Abstract
Medium-term electric energy demand forecasting is coming a key tool for energy management, power system operation and maintenance scheduling. This paper offers a solution to forecasting monthly electricity demand based on multilayer perceptron model which approximates a relationship between historical and future demand patterns. Energy demand time series exhibit non-stationarity, long-run trend, cycles of seasonal fluctuations and random noise. To simplify the forecasting problem the monthly demand time series is represented by patterns of yearly periods, which filter out a trend and unify data. An output variable is encoded using coding variables describing the process. The coding variables are determined on historical data or predicted using ARIMA and exponential smoothing. As an illustration, the proposed neural network model is applied to monthly energy demand forecasting for four European countries. The results confirm high accuracy of the model and its competitiveness compared to other models such as ARIMA, exponential smoothing, kernel regression and neuro-fuzzy system.
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References
Ghiassi, M., Zimbra, D.K., Saidane, H.: Medium term system load forecasting with a dynamic artificial neural network model. Electr. Power Syst. Res. 76, 302–316 (2006)
Gavrilas, M., Ciutea, I., Tanasa, C.: Medium-term load forecasting with artificial neural network models. In: IEEE Conference on Electricity Distribution (IEE Conf. Publ No. 482) (2001)
González-Romera, E., Jaramillo-Morán, M.A., Carmona-Fernández, D.: Monthly electric energy demand forecasting with neural networks and Fourier series. Energy Convers. Manage. 49, 3135–3142 (2008)
González-Romera, E., Jaramillo-Morán, M.A., Carmona-Fernández, D.: Monthly electric energy demand forecasting based on trend extraction. IEEE Trans. Power Syst. 21(4), 1935–46 (2006)
Doveh, E., Feigin, P., Hyams, L.: Experience with FNN models for medium term power demand predictions. IEEE Trans. Power Syst. 14(2), 538–546 (1999)
Pei-Chann, C., Chin-Yuan, F., Jyun-Jie, L.: Monthly electricity demand forecasting based on a weighted evolving fuzzy neural network approach. Electr. Power Energy Syst. 33, 17–27 (2011)
Chen, J.F., Lo, S.K., Do, Q.H.: Forecasting monthly electricity demands: an application of neural networks trained by heuristic algorithms. Information 8(1), 31 (2017)
Aquinode, R.R.B., et al.: Development of an artificial neural network by genetic algorithm to mid-term load forecasting. In: Proceedings of International Joint Conference on Neural Networks, pp. 1726–1731 (2007)
Pełka, P., Dudek, G.: Neuro-fuzzy system for medium-term electric energy demand forecasting. In: Borzemski, L., Świątek, J., Wilimowska, Z. (eds.) ISAT 2017. AISC, vol. 655, pp. 38–47. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-67220-5_4
Dudek, G., Pełka, P.: Medium-term electric energy demand forecasting using Nadaraya-Watson estimator. In: IEEE 18th International Conference on Electric Power Engineering, pp. 1–6 (2017)
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Pełka, P., Dudek, G. (2019). Pattern-Based Forecasting Monthly Electricity Demand Using Multilayer Perceptron. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2019. Lecture Notes in Computer Science(), vol 11508. Springer, Cham. https://doi.org/10.1007/978-3-030-20912-4_60
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DOI: https://doi.org/10.1007/978-3-030-20912-4_60
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