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Short Term Hourly Load Forecasting Using Abductive Networks R. E. Abdel-Aal

Summary: Short Term Hourly Load Forecasting Using Abductive Networks
R. E. Abdel-Aal
Center for Applied Physical Sciences, Research Institute,
King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
Short-term load modeling and forecasting are essential for operating power utilities profitably
and securely. Modern machine learning approaches such as neural networks have been used for
this purpose. This paper proposes using the alternative technique of abductive networks, which
offers the advantages of simplified and more automated model synthesis and analytical input-
output models that automatically select influential inputs, provide better insight and
explanations, and allow comparison with statistical and empirical models. Using hourly
temperature and load data for five years, 24 dedicated models for forecasting next-day hourly
loads have been developed. Evaluated on data for the 6th
year, the models give an overall mean
absolute percentage error (MAPE) of 2.67%. Next-hour models utilizing load data up to the
preceding hour give a MAPE of 1.14%, outperforming neural network models for the same
utility data. Two methods are described for dealing with the load growth trend. Effects of
varying model complexity are investigated and proposals made for further improving
forecasting performance.
Index Terms: Abductive networks, Neural networks, Neural network applications, Load


Source: Abdel-Aal, Radwan E. - Computer Engineering Department, King Fahd University of Petroleum and Minerals


Collections: Computer Technologies and Information Sciences; Power Transmission, Distribution and Plants