Hourly Energy Consumption Forecasting for Office Buildings Based on Support Vector Machine

Aimed at the nonlinearity and uncertainty of building energy consumption, a forecasting approach based on the support vector machine is proposed in this paper for the prediction of hourly energy consumption of an office building. The univariate model test is used to determine the input parameters. S...

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Autores principales:	XIAO Ran, WEI Ziqing, ZHAI Xiaoqiang
Formato:	article
Lenguaje:	ZH
Publicado:	Editorial Office of Journal of Shanghai Jiao Tong University 2021
Materias:	energy consumption forecasting approach support vector machine grid search confidence interval Engineering (General). Civil engineering (General) TA1-2040 Chemical engineering TP155-156 Naval architecture. Shipbuilding. Marine engineering VM1-989
Acceso en línea:	https://doaj.org/article/70d4f9da31594529943691a2c3294798
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spelling	oai:doaj.org-article:70d4f9da31594529943691a2c32947982021-11-04T09:28:54ZHourly Energy Consumption Forecasting for Office Buildings Based on Support Vector Machine1006-246710.16183/j.cnki.jsjtu.2019.310https://doaj.org/article/70d4f9da31594529943691a2c32947982021-03-01T00:00:00Zhttp://xuebao.sjtu.edu.cn/CN/10.16183/j.cnki.jsjtu.2019.310https://doaj.org/toc/1006-2467Aimed at the nonlinearity and uncertainty of building energy consumption, a forecasting approach based on the support vector machine is proposed in this paper for the prediction of hourly energy consumption of an office building. The univariate model test is used to determine the input parameters. Superior model hyper-parameters are found by grid search optimization. The confidence interval of the model fitting error is applied to describe the uncertainty of building energy consumption. A case study is conducted using the data collected from an actual office building to verify the proposed approach. The results show that the overall mean absolute percentage error (MAPE) of the model after grid search optimization is reduced by 31.3%, and a higher model precision is achieved. After combining the prediction with the confidence interval, MAPE is found to be lower than 1.5% in different seasons and the building operation fluctuations are embodied. This approach can be used in the diagnosis and optimization of building operation.XIAO RanWEI ZiqingZHAI XiaoqiangEditorial Office of Journal of Shanghai Jiao Tong Universityarticleenergy consumptionforecasting approachsupport vector machinegrid searchconfidence intervalEngineering (General). Civil engineering (General)TA1-2040Chemical engineeringTP155-156Naval architecture. Shipbuilding. Marine engineeringVM1-989ZHShanghai Jiaotong Daxue xuebao, Vol 55, Iss 03, Pp 331-336 (2021)
institution	DOAJ
collection	DOAJ
language	ZH
topic	energy consumption forecasting approach support vector machine grid search confidence interval Engineering (General). Civil engineering (General) TA1-2040 Chemical engineering TP155-156 Naval architecture. Shipbuilding. Marine engineering VM1-989
spellingShingle	energy consumption forecasting approach support vector machine grid search confidence interval Engineering (General). Civil engineering (General) TA1-2040 Chemical engineering TP155-156 Naval architecture. Shipbuilding. Marine engineering VM1-989 XIAO Ran WEI Ziqing ZHAI Xiaoqiang Hourly Energy Consumption Forecasting for Office Buildings Based on Support Vector Machine
description	Aimed at the nonlinearity and uncertainty of building energy consumption, a forecasting approach based on the support vector machine is proposed in this paper for the prediction of hourly energy consumption of an office building. The univariate model test is used to determine the input parameters. Superior model hyper-parameters are found by grid search optimization. The confidence interval of the model fitting error is applied to describe the uncertainty of building energy consumption. A case study is conducted using the data collected from an actual office building to verify the proposed approach. The results show that the overall mean absolute percentage error (MAPE) of the model after grid search optimization is reduced by 31.3%, and a higher model precision is achieved. After combining the prediction with the confidence interval, MAPE is found to be lower than 1.5% in different seasons and the building operation fluctuations are embodied. This approach can be used in the diagnosis and optimization of building operation.
format	article
author	XIAO Ran WEI Ziqing ZHAI Xiaoqiang
author_facet	XIAO Ran WEI Ziqing ZHAI Xiaoqiang
author_sort	XIAO Ran
title	Hourly Energy Consumption Forecasting for Office Buildings Based on Support Vector Machine
title_short	Hourly Energy Consumption Forecasting for Office Buildings Based on Support Vector Machine
title_full	Hourly Energy Consumption Forecasting for Office Buildings Based on Support Vector Machine
title_fullStr	Hourly Energy Consumption Forecasting for Office Buildings Based on Support Vector Machine
title_full_unstemmed	Hourly Energy Consumption Forecasting for Office Buildings Based on Support Vector Machine
title_sort	hourly energy consumption forecasting for office buildings based on support vector machine
publisher	Editorial Office of Journal of Shanghai Jiao Tong University
publishDate	2021
url	https://doaj.org/article/70d4f9da31594529943691a2c3294798
work_keys_str_mv	AT xiaoran hourlyenergyconsumptionforecastingforofficebuildingsbasedonsupportvectormachine AT weiziqing hourlyenergyconsumptionforecastingforofficebuildingsbasedonsupportvectormachine AT zhaixiaoqiang hourlyenergyconsumptionforecastingforofficebuildingsbasedonsupportvectormachine
_version_	1718444968059600896

Hourly Energy Consumption Forecasting for Office Buildings Based on Support Vector Machine

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