Modelling Energy Demand Forecasting Using Neural Networks with Univariate Time Series

2018 ◽  
Author(s):  
S. Cankurt ◽  
M. Yasin
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Energy Demand ◽  
Demand Forecasting ◽  
Univariate Time Series ◽  
Energy Demand Forecasting

Energy demand forecasting of buildings using random neural networks

2020 ◽  
Vol 38 (4) ◽  
pp. 4753-4765
Author(s):  
Jawad Ahmad ◽  
Ahsen Tahir ◽  
Hadi Larijani ◽  
Fawad Ahmed ◽  
Syed Aziz Shah ◽  
...  
Keyword(s):  
Neural Networks ◽  
Energy Demand ◽  
Demand Forecasting ◽  
Energy Demand Forecasting ◽  
Random Neural Networks

Monthly electric energy demand forecasting with neural networks and Fourier series

2008 ◽  
Vol 49 (11) ◽  
pp. 3135-3142 ◽  
Author(s):  
E. González-Romera ◽  
M.A. Jaramillo-Morán ◽  
D. Carmona-Fernández
Keyword(s):  
Neural Networks ◽  
Fourier Series ◽  
Energy Demand ◽  
Demand Forecasting ◽  
Electric Energy ◽  
Energy Demand Forecasting

scholarly journals Urban integrated energy demand forecasting method considering multi-station fusion

2021 ◽  
Vol 651 (2) ◽  
pp. 022084
Author(s):  
Haoyu Wu ◽  
Jiaxin Ma ◽  
Chunyan Zhang ◽  
Hua Zhou ◽  
Shimin Bian ◽  
...  
Keyword(s):  
Energy Demand ◽  
Demand Forecasting ◽  
Forecasting Method ◽  
Energy Demand Forecasting

scholarly journals Usage of the Pareto Fronts as a Tool to Select Data in the Forecasting Process—A Short-Term Electric Energy Demand Forecasting Case

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3204
Author(s):  
Michał Sabat ◽  
Dariusz Baczyński
Keyword(s):  
Energy Demand ◽  
Demand Forecasting ◽  
Electric Energy ◽  
Forecasting Model ◽  
Short Term ◽  
Complex Evaluation ◽  
Proposed Model ◽  
Energy Demand Forecasting ◽  
Pareto Fronts ◽  
Naive Approach

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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