Letters: Energy demand prediction using GMDH networks

Authors:
Dipti Srinivasan
Affiliations:
Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117576, Singapore
Venue:
Neurocomputing
Year:
2008

Citing 3
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Abstract

The electric power industry is in transition as it moves towards a competitive and deregulated environment. In this emerging market, traditional electric utilities as well as energy traders, power pools and independent system operators (ISOs) need the capability to predict as precisely as possible how much energy their customers will use in the near future. This paper presents a medium-term energy demand forecasting system that helps utilities identify and forecast energy demand for each of the end-use consumption sector of the energy system, representing residential, industrial, commercial, non-industrial, entertainment and public lighting load. The demand forecasting system is organized and implemented in a modular fashion using high accuracy forecast models. These models are developed for each sector to account for the growth trends and seasonal effects. A comparative evaluation of various traditional and neural network-based methods for obtaining the forecast of monthly energy demand was carried out. Among the models tested, artificial neural network (ANN)-based models were determined to produce better results. In particular, group method of data handling (GMDH) neural network, composed of self-organizing active neurons, was proven very effective in producing forecasts that were significantly more accurate and less labor-intensive than traditional time-series and regression-based models.