energy demand forecasting
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2022 ◽  
Vol 31 (1) ◽  
pp. 539-553
Author(s):  
Taher M. Ghazal ◽  
Sajida Noreen ◽  
Raed A. Said ◽  
Muhammad Adnan Khan ◽  
Shahan Yamin Siddiqui ◽  
...  


2021 ◽  
Vol 68 (12) ◽  
pp. 881-894
Author(s):  
S. P. Filippov ◽  
V. A. Malakhov ◽  
F. V. Veselov


2021 ◽  
Vol 299 ◽  
pp. 117321
Author(s):  
Jieyang Peng ◽  
Andreas Kimmig ◽  
Zhibin Niu ◽  
Jiahai Wang ◽  
Xiufeng Liu ◽  
...  




Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3204
Author(s):  
Michał Sabat ◽  
Dariusz Baczyński

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%.



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