Abstract for Research Paper 2006-07

AEPCO and the University of Arizona’s Department of Agriculture and Resource Economics collaborated during the fall semester 2005 on a project to improve forecasts of next-day electricity load. The project was conducted as part of an AREC M.S. class in applied econometrics. Students developed ARIMA models for forecasting hourly loads for next for five distinct seasons in 2004: winter, spring, pre-monsoon, monsoon, and fall periods. ARIMA forecast errors at coincidental peaks were generally at or below 5 percent. The time of day of coincidental peaks was usually forecast correctly. ARIMA forecast also captured the shape of 24-hour profiles adequately.


	Forecasting Short-Term Electricity Load Profiles Clifford A. Cathers and Gary D. Thompson
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	Abstract
	AEPCO and the University of Arizona’s Department of Agriculture and Resource Economics collaborated during the fall semester 2005 on a project to improve forecasts of next-day electricity load. The project was conducted as part of an AREC M.S. class in applied econometrics. Students developed ARIMA models for forecasting hourly loads for next for five distinct seasons in 2004: winter, spring, pre-monsoon, monsoon, and fall periods. ARIMA forecast errors at coincidental peaks were generally at or below 5 percent. The time of day of coincidental peaks was usually forecast correctly. ARIMA forecast also captured the shape of 24-hour profiles adequately.