Abstract
As an essential energy in the daily life, electricity which is difficult to store has become a hot issue in power system. Short-term electric load forecasting (STLF) which is regarded as a vital tool helps electric power companies make good decisions. It can not only guarantee adequate energy supply but also avoid unnecessary wastes. Although there exists quantity of forecasting methods, most of them are not able to make accurate predictions. Therefore, a forecasting method with high accuracy is particularly important. In this paper, a combined model based on neural networks and least squares support vector machine (LSSVM) is proposed to improve the forecasting accuracy. At first, three forecasting methods named generalized regression neural network (GRNN), Elman, LSSVM are utilized to forecast respectively. Among them, simulate anneal (SA) arithmetic is used to optimize GRNN. Then, SA is employed to determine the weight coefficients of each individual method. At last, multiplying all the three forecasting results with the corresponding weights, the final result of the combined model can be attained. Using the electric load data of Queensland of Australia as experimental simulation, case studies show that the proposed combined model works well for STLF and the results prove more accurate.
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Masa-Bote, D., Castillo-Cagigal, M., et al.: Improving photvoltaics grid integration through short time forecasting and self-consumption. Appl. Energy 125, 103–113 (2014)
Hong, W.C.: Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artificial bee colony algorithm. Energy 36, 5568–5578 (2011)
Li, S., Wang, P., Goel, L.: Short-term load forecasting by wavelet transform and evolutionary extreme learning machine. Electr. Power Syst Res. 122, 96–103 (2015)
Deihimi, A., Showkati, H.: Application of echo state networks in short-term electric load forecasting. Energy 39, 327–340 (2012)
Zhang, R., Dong, Z.Y., Xu, Y., Meng, K., Wong, K.P.: Short-term load forecasting of Australian national electricity market by an ensemble model of extreme learning machine. Gener. Transm. Distrib. IET 7, 391–397 (2013)
Kandil, N., Wamkeue, R., Saad, M., Georges, S.: An efficient approach for short term load forecasting using artificial neural networks. Int. J. Electr. Power Energy Syst. 28(8), 525–530 (2006)
Jin, M., Zhou, X., Zhang, Z.M., Tentzeris, M.M.: Short-term power load forecasting using grey correlation contest modeling. Expert Syst. Appl. 39, 773–779 (2012)
Moghram, I., Rahman, S.: Analysis and evaluation of five short-term load forecasting techniques. IEEE Trans. Power Syst. 4(4), 1484–1494 (1989)
Amina, M., Kodogiannis, V.S., Petrounias, I., Tomtsis, D.: A hybrid intelligent approach for the prediction of electricity consumption. Int. J. Electr. Power Energy Syst. 43(1), 99–108 (2012)
Xiao, L., Wang, J., Yang, X., Xiao, L.: A hybrid model based on data preprocessing for electrical power forecasting. Electr. Power Energy Syst. 64, 311–327 (2015)
Liu, N., Tang, Q., Zhang, J.H., Fan, W., Liu, J.: A hybrid forecasting model with parameter optimization for short-term load forecasting of micro-grid. Appl. Energy 129, 336–345 (2014)
Wang, J.Z., Zhu, S.L., Zhang, W.Y., Lu, H.Y.: Combined modeling for electric load forecasting with adaptive partical swarm optimization. Energy 35(4), 1671–1678 (2010)
Xiao, Y., Liu, J.J., et al.: A neuro-fuzzy combination model based on singular spectrum analysis for air transport demand forecasting. J. Air Transp. Manage. 39, 1–11 (2014)
Souptick, C., Sanjay, G., Dilip, K.P.: A combined neural network and genetic algorithm based approach for optimally designed femoral implant having improved primary stability. Appl. Soft Comput. 38, 296–307 (2016)
Xia, C.H., Lei, B.J., Wang, H.P., Li, J.N.: GRNN short-term load forecasting model and virtual instrument design. Energy Procedia 13, 9150–9158 (2011)
Chelgani, S.C., Jorjani, E.: Microwave irradiation pretreatment and peroxyacetic acid desulfurzation of coal and application of GRNN simultaneous predictor. Fuel 90(11), 3156–3163 (2011)
Li, H.Z., Guo, S., Li, C.J., Sun, J.Q.: A hybrid annual power load forecasting model based on generalized neural network with fruit fly optimization algorithm. Knowl. Based Syst. 37, 378–387 (2013)
Kelo, S., Dudul, S.: A wavelet Elman neural network for short-term load prediction under the influence of temperature. Electr. Power Energy Syst. 43, 1063–1071 (2012)
Song, Q.: On the weight convergence of Elman networks. IEEE Trans. Neural Netw. 21(3), 96–101 (2010)
Li, X., Chen, G., Chen, Z., Yuan, Z.: Chaotifying linear Elman networks. IEEE Trans. Neural Netw. 13(5), 1193–1199 (2002)
Shayeghi, H., Ghasemi, A.: Day-ahead electricity prices forecasting by a modified CGSA technique and hybrid WT in LSSVM based scheme. Energy Convers. Manage. 74, 482–491 (2013)
Zhang, M., Fu, L.: Unbiased least squares support vector machine with polynomial kernel. In: 8th IEEE International Conference on Signal Processing (ICSP-2006), vol. 3, Guilin, China, pp. 16–20 (2006)
Xie, L., Zheng, H., Zhang, L.Z.: Electricity price forecasting by clustering-LSSVM. In: Proceedings of the International Power, Engineering Conference, pp. 697–702 (2007)
Hong, W.C.: Traffic flow forecasting by seasonal SVR with chaotic simulated annealing algorithm. Neurocomputing 74, 2096–2107 (2011)
Lv, P., Yuan, L., Zhang, J.: Clound theory-based simulated annealing algorithm and application. Eng. Appl. Artif. Intell. 22, 742–749 (2009)
Pai, P.F., Hong, W.C.: Support vector machines with simulated annealing algorithms in electricity load forecasting. Energy Convers. Manage. 46(17), 2669–2688 (2005)
Chandra, R.: Competition and collaboration in cooperative coevolution of Elman recurrent neural networks for time-series prediction. IEEE Trans. Neural Netw. Learn. Syst. 26, 3123–3136 (2015)
Specht, D.F.: A general regression neural network. IEEE Trans. Neural Netw. 2(6), 568–576 (1991)
Suykens, J.A.K., Van Gestel, T., De Brabanter, J., et al.: Least Square Support Vector Machines. World Scientific, Singapore (2002)
An, N., Zhao, W., Wang, J., et al.: Using multi-output feedforward nerual network with empirical mode decomposition based signal filtering for electricity demand forecasting. Energy 49, 279–288 (2013)
Acknowledgments
The authors would like to thank the Natural Science Foundation of PR of China (61073193,61300230), the Key Science and Technology Foundation of Gansu Province (1102FKDA010), the Natural Science Foundation of Gansu Province (1107RJZA188), and the Science and Technology Support Program of Gansu Province (1104GKCA037) for supporting this research.
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Li, C., He, Z., Wang, Y. (2016). A Combined Model Based on Neural Networks, LSSVM and Weight Coefficients Optimization for Short-Term Electric Load Forecasting. In: Song, S., Tong, Y. (eds) Web-Age Information Management. WAIM 2016. Lecture Notes in Computer Science(), vol 9998. Springer, Cham. https://doi.org/10.1007/978-3-319-47121-1_10
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DOI: https://doi.org/10.1007/978-3-319-47121-1_10
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