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

被引：34
|
作者：
Wang, Yizhen [1 ]
Zhang, Ningqing [1 ]
Chen, Xiong [1 ,2 ]
机构：
[1] Fudan Univ, Sch Informat Sci & Technol, Shanghai 200433, Peoples R China
[2] Zhuhai Fudan Innovat Inst, Zhuhai 519000, Peoples R China
来源：
ENERGIES | 2021年 / 14卷 / 10期
关键词：
short-term load forecasting; recurrent neural network; residential load forecasting; meteorological data; PREDICTION;
D O I：
10.3390/en14102737
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
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.
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页数：13
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