A Novel Short-Term Residential Electric Load Forecasting Method Based on Adaptive Load Aggregation and Deep Learning Algorithms

Short-term residential load forecasting is the precondition of the day-ahead and intra-day scheduling strategy of the household microgrid. Existing short-term electric load forecasting methods are mainly used to obtain regional power load for system-level power dispatch. Due to the high volatility,...

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Autores principales:	Tingting Hou, Rengcun Fang, Jinrui Tang, Ganheng Ge, Dongjun Yang, Jianchao Liu, Wei Zhang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	residential electric load forecasting adaptive load aggregation deep learning home energy management load cluster Technology T
Acceso en línea:	https://doaj.org/article/a015620b0ec74e34a959f7ffd4724756
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id	oai:doaj.org-article:a015620b0ec74e34a959f7ffd4724756
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spelling	oai:doaj.org-article:a015620b0ec74e34a959f7ffd47247562021-11-25T17:29:01ZA Novel Short-Term Residential Electric Load Forecasting Method Based on Adaptive Load Aggregation and Deep Learning Algorithms10.3390/en142278201996-1073https://doaj.org/article/a015620b0ec74e34a959f7ffd47247562021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/22/7820https://doaj.org/toc/1996-1073Short-term residential load forecasting is the precondition of the day-ahead and intra-day scheduling strategy of the household microgrid. Existing short-term electric load forecasting methods are mainly used to obtain regional power load for system-level power dispatch. Due to the high volatility, strong randomness, and weak regularity of the residential load of a single household, the mean absolute percentage error (MAPE) of the traditional methods forecasting results would be too big to be used for home energy management. With the increase in the total number of households, the aggregated load becomes more and more stable, and the cyclical pattern of the aggregated load becomes more and more distinct. In the meantime, the maximum daily load does not increase linearly with the increase in households in a small area. Therefore, in our proposed short-term residential load forecasting method, an optimal number of households would be selected adaptively, and the total aggregated residential load of the selected households is used for load prediction. In addition, ordering points to identify the clustering structure (OPTICS) algorithm are also selected to cluster households with similar power consumption patterns adaptively. It can be used to enhance the periodic regularity of the aggregated load in alternative. The aggregated residential load and encoded external factors are then used to predict the load in the next half an hour. The long short-term memory (LSTM) deep learning algorithm is used in the prediction because of its inherited ability to maintain historical data regularity in the forecasting process. The experimental data have verified the effectiveness and accuracy of our proposed method.Tingting HouRengcun FangJinrui TangGanheng GeDongjun YangJianchao LiuWei ZhangMDPI AGarticleresidential electric load forecastingadaptive load aggregationdeep learninghome energy managementload clusterTechnologyTENEnergies, Vol 14, Iss 7820, p 7820 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	residential electric load forecasting adaptive load aggregation deep learning home energy management load cluster Technology T
spellingShingle	residential electric load forecasting adaptive load aggregation deep learning home energy management load cluster Technology T Tingting Hou Rengcun Fang Jinrui Tang Ganheng Ge Dongjun Yang Jianchao Liu Wei Zhang A Novel Short-Term Residential Electric Load Forecasting Method Based on Adaptive Load Aggregation and Deep Learning Algorithms
description	Short-term residential load forecasting is the precondition of the day-ahead and intra-day scheduling strategy of the household microgrid. Existing short-term electric load forecasting methods are mainly used to obtain regional power load for system-level power dispatch. Due to the high volatility, strong randomness, and weak regularity of the residential load of a single household, the mean absolute percentage error (MAPE) of the traditional methods forecasting results would be too big to be used for home energy management. With the increase in the total number of households, the aggregated load becomes more and more stable, and the cyclical pattern of the aggregated load becomes more and more distinct. In the meantime, the maximum daily load does not increase linearly with the increase in households in a small area. Therefore, in our proposed short-term residential load forecasting method, an optimal number of households would be selected adaptively, and the total aggregated residential load of the selected households is used for load prediction. In addition, ordering points to identify the clustering structure (OPTICS) algorithm are also selected to cluster households with similar power consumption patterns adaptively. It can be used to enhance the periodic regularity of the aggregated load in alternative. The aggregated residential load and encoded external factors are then used to predict the load in the next half an hour. The long short-term memory (LSTM) deep learning algorithm is used in the prediction because of its inherited ability to maintain historical data regularity in the forecasting process. The experimental data have verified the effectiveness and accuracy of our proposed method.
format	article
author	Tingting Hou Rengcun Fang Jinrui Tang Ganheng Ge Dongjun Yang Jianchao Liu Wei Zhang
author_facet	Tingting Hou Rengcun Fang Jinrui Tang Ganheng Ge Dongjun Yang Jianchao Liu Wei Zhang
author_sort	Tingting Hou
title	A Novel Short-Term Residential Electric Load Forecasting Method Based on Adaptive Load Aggregation and Deep Learning Algorithms
title_short	A Novel Short-Term Residential Electric Load Forecasting Method Based on Adaptive Load Aggregation and Deep Learning Algorithms
title_full	A Novel Short-Term Residential Electric Load Forecasting Method Based on Adaptive Load Aggregation and Deep Learning Algorithms
title_fullStr	A Novel Short-Term Residential Electric Load Forecasting Method Based on Adaptive Load Aggregation and Deep Learning Algorithms
title_full_unstemmed	A Novel Short-Term Residential Electric Load Forecasting Method Based on Adaptive Load Aggregation and Deep Learning Algorithms
title_sort	novel short-term residential electric load forecasting method based on adaptive load aggregation and deep learning algorithms
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/a015620b0ec74e34a959f7ffd4724756
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A Novel Short-Term Residential Electric Load Forecasting Method Based on Adaptive Load Aggregation and Deep Learning Algorithms

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