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 Survey on short-term load forecasting using hybrid neural network techniques

2018 ◽  
Vol 7 (2.8) ◽  
pp. 464
Author(s):  
Shaive Dalela ◽  
Aditya Verma ◽  
A L.Amutha
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Load Forecasting ◽  
Theoretical Data ◽  
Electric Power System ◽  
Support Vector ◽  
Short Term ◽  
Load Demand ◽  
Short Term Load Forecasting ◽  
Artificial Neural

Load forecasting is an issue of great importance for the reliable operation of the electric power system grids. Various forecasting methodologies have been proposed in the international research bibliography, following different models and mathematical approaches. In the current work, several latest methodologies based on artificial neural networks along with other techniques have be discussed, in order to obtain short-term load forecasting. In this paper, approaches taken by different researchers considering different parameters in means of predicting the lease error has been shown.  The paper investigates the application of artificial neural networks (ANN) with fuzzy logic (FL), Genetic Algorithm(GA), Particle Swarm Optimization(PSO) and Support Vector Machines(SVM) as forecasting tools for predicting the load demand in short term category. The extracted outcomes indicate the effectiveness of the proposed method, reducing the relative error between real and theoretical data

Short-term electric power load forecasting using feedforward neural networks

2004 ◽  
Vol 21 (3) ◽  
pp. 157-167 ◽  
Author(s):  
Heidar A. Malki ◽  
Nicolaos B. Karayiannis ◽  
Mahesh Balasubramanian
Keyword(s):  
Neural Networks ◽  
Electric Power ◽  
Load Forecasting ◽  
Feedforward Neural Networks ◽  
Short Term ◽  
Power Load ◽  
Power Load Forecasting ◽  
Electric Power Load

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

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.

Quantitative Analysis of the Relationship between Temperature and Power Load

2014 ◽  
Vol 986-987 ◽  
pp. 428-432 ◽  
Author(s):  
Jian Liang Zhong ◽  
Bei Zhao ◽  
Da Zhang ◽  
Hai Bao
Keyword(s):  
Load Forecasting ◽  
Electric Power System ◽  
Effect Of Temperature ◽  
Series Data ◽  
Practical Implementation ◽  
Regression Equations ◽  
Short Term ◽  
Power Load ◽  
Short Term Load Forecasting ◽  
The Relationship

This paper presents the results of a study regarding the relationship between temperature and power load of the electric power system. Weather-influenced load part is picked up from original load series data with the conclusion that the lagged effect of temperature on load is within 12 hours. Furthermore, decision tree and step regression methods are employed to get a group of decision trees and corresponding regression equations which are able to quantitatively describe the relationship between load and temperature. A short-term load forecasting algorithm is then developed and its practical implementation shows this quatitative analysis method could reliably reflect the influence of the temperature changes on the load and effectively improve the accuracy of short-term load forecasting.

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