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

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...

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Bibliographic Details
Published in:Energies (Basel) 2021-05, Vol.14 (10), p.2737
Main Authors: Wang, Yizhen, Zhang, Ningqing, Chen, Xiong
Format: Article
Language:English
Subjects:
Accuracy
Automatic meter reading
Datasets
Economic development
Economic forecasting
Economic growth
Electrical loads
Forecasting
Households
Humidity
Lifestyles
Long short-term memory
meteorological data
Neural networks
Power consumption
recurrent neural network
Recurrent neural networks
residential load forecasting
short-term load forecasting
Time series
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Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en14102737