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Electricity load forecasting based on autocorrelation analysis

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3 Author(s)
Sood, R. ; Sch. of Electr. & Inf. Eng., Univ. of Sydney, Sydney, NSW, Australia ; Koprinska, I. ; Agelidis, V.G.

We present new approaches for 5-minute ahead electricity load forecasting. They were evaluated on data from the Australian electricity market operator for 2006-2008. After examining the load characteristics using autocorrelation analysis with 4-week sliding window, we selected 51 features. Using this feature set with linear regression and support vector regression we achieved an improvement of 7.56% in the Mean Absolute Percentage Error (MAPE) over the industry model which uses backpropagation neural network. We then investigated the application of a number of methods for further feature subset selection. Using a subset of 38 and 14 of these features with the same algorithms we were able to achieve an improvement of 6.53% and 4.81% in MAPE, respectively, over the industry model.

Published in:

Neural Networks (IJCNN), The 2010 International Joint Conference on

Date of Conference:

18-23 July 2010