Title :
Use of ANNs for short-term load forecasting
Author :
Kandil, Nahi ; Sood, Vijay ; Saad, Maarouf
Author_Institution :
Concordia Univ., Montreal, Que., Canada
Abstract :
The application of artificial neural networks (ANNs) to short term load forecasting has gained a lot of attention. The availability of historical load data on the utility databases makes this area highly suitable for ANN implementation, ANNs are able to learn the relationship among past, current, and future weather variables and loads combining both time series and regressional approaches. A wide variety of different types of ANNs have been used for load forecasting in the last few years resulting in a noticeable number of publications on the subject. In this paper, a MLP type of artificial neural network is used for short-term load forecasting using real load and weather data from the Hydro-Quebec databases.
Keywords :
load forecasting; multilayer perceptrons; power system analysis computing; statistical analysis; time series; ANN; Hydro-Quebec databases; artificial neural networks; historical load data; multilayer perceptron; regressional approach; scheduling; short-term load forecasting; time series; utility databases; weather variables; Artificial neural networks; Casting; Clouds; Economic forecasting; Load forecasting; Power system reliability; Power system security; Predictive models; Weather forecasting; Wind;
Conference_Titel :
Electrical and Computer Engineering, 1999 IEEE Canadian Conference on
Conference_Location :
Edmonton, Alberta, Canada
Print_ISBN :
0-7803-5579-2
DOI :
10.1109/CCECE.1999.808193