scholarly journals Macromodelling as an Approach to Short-Term Load Forecasting of Electric Power System Objects

2017 ◽  
Vol 7 (1) ◽  
pp. 25-32
Author(s):  
Oksana Hoholyuk ◽  
◽  
Yuriy Kozak ◽  
Taras Nakonechnyy ◽  
Petro Stakhiv ◽  
...  
Keyword(s):  
Power System ◽  
Electric Power ◽  
Load Forecasting ◽  
Electric Power System ◽  
Short Term ◽  
Short Term Load Forecasting

Short-Term Load Forecasting for Electric Power System Based on DB Wavelet and Regression BP Neural Networks

2013 ◽  
Vol 380-384 ◽  
pp. 3018-3021
Author(s):  
Kun Zhang ◽  
Yan Hui Wang
Keyword(s):  
Neural Networks ◽  
Electric Power ◽  
Dynamic Balance ◽  
Load Forecasting ◽  
Electric Power System ◽  
Wavelet Coefficients ◽  
Bp Neural Networks ◽  
Short Term ◽  
Power Load ◽  
Short Term Load Forecasting

In order to ensure the dynamic balance of power load and improve the accuracy of short-term load forecasting, this paper presents a method of short-term load forecasting for electric power based on DB wavelet and regression BP neural networks. In this method, we get the wavelet coefficients at different scales through series decomposing of wavelet decomposition to load sample, and each scale wavelet coefficients for threshold selection, and then trained adjusted wavelet coefficients by regression BP neural networks, reconstructed load sequence predicted date through inverse wavelet transform. Finally, the accuracy of this method is significantly higher than BP neural network by examples verification.

scholarly journals Short-Term Load Forecasting for Spanish Insular Electric Systems

Energies ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3645 ◽  
Author(s):  
Eduardo Caro ◽  
Jesús Juan
Keyword(s):  
Electric Power ◽  
Load Forecasting ◽  
Electric Power System ◽  
Balearic Islands ◽  
Energy Resources ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Short Term Load Forecasting ◽  
System Operator ◽  
Optimal Coordination

In any electric power system, the Transmission System Operator (TSO) requires the use of short-term load forecasting algorithms. These predictions are essential for appropriate planning of the energy resources and optimal coordination for the generation agents. This study focuses on the development of a prediction model to be applied to the ten main Spanish islands: seven insular systems in the Canary Islands, and three systems in the Balearic Islands. An exhaustive analysis is presented concerning both the estimation results and the forecasting accuracy, benchmarked against an alternative prediction software and a set of modified models. The developed models are currently being used by the Spanish TSO (Red Eléctrica de España, REE) to make hourly one-day-ahead forecasts of the electricity demand of insular systems.

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