Machine-Learning based methods in short-term load forecasting |
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Authors: | Weilin Guo Liang Che Mohammad Shahidehpour Xin Wan |
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Affiliation: | 1. College of Electrical and Information Engineering, Hunan University, Changsha, Hunan 410082, China;2. Robert W. Galvin Center for Electricity Innovation, Illinois Institute of Technology, Chicago, IL 60616, USA;3. Dadu River Hydro Power Development Company of China Guodian Corporation, Chengdu, Sichuan 610041, China |
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Abstract: | Short-term load forecasting is of great significance to the secure and efficient operation of power systems. However, loads can be affected by a variety of external impact factors and thus involve high levels of uncertainties. So it is a challenging task to achieve an accurate load forecast. This paper discusses three commonly-used machine-learning methods used for load forecasting, i.e., the support vector machine method, the random forest regression method, and the long short-term memory neural network method. The features and applications of these methods are analyzed and compared. By integrating the advantages of these methods, a fusion forecasting approach and a data preprocessing technique are proposed for improving the forecasting accuracy. A comparative study based on real load data is performed to verify that the proposed approach is capable of achieving a relatively higher forecasting accuracy. |
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Keywords: | Short-term load forecasting Machine learning Support vector machine Long short-term memory |
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