Load forecasting for remote area power supply systems

• Published in: Proceedings the 11th Conference on Artificial Intelligence for Applications pp. 231 - 237
• Main Authors: Cheok, K., Kottathra, K., Pryor, T.L., Cole, G.R.
• Format: Conference Proceeding
• Language: English
• Published: IEEE Comput. Soc. Press 1995
• Subjects: Artificial neural networks; Australia; Backpropagation; Digital filters; Error analysis; Frequency; Load forecasting; Passband; Power supplies; White noise
• ISBN: 0818670703; 9780818670701
• DOI: 10.1109/CAIA.1995.378818

An artificial neural net approach is applied to short-term load forecasting in three remote area power supply systems (RAPS) in Western Australia. Such systems are usually in the kW range and are characterised by very irregular load profiles which make prediction difficult. The data used was collected over a year in W.A. and evaluated for each season. The feedforward backpropagation network outperformed the statistical techniques and mean absolute percentage errors between 3.9% and 13.5% were obtained. Digital filters were used to decompose the load into low and high frequency passbands in a hypothetical case where known data is used to determine the learning abilities of the ANN. An upper limit on the accuracies of between 3.2% and 9.8% was achieved in this case. However an error analysis of the residuals shows that these have not yet been reduced to white noise indicating that further improvements are still possible.< >