Abstract
Transmission, distribution, and micro-grid system operators are struggling with the increasing number of renewables and the changing nature of energy demand. This necessitates the use of prognostic methods based on ever shorter time series. This study depicted an attempt to develop an appropriate method by introducing a novel forecasting model based on the idea to use the Pareto fronts as a tool to select data in the forecasting process. The proposed model was implemented to forecast short-term electric energy demand in Poland using historical hourly demand values from Polish TSO. The study rather intended on implementing the range of different approaches—scenarios of Pareto fronts usage than on a complex evaluation of the obtained results. However, performance of proposed models was compared with a few benchmark forecasting models, including naïve approach, SARIMAX, kNN, and regression. For two scenarios, it has outperformed all other models by minimum 7.7%.
Highlights
Transmission, distribution, and micro-grid system operators are struggling with the increasing number of renewables and the changing nature of energy demand
Performance of proposed models was compared with a few benchmark forecasting models, including naïve approach, SARIMAX, k nearest neighbors (kNN), and regression
In the electric energy sector, transmission system operators (TSOs), distribution system operators (DSOs), commercial operators (COs), or commercial and technical operators (CTOs) perform numerous different forecasts to plan their activities in an optimal way
Summary
Transmission, distribution, and micro-grid system operators are struggling with the increasing number of renewables and the changing nature of energy demand. This necessitates the use of prognostic methods based on ever shorter time series. In the electric energy sector, transmission system operators (TSOs), distribution system operators (DSOs), commercial operators (COs), or commercial and technical operators (CTOs) perform numerous different forecasts to plan their activities in an optimal way. The high accuracy forecasts enable, in the short-term, the TSOs to provide more secure power system operation, i.e., to balance demand with production and minimalize its costs. Forecasts’ accuracy will influence the possibility of green energy inclusion in energy mix and proper management of energy storage which can give benefits both for customer and energy provider [6]
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