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• W2736193136 abstract "In short-term load forecasting, dataset construction plays a vital role in the improvement of forecasting accuracy. This paper begins with filtering the load data of the target city, transferring and expanding the load data of the nearby cities, and analysing the influence of the periodicity of load, holiday and load growth rate on dataset construction. A dataset construction method combining compress-filtering and transfer-expanding is then proposed based on the analysis. In this method, firstly, we apply the mean compress method to compress monthly data into weekly data in which both the periodic trend and the randomness of other related factors are taken into consideration. The training set is selected according to the similarity between the load variation pattern of the predicted month and that of the historical data. Secondly, based on the analysis of the similarity of the load data between the target city and the nearby cities, we introduce the load growth rate to measure the difference of the load variation patterns between different cities. Based on the load growth rate, a transfer learning method is put forward which transfers the data of the source city to the target city. The case study on real load data shows that, compared with the mutual information filtering-based predicting method and the knowledge transfer expanding method, the mean absolute percent error is decreased by 26% and 9.5%, respectively." @default.
• W2736193136 created "2017-07-21" @default.
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• W2736193136 date "2017-05-01" @default.
• W2736193136 modified "2023-09-23" @default.
• W2736193136 title "Compress-filtering and transfer-expanding of data set for short-term load forecasting" @default.
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• W2736193136 doi "https://doi.org/10.1109/ijcnn.2017.7965974" @default.
• W2736193136 hasPublicationYear "2017" @default.
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