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

Tingting Hou, Rengcun Fang, Jinrui Tang, Ganheng Ge, Dongjun Yang, Jianchao Liu and Wei Zhang
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Tingting Hou: Economics and Technology Research Institute, State Grid Hubei Electric Power Company, Wuhan 430077, China
Rengcun Fang: Economics and Technology Research Institute, State Grid Hubei Electric Power Company, Wuhan 430077, China
Jinrui Tang: School of Automation, Wuhan University of Technology, Wuhan 430070, China
Ganheng Ge: School of Automation, Wuhan University of Technology, Wuhan 430070, China
Dongjun Yang: Economics and Technology Research Institute, State Grid Hubei Electric Power Company, Wuhan 430077, China
Jianchao Liu: School of Automation, Wuhan University of Technology, Wuhan 430070, China
Wei Zhang: Economics and Technology Research Institute, State Grid Hubei Electric Power Company, Wuhan 430077, China

Energies, 2021, vol. 14, issue 22, 1-21

Abstract: 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.

Keywords: residential electric load forecasting; adaptive load aggregation; deep learning; home energy management; load cluster (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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