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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References
Shyh, J.H., Kuang, R.S.: Short-term Load Forecasting Via ARMA Model Identification Including Non-Gaussian Process Considerations. IEEE Transactions on Power Systems 18, 673–679 (2003)
Yong, H.L., Pin, C.L.: Novel High-precision Grey Forecasting Model. Automation in Construction 16, 771–777 (2007)
Chorng, S.O., Jih, J.H., Gwo, H.T.: Model Identification of ARIMA Family Using Genetic Algorithms. Applied Mathematics and Computation 164, 885–912 (2005)
Senjyu, T., Andal, P., Uezato, K., Funabashi, T.: Next Day Load Curve Forecasting Using Recurrent Neural Network Structure. IEEE Proceedings Generation, Transmission and Distribution 151, 388–394 (2004)
Baczynski, D., Parol, M.: Influence of Artificial Neural Network Structure on Quality of Short-term Electric Energy Consumption Forecast. IEEE Proceedings Generation, Transmission and Distribution 151, 241–245 (2004)
Wang, N., Zhang, W.X.: A Restricted Least Squares Estimation for Fuzzy Linear Regression Models. Fuzzy Systems and Mathematics 20, 17–124 (2006)
Song, K.B., Baek, Y.S., Hong, D.H., Jan, G.: Short-Term Load Forecasting for the Holidays Using Fuzzy Linear Regression Method. IEEE transactions on power systems 20, 96–101 (2005)
Saksornchai, T., Lee, W.J., Methaprayoon, K.: Improve the Unit Commitment Scheduling by Using the Neural-Network-Based Short-Term Load Forecasting. IEEE Transactions on Industry Applications 41, 169–179 (2005)
Abdel, A.R.E.: Short-term Hourly Load Forecasting Using Abductive Networks. IEEE Transactions on Power Systems 19, 164–173 (2004)
Ming, M., Lu, J.C., Sun, W.: Short-Term Load Forecasting Based on Ant Colony Clustering and Improved BP Neural Networks. In: 2006 International Conference on Machine Learning and Cybernetics, vol. 2, pp. 3012–3015 (2006)
Naresh, R., Dubey, J., Sharma, J.: Two-phase Neural Network Based Modelling Framework of Constrained economic load dispatch. IEEE Proceedings Generation, Transmission and Distribution 151, 373–378 (2004)
Yu, S.W., Zhu, K.J., Diao, F.Q.: A Dynamic All Parameters Adaptive BP Neural Networks Model and Its Application on Oil Reservoir Prediction. Applied Mathematics and Computation 195, 66–75 (2008)
Ivan, N.D.S., Rogerio, A.F.: An Approach Based on Neural Networks for Estimation and Generalization of Crossflow Filtration Processes. Applied Soft Computing 8, 590–598 (2008)
Al-Hamadi, H.M., Soliman, S.A.: Fuzzy Short-term Electric Load Forecasting Using Kalman Filter. IEEE Proc. Gener. Transm. Distrib. 153, 217–227 (2006)
Niu, D.X., Chen, Z.Y., Xing, M., Xie, H.: Combined Optimum Gray Neural Network Model of The Seasonal Power Load Forecasting With the Double Trends. Proceeding of the CSEE 22, 29–32 (2002)
Paw, L.Z.: Rough sets. International Journal of Computer InformationScience 5, 341–356 (1982)
Stephen, A.B., Wei, H.L., Michael, A.B.: Generalized Multiscale Radial Basis Function Networks. Neural Networks 20, 1081–1094 (2007)
Chen, H.J., Du, Y.J., Jiang, J.N.: Weather Sensitive Short-Term Load Forecasting Using Knowledge-Based ARX Models. IEEE Power Engineering Society General Meeting 1, 1190–1196 (2005)
Kuan, J., Lewis, P.: Fast k nearest neighbour search for R-tree family. In: International Conference on Information Communications and Signal Processing ICICS 1997, vol. 2, pp. 924–928 (1997)
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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
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