An efficient approach for short term load forecasting using artificial neural networks

• Published in: International journal of electrical power & energy systems Vol. 28; no. 8; pp. 525 - 530
• Main Authors: Kandil, Nahi, Wamkeue, René, Saad, Maarouf, Georges, Semaan
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.10.2006; Elsevier
• Subjects: Applied sciences; Artificial neural networks; Electrical engineering. Electrical power engineering; Electrical power engineering; Exact sciences and technology; Load forecasting; Miscellaneous; Operation. Load control. Reliability; Power networks and lines; Power systems; Canada; North America; America; Quebec; Load forecasting; Artificial neural networks; Power systems; Short term; Error estimation; Electrical network; Demand forecasting; Database; Wind velocity; Load management; Neural network; Forecasting management
• ISSN: 0142-0615; 1879-3517
• DOI: 10.1016/j.ijepes.2006.02.014

In previous work, we applied artificial neural networks (ANN) for short term load forecasting using real load and weather data from the Hydro-Quebec databases where three types of variables were used as inputs to the neural network: (a) hour and day indicators, (b) weather related inputs and (c) historical loads. In general, for forecasting with a lead time of up to a few days ahead, load history (for the last few days) is not available, and therefore, estimated values of this load are used instead. However, a small error in these estimated values may grow up dramatically and lead to a serious problem in load forecasting since this error is fed back as an input to the forecasting procedure. In this paper, we demonstrate ANN capabilities in load forecasting without the use of load history as an input. In addition, only temperature (from weather variables) is used, in this application, where results show that other variables like sky condition (cloud cover) and wind velocity have no serious effect and may not be considered in the load forecasting procedure.