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Short-term power load forecasting method combining with multi-algorithm & multi-model and online second learning
ZHOU Mo, JIN Min
Journal of Computer Applications    2017, 37 (11): 3317-3322.   DOI: 10.11772/j.issn.1001-9081.2017.11.3317
Abstract610)      PDF (1027KB)(608)       Save
In order to improve the forecasting accuracy of the short-term power load, a forecasting method combining multi-algorithm & multi-model and online second learning was newly proposed. First, the input variables were selected by using mutual information and statistical information and a dataset was constructed. Then, multiple training sets were generated by performing diverse sampling with bootstrap on the original training set. Multiple models were obtained using different artificial intelligence and machine-learning algorithms. Finally, the offline second-learning method was improved. A new training set was generated using the actual load, and the multi-model forecasts for recent period within the forecasted time, which is trained by online second learning to obtain the final forecasting results. The load in Guangzhou, China was studied. Compared to the optimal single-model, single-algorithm & multi-model and multi-algorithm & single-model, Mean Absolute Percentage Error (MAPE) of the proposed model was reduced by 21.07%, 7.64% and 5.00%, respectively, in the daily total load forecasting, and by 16.02%, 7.60%, and 13.14%, respectively, in the daily peak load forecasting. The experimental results show that the proposed method can improve the prediction accuracy of the power load, reduce costs, implement optimal scheduling management, and ensure security with early warnings in smart grids.
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