Abstract
Artificial neural network (ANN) is used in load forecasting widely. However, there are still some difficulties in choosing the input variables and selecting one appropriate architecture of the neural networks. According to the characteristics of electric short-term load forecasting, presents on a BPANN basing on rough set. Rough set theory is first used to perform input attributes selection. The initial decision table involves factors of weather and date which are able to affect load curve. Then K-Nearest Neighbor method is taken into selecting of most similar data to the target day as the training set of BPANN. Reduced input data of BPANN can avoid over-training and improved performance of BPANN and decreases times of training. The forecasting practice in Baoding Electric Power Company shows that the proposed model is feasible and has a good forecasting precision.
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Li, CX., Niu, DX., Meng, LM. (2008). Rough Set Combine BP Neural Network in Next Day Load Curve Forcasting. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds) Advances in Neural Networks - ISNN 2008. ISNN 2008. Lecture Notes in Computer Science, vol 5264. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87734-9_1
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DOI: https://doi.org/10.1007/978-3-540-87734-9_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87733-2
Online ISBN: 978-3-540-87734-9
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