Conforming load and weather diversity for the analysis of a multi-region forecasting system

• Published in: 2012 25th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) pp. 1 - 4
• Main Authors: Wright, C., Chan, C. W., Laforge, P.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.04.2012
• Subjects: Aggregates; Artificial neural networks; Electric power systems; Forecasting; Load diversity; Load forecasting; Load modeling; Meteorology; Multi-region; Neural network; Predictive models
• ISBN: 1467314315; 9781467314312
• ISSN: 0840-7789
• DOI: 10.1109/CCECE.2012.6334904

Power systems in large geographic environments experience diverse weather phenomena. Due to spatial separation and economic diversification, load centres will exhibit differing electrical demand. This diversity of weather and electric loads proves challenging for load forecasters using a single aggregate model. In such systems, aggregate response of these load centres cannot be properly analyzed by a single load-weather model. Instead, the aggregate demand is best explained through multi-region modeling. This paper describes the load and weather diversity within the control area of an electric utility in the province of Saskatchewan. An existing aggregate similar day model is contrasted against Artificial Neural Network (ANN) models based on both an aggregate and a multi-region system. Results confirm the superior performance of the proposed multi-region load forecasting system as compared to the two aggregate load forecasting models.