Abstract
For the characteristics of short-term load forecasting, we established load forecasting model based on BP neural network, combined the advantages of gray prediction and Markov forecasting, and make an amendment for the prediction residual, this has greatly improved the precision of prediction. Research has shown that neural network and gray - Markov residual error correction model has the value of popularization and application.
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Niu, D., Xu, C., Li, J., Wei, Y. (2010). Application of Short-Term Load Forecasting Based on Improved Gray-Markov Residuals Amending of BP Neural Network. In: Tan, Y., Shi, Y., Tan, K.C. (eds) Advances in Swarm Intelligence. ICSI 2010. Lecture Notes in Computer Science, vol 6146. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13498-2_73
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DOI: https://doi.org/10.1007/978-3-642-13498-2_73
Publisher Name: Springer, Berlin, Heidelberg
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