Monthly Electric Energy Demand Forecasting Based on Trend Extraction

2006 ◽  
Vol 21 (4) ◽  
pp. 1946-1953 ◽  
Author(s):  
E. Gonzalez-Romera ◽  
M.A. Jaramillo-Moran ◽  
D. Carmona-Fernandez
Keyword(s):  
Energy Demand ◽  
Demand Forecasting ◽  
Electric Energy ◽  
Trend Extraction ◽  
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%.

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

Profiling of Prosumers for the Needs of Electric Energy Demand Estimation in Microgrids

2015 ◽  
Vol 4 (4) ◽  
pp. 29-45 ◽  
Author(s):  
Karol Fabisz ◽  
Agata Filipowska ◽  
Tymoteusz Hossa
Keyword(s):  
Energy Consumption ◽  
Smart Grids ◽  
Energy Demand ◽  
Additional Data ◽  
Demand Forecasting ◽  
Electric Energy ◽  
Demand Estimation ◽  
Ex Post ◽  
Or Groups ◽  
Energy Demand Forecasting

Nowadays, a lot of attention regarding smart grids' development is devoted to delivery of methods for estimation of the energy demand taking into account the behavior of network participants (being single prosumers or groups of prosumers). These methods take an advantage from an analysis of the ex-post data on energy consumption, usually with no additional data about profiles of prosumers. The goal of this paper is to present and validate a method for an energy demand forecasting based on profiling of prosumers that enables estimation of the energy demand for every user stereotype, every hour, every day of the year and even for every device. The paper presents possible scenarios on how the proposed approach can be used for the benefit of the microgrid.

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