THERMAL SCIENCE

International Scientific Journal

Slobodan A. Ilić

,

Srđan Vukmirović

,

Aleksandar M. Erdeljan

,

Filip J. Kulić

HYBRID ARTIFICIAL NEURAL NETWORK SYSTEM FOR SHORT-TERM LOAD FORECASTING

ABSTRACT
This paper presents a novel hybrid method for Short-Term Load Forecasting (STLF). The system comprises of two Artificial Neural Networks (ANN), assembled in a hierarchical order. The first ANN is a Multilayer Perceptron (MLP) which functions as integrated load predictor (ILP) for the forecasting day. The output of the ILP is then fed to another, more complex MLP, which acts as an hourly load predictor (HLP) for a forecasting day. By using a separate ANN that predicts the integral of the load (ILP), additional information is presented to the actual forecasting ANN (HLP), while keeping its input space relatively small. This property enables online training and adaptation, as new data become available, because of the short training time. Different sizes of training sets have been tested, and the optimum of 30 day sliding time-window has been determined. The system has been verified on recorded data from Serbian electrical utility company. The results demonstrate better efficiency of the proposed method in comparison to non-hybrid methods because it produces better forecasts and yields smaller mean average percentage error (MAPE).
KEYWORDS
STLF, MLP, prediction model, hybrid neural network structure
PAPER SUBMITTED: 2012-01-30
PAPER REVISED: 2012-03-12
PAPER ACCEPTED: 2012-03-22
DOI REFERENCE: https://doi.org/10.2298/TSCI120130073I
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2012, VOLUME 16, ISSUE Supplement 1, PAGES [S215 - S224]
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Hybrid artificial neural network system for short-term load forecasting

© 2024 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence