show that the regression models perform significantly better than classification models for the prediction of electricity demand and slightly better for the prediction of heat demand. The GA feature selection improves the performance of all models and demonstrates that historical heat demand, temperature, and the ‘agent schedules’, which derive from large occupancy fluctuations in the building, are the main factors influencing the heat demand prediction. For electricity demand prediction, feature selection picks almost all ‘agent schedule’ features that are available and the historical electricity demand. Historical heat demand is not picked as a feature for electricity demand prediction by the GA feature selection and vice versa. However, the exclusion of historical heat/electricity demand from the selected features significantly reduces the performance of the demand prediction. data driven buildings thermal demand electricity demand demand prediction Technology T Daniel Friedrich verfasserin aut Gareth P. Harrison verfasserin aut In Energies MDPI AG, 2008 13(2020), 4, p 780 (DE-627)572083742 (DE-600)2437446-5 19961073 nnns volume:13 year:2020 number:4, p 780 https://doi.org/10.3390/en13040780 kostenfrei https://doaj.org/article/caf65dd8775941c78f6bdf8cc6d84605 kostenfrei https://www.mdpi.com/1996-1073/13/4/780 kostenfrei https://doaj.org/toc/1996-1073 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2020 4, p 780
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allfieldsGer	10.3390/en13040780 doi (DE-627)DOAJ025367625 (DE-599)DOAJcaf65dd8775941c78f6bdf8cc6d84605 DE-627 ger DE-627 rakwb eng Zihao Li verfasserin aut Demand Forecasting for a Mixed-Use Building Using Agent-Schedule Information with a Data-Driven Model 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier There is great interest in data-driven modelling for the forecasting of building energy consumption while using machine learning (ML) modelling. However, little research considers classification-based ML models. This paper compares the regression and classification ML models for daily electricity and thermal load modelling in a large, mixed-use, university building. The independent feature variables of the model include outdoor temperature, historical energy consumption data sets, and several types of ‘agent schedules’ that provide proxy information that is based on broad classes of activity undertaken by the building’s inhabitants. The case study compares four different ML models testing three different feature sets with a genetic algorithm (GA) used to optimize the feature sets for those ML models without an embedded feature selection process. The results show that the regression models perform significantly better than classification models for the prediction of electricity demand and slightly better for the prediction of heat demand. The GA feature selection improves the performance of all models and demonstrates that historical heat demand, temperature, and the ‘agent schedules’, which derive from large occupancy fluctuations in the building, are the main factors influencing the heat demand prediction. For electricity demand prediction, feature selection picks almost all ‘agent schedule’ features that are available and the historical electricity demand. Historical heat demand is not picked as a feature for electricity demand prediction by the GA feature selection and vice versa. However, the exclusion of historical heat/electricity demand from the selected features significantly reduces the performance of the demand prediction. data driven buildings thermal demand electricity demand demand prediction Technology T Daniel Friedrich verfasserin aut Gareth P. Harrison verfasserin aut In Energies MDPI AG, 2008 13(2020), 4, p 780 (DE-627)572083742 (DE-600)2437446-5 19961073 nnns volume:13 year:2020 number:4, p 780 https://doi.org/10.3390/en13040780 kostenfrei https://doaj.org/article/caf65dd8775941c78f6bdf8cc6d84605 kostenfrei https://www.mdpi.com/1996-1073/13/4/780 kostenfrei https://doaj.org/toc/1996-1073 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2020 4, p 780
allfieldsSound	10.3390/en13040780 doi (DE-627)DOAJ025367625 (DE-599)DOAJcaf65dd8775941c78f6bdf8cc6d84605 DE-627 ger DE-627 rakwb eng Zihao Li verfasserin aut Demand Forecasting for a Mixed-Use Building Using Agent-Schedule Information with a Data-Driven Model 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier There is great interest in data-driven modelling for the forecasting of building energy consumption while using machine learning (ML) modelling. However, little research considers classification-based ML models. This paper compares the regression and classification ML models for daily electricity and thermal load modelling in a large, mixed-use, university building. The independent feature variables of the model include outdoor temperature, historical energy consumption data sets, and several types of ‘agent schedules’ that provide proxy information that is based on broad classes of activity undertaken by the building’s inhabitants. The case study compares four different ML models testing three different feature sets with a genetic algorithm (GA) used to optimize the feature sets for those ML models without an embedded feature selection process. The results show that the regression models perform significantly better than classification models for the prediction of electricity demand and slightly better for the prediction of heat demand. The GA feature selection improves the performance of all models and demonstrates that historical heat demand, temperature, and the ‘agent schedules’, which derive from large occupancy fluctuations in the building, are the main factors influencing the heat demand prediction. For electricity demand prediction, feature selection picks almost all ‘agent schedule’ features that are available and the historical electricity demand. Historical heat demand is not picked as a feature for electricity demand prediction by the GA feature selection and vice versa. However, the exclusion of historical heat/electricity demand from the selected features significantly reduces the performance of the demand prediction. data driven buildings thermal demand electricity demand demand prediction Technology T Daniel Friedrich verfasserin aut Gareth P. Harrison verfasserin aut In Energies MDPI AG, 2008 13(2020), 4, p 780 (DE-627)572083742 (DE-600)2437446-5 19961073 nnns volume:13 year:2020 number:4, p 780 https://doi.org/10.3390/en13040780 kostenfrei https://doaj.org/article/caf65dd8775941c78f6bdf8cc6d84605 kostenfrei https://www.mdpi.com/1996-1073/13/4/780 kostenfrei https://doaj.org/toc/1996-1073 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2020 4, p 780
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source	In Energies 13(2020), 4, p 780 volume:13 year:2020 number:4, p 780
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author	Zihao Li
spellingShingle	Zihao Li misc data driven misc buildings misc thermal demand misc electricity demand misc demand prediction misc Technology misc T Demand Forecasting for a Mixed-Use Building Using Agent-Schedule Information with a Data-Driven Model
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topic_title	Demand Forecasting for a Mixed-Use Building Using Agent-Schedule Information with a Data-Driven Model data driven buildings thermal demand electricity demand demand prediction
topic	misc data driven misc buildings misc thermal demand misc electricity demand misc demand prediction misc Technology misc T
topic_unstemmed	misc data driven misc buildings misc thermal demand misc electricity demand misc demand prediction misc Technology misc T
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title	Demand Forecasting for a Mixed-Use Building Using Agent-Schedule Information with a Data-Driven Model
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title_sort	demand forecasting for a mixed-use building using agent-schedule information with a data-driven model
title_auth	Demand Forecasting for a Mixed-Use Building Using Agent-Schedule Information with a Data-Driven Model
abstract	There is great interest in data-driven modelling for the forecasting of building energy consumption while using machine learning (ML) modelling. However, little research considers classification-based ML models. This paper compares the regression and classification ML models for daily electricity and thermal load modelling in a large, mixed-use, university building. The independent feature variables of the model include outdoor temperature, historical energy consumption data sets, and several types of ‘agent schedules’ that provide proxy information that is based on broad classes of activity undertaken by the building’s inhabitants. The case study compares four different ML models testing three different feature sets with a genetic algorithm (GA) used to optimize the feature sets for those ML models without an embedded feature selection process. The results show that the regression models perform significantly better than classification models for the prediction of electricity demand and slightly better for the prediction of heat demand. The GA feature selection improves the performance of all models and demonstrates that historical heat demand, temperature, and the ‘agent schedules’, which derive from large occupancy fluctuations in the building, are the main factors influencing the heat demand prediction. For electricity demand prediction, feature selection picks almost all ‘agent schedule’ features that are available and the historical electricity demand. Historical heat demand is not picked as a feature for electricity demand prediction by the GA feature selection and vice versa. However, the exclusion of historical heat/electricity demand from the selected features significantly reduces the performance of the demand prediction.
abstractGer	There is great interest in data-driven modelling for the forecasting of building energy consumption while using machine learning (ML) modelling. However, little research considers classification-based ML models. This paper compares the regression and classification ML models for daily electricity and thermal load modelling in a large, mixed-use, university building. The independent feature variables of the model include outdoor temperature, historical energy consumption data sets, and several types of ‘agent schedules’ that provide proxy information that is based on broad classes of activity undertaken by the building’s inhabitants. The case study compares four different ML models testing three different feature sets with a genetic algorithm (GA) used to optimize the feature sets for those ML models without an embedded feature selection process. The results show that the regression models perform significantly better than classification models for the prediction of electricity demand and slightly better for the prediction of heat demand. The GA feature selection improves the performance of all models and demonstrates that historical heat demand, temperature, and the ‘agent schedules’, which derive from large occupancy fluctuations in the building, are the main factors influencing the heat demand prediction. For electricity demand prediction, feature selection picks almost all ‘agent schedule’ features that are available and the historical electricity demand. Historical heat demand is not picked as a feature for electricity demand prediction by the GA feature selection and vice versa. However, the exclusion of historical heat/electricity demand from the selected features significantly reduces the performance of the demand prediction.
abstract_unstemmed	There is great interest in data-driven modelling for the forecasting of building energy consumption while using machine learning (ML) modelling. However, little research considers classification-based ML models. This paper compares the regression and classification ML models for daily electricity and thermal load modelling in a large, mixed-use, university building. The independent feature variables of the model include outdoor temperature, historical energy consumption data sets, and several types of ‘agent schedules’ that provide proxy information that is based on broad classes of activity undertaken by the building’s inhabitants. The case study compares four different ML models testing three different feature sets with a genetic algorithm (GA) used to optimize the feature sets for those ML models without an embedded feature selection process. The results show that the regression models perform significantly better than classification models for the prediction of electricity demand and slightly better for the prediction of heat demand. The GA feature selection improves the performance of all models and demonstrates that historical heat demand, temperature, and the ‘agent schedules’, which derive from large occupancy fluctuations in the building, are the main factors influencing the heat demand prediction. For electricity demand prediction, feature selection picks almost all ‘agent schedule’ features that are available and the historical electricity demand. Historical heat demand is not picked as a feature for electricity demand prediction by the GA feature selection and vice versa. However, the exclusion of historical heat/electricity demand from the selected features significantly reduces the performance of the demand prediction.
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title_short	Demand Forecasting for a Mixed-Use Building Using Agent-Schedule Information with a Data-Driven Model
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