<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml"><p>The paper presents an improved method for 1-24 hours load forecasting in the power system, integrating and combining different neural forecasting results by an ensemble system. We will integrate the results of partial predictions made by three solutions, out of which one relies on a multilayer perceptron and two others on self-organizing networks of the competitive type. As the expert system we will apply different integration methods: simple averaging, SVD based weighted averaging, principal component analysis and blind source separation. The results of numerical experiments, concerning forecasting the hourly load for the next 24 hours of the Polish power system, will be presented and discussed. We will compare the performance of different ensemble methods on the basis of the mean absolute percentage error, mean squared error and maximum percentage error. They show a significant improvement of the proposed ensemble method in comparison to the individual results of prediction. The comparison of our work with the results of other papers for the same data proves the superiority of our approach.</p></abstract>

The paper presents an improved method for 1-24 hours load forecasting in the power system, integrating and combining different neural forecasting results by an ensemble system. We will integrate the results of partial predictions made by three solutions, out of which one relies on a multilayer perceptron and two others on self-organizing networks of the competitive type. As the expert system we will apply different integration methods: simple averaging, SVD based weighted averaging, principal component analysis and blind source separation. The results of numerical experiments, concerning forecasting the hourly load for the next 24 hours of the Polish power system, will be presented and discussed. We will compare the performance of different ensemble methods on the basis of the mean absolute percentage error, mean squared error and maximum percentage error. They show a significant improvement of the proposed ensemble method in comparison to the individual results of prediction. The comparison of our work with the results of other papers for the same data proves the superiority of our approach.

Ensemble Neural Network Approach for Accurate Load Forecasting in a Power System

Institute of the Theory of Electrical Engineering, Measurements and Information Systems, Warsaw University of Technology, pl. Politechniki 1, 00-661 Warsaw, Poland

Institute of Electronic Systems, Military University of Technology, ul. Kaliskiego 21, 00-908 Warsaw, Poland

Department of Business Informatics, Higher School of Economics, Al. Niepodległości 123, 02-554 Warsaw, Poland

International Journal of Applied Mathematics and Computer Science

{"article-title":"Ensemble Neural Network Approach for Accurate Load Forecasting in a Power System"}

The paper presents an improved method for 1-24 hours load forecasting in the power system, integrating and combining different neural forecasting results by an...

Ensemble Neural Network Approach for Accurate Load Forecasting in a Power System

Publié en ligne: 08 juil. 2009

Pages: 303 - 315

DOI: https://doi.org/10.2478/v10006-009-0026-2

Mots clés
neural networks, blind source separation, ensemble of predictors, load forecasting

This content is open access.

Ensemble Neural Network Approach for Accurate Load Forecasting in a Power System

Publié en ligne: 08 juil. 2009

Pages: 303 - 315

DOI: https://doi.org/10.2478/v10006-009-0026-2

Mots clésneural networks, blind source separation, ensemble of predictors, load forecasting

This content is open access.

Mots clés
neural networks, blind source separation, ensemble of predictors, load forecasting