Abstract
Voltage control of distribution systems need load forecast. For improvement of efficiency and sustainability of the automated control of Smart Power Grids (SPG) the control system of the process should contain a subsystem of forecasting time series - load forecasting, as a parameter directly associated with voltage. The highest requirements applicate for the accuracy of short-term (day-week-month) and operational (within the current day) forecasts, as they determine the management of the current mode of operation of the SPG. The paper describes forecasting methods and concludes that using of artificial neural networks for this problem is preferable. It shows that for the complex real networks particle swarm method is faster and more accurate than traditional back propagation method. Finally the used model of short-term load forecasting (STLF) is described and tuning of all presented models is done. The paper concentrates of training methods of neural networks and uses “back propagation” algorithm and “particle swarm” algorithm for this purpose.
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Lis, R., Vanin, A., Kotelnikova, A. (2018). Performance Comparison of Neural Network Training Algorithms for Load Forecasting in Smart Grids. In: Świątek, J., Borzemski, L., Wilimowska, Z. (eds) Information Systems Architecture and Technology: Proceedings of 38th International Conference on Information Systems Architecture and Technology – ISAT 2017. ISAT 2017. Advances in Intelligent Systems and Computing, vol 656. Springer, Cham. https://doi.org/10.1007/978-3-319-67229-8_9
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DOI: https://doi.org/10.1007/978-3-319-67229-8_9
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