Abstract
An artificial neural network was used for sample preprocessing in this paper. Firstly, the data points were classified into three types as follows: the high load type, the medium load type and the low load type. Then, the artificial neural network was adopted to forecast the load type of the predict point. Finally, a support vector machine forecasting model was created on the basis of data points whose load type is the same as the predict point. Comparisons were made between different methods. The results show that the model established in this paper is better than other methods in forecasting accuracy and computing speed.
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Niu, DX., Wang, Q., Li, JC. (2006). Short Term Load Forecasting Model Based on Support Vector Machine. In: Yeung, D.S., Liu, ZQ., Wang, XZ., Yan, H. (eds) Advances in Machine Learning and Cybernetics. Lecture Notes in Computer Science(), vol 3930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11739685_92
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DOI: https://doi.org/10.1007/11739685_92
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33584-9
Online ISBN: 978-3-540-33585-6
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