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A learning framework based on weighted knowledge transfer for holiday load forecasting
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DOI：10.1007/s40565-018-0435-z.png)
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Abstract
En 中文
Since the variation pattern of load during holidays is different than that of non-holidays, forecasting holiday load is a challenging task. With a focus on this problem, we propose a learning framework based on weighted knowledge transfer for daily peak load forecasting during holidays. First, we select source cities which can provide extra hidden knowledge to improve the forecast accuracy of the load of the target city. Then, all the instances which are from source cities and the target city will be weighted and trained by the improved weighted transfer learning algorithm which is based on the TrAdaBoost algorithm and can decrease negative transfer. We evaluate our method with the classical support vector machine method and a method based on knowledge transfer on a real data set, which includes eleven cities from Guangdong province to illustrate the performance of the method. To solve the problem of limited historical holiday load data, we transfer the data from nearby cities based on the fact that nearby cities in Guangdong province have a similar economic development level and similar load variation pattern. The results of comparative experiments show that the forecasting framework proposed by this paper outperforms these methods in terms of mean absolute percent error and mean absolute scaled error.
Keywords:
Load forecasting
Holiday effect
Sparse data
Weighted transfer learning
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