Citation: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 11, pages 307–315 (2017)
Abstract. Demand peaks in electrical power system cause serious challenges for energy providers as these events are typically difficult to foresee and require the grid to support extraordinary consumption levels. Accurate peak forecasting enables utility providers to plan the resources and also to take control actions to balance electricity supply and demand. However, this is difficult in practice as it requires precision in prediction of peaks in advance. In this paper, our contribution is the proposal of data mining scheme to detect the peak load in the electricity system at country level. For this purpose we undertake the approach different from time series forecasting and represent it as pattern recognition problem. We utilize set of artificial neural networks to benefit from accurate detection of the peaks in the Polish power system. The key finding is that the algorithms can accurately detect 96.2\% of the electricity peaks up to 24 hours ahead.
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