Abstract
Power demand prediction is important for the economically efficient operation and effective control of power systems and enables to plan the load of generating unit. A precise load forecasting is required to avoid high generation cost and the spinning reserve capacity. Under-prediction of the demands leads to an insufficient reserve capacity preparation and can threaten the system stability, on the other hand, over-prediction leads to an unnecessarily large reserve that leads to a high cost preparations. Cooperation on the electricity grid requires from all providers to foresee the load within a sufficient accuracy. Differential polynomial neural network is a new neural network type, which forms and resolves an unknown general partial differential equation of an approximation of a searched function, described by data observations. It generates convergent sum series of relative polynomial derivative terms, which can substitute for the ordinary differential equation, describing 1-parametric function time-series with partial derivatives. A new method of the short-term power demand prediction, based on similarity relations of subsequent day progress cycles is presented and tested using the combined differential polynomial network.
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Zjavka, L. (2014). Power Demand Daily Predictions Using the Combined Differential Polynomial Network. In: Kömer, P., Abraham, A., Snášel, V. (eds) Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014. Advances in Intelligent Systems and Computing, vol 303. Springer, Cham. https://doi.org/10.1007/978-3-319-08156-4_8
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DOI: https://doi.org/10.1007/978-3-319-08156-4_8
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08155-7
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