Survey on Short-term Load Forecasting Algorithm Based on Machine Learning

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    Load forecasting methods emerge one after another to maintain the stability of power grids. However, due to the characteristic difference in the generalization ability of algorithms and model complexity, the applicability of these methods to load forecasting differs. This study discusses and summarizes the research status of short-term power load forecasting both at home and abroad in the past five years from multiple dimensions, such as experimental data sets, data preprocessing, forecasting algorithms, optimization models, and evaluation methods. Meanwhile, we also present a summary of the advantages, disadvantages, and applicability of various forecasting algorithms, and the development trend of the short-term power load forecasting system is expounded and predicted. This study is expected to provide a reference for the forecasting model selection of power system loads in the future.

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  • Received:December 28,2021
  • Revised:January 28,2022
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  • Online: July 07,2022
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