Performance Comparison of Short Term Load Forecasting Techniques

Prasad, T. Nageswara

Abstract

Load forecasting plays a major role in planning and operation of a power system. Many techniques are available in the literature among these neural networks, linear multiple regression, regression trees, curve fitting and averaging models are the most popular because these models gives accurate solutions with very less tolerable Least Mean Absolute Percent Error(MAPE). In this paper a comparative study was made between these forecasting models and it was found that when compared to the four independent models, the averaging model i.e. combination of Curve Fitting, Regression Trees Neural Network gives less MAPE. MATLAB programming results validates that averaging model gives better performance than individual models.

Más información

Título según WOS: ID WOS:000376347400026 Not found in local WOS DB
Título de la Revista: INTERNATIONAL JOURNAL OF GRID AND DISTRIBUTED COMPUTING
Volumen: 9
Número: 4
Editorial: NADIA
Fecha de publicación: 2016
Página de inicio: 287
Página final: 301
DOI:

10.14257/ijgdc.2016.9.4.26

Notas: ISI