A Short-Term Residential Load Forecasting Model Based on LSTM Recurrent Neural Network Considering Weather Features

Yizhen Wang, Ningqing Zhang and Xiong Chen
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Yizhen Wang: School of Information Science and Technology, Fudan University, Shanghai 200433, China
Ningqing Zhang: School of Information Science and Technology, Fudan University, Shanghai 200433, China
Xiong Chen: School of Information Science and Technology, Fudan University, Shanghai 200433, China

Energies, 2021, vol. 14, issue 10, 1-13

Abstract: With economic growth, the demand for power systems is increasingly large. Short-term load forecasting (STLF) becomes an indispensable factor to enhance the application of a smart grid (SG). Other than forecasting aggregated residential loads in a large scale, it is still an urgent problem to improve the accuracy of power load forecasting for individual energy users due to high volatility and uncertainty. However, as an important variable that affects the power consumption pattern, the influence of weather factors on residential load prediction is rarely studied. In this paper, we review the related research of power load forecasting and introduce a short-term residential load forecasting model based on a long short-term memory (LSTM) recurrent neural network with weather features as an input.

Keywords: short-term load forecasting; recurrent neural network; residential load forecasting; meteorological data (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 (11)

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