Abstract
The present study proposes a short-term load forecasting method based on weighted grey relational degree and improved support vector machines with the artificial bee colony algorithm (ABC-SVM). The entropy weight method was employed to obtain the weight of load-related physical information, and the historical and forecast load data selected based on the weighted grey relational degree were input into the support vector machine (SVM) to build a forecasting model. Meanwhile, the SVM parameters were optimized by the improved artificial bee colony algorithm before the model was used to perform load forecasting. The experimental results show that the proposed method could effectively improve the accuracy of the forecasting model and simplify the calculation, thus having research and practical value.
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Funding
This research was supported by the 2018 Key Projects of Guangdong Ocean University Cunjin College (No. CJXK201801) and the 2018 Project of Industry-University Cooperation of Education Department (No. 201802296001).
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Ruxue, L., Shumin, L., Miaona, Y. et al. Load Forecasting Based on Weighted Grey Relational Degree and Improved ABC-SVM. J. Electr. Eng. Technol. 16, 2191–2200 (2021). https://doi.org/10.1007/s42835-021-00727-3
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DOI: https://doi.org/10.1007/s42835-021-00727-3