Variable Selection for Five-Minute Ahead Electricity Load Forecasting

We use autocorrelation analysis to extract 6 nested feature sets of previous electricity loads for 5-minite ahead electricity load forecasting. We evaluate their predictive power using Australian electricity data. Our results show that the most important variables for accurate prediction are previous loads from the forecast day, 1, 2 and 7 days ago. By using also load variables from 3 and 6 days ago, we achieved small further improvements. The 3 bigger feature sets (37-51 features) when used with linear regression and support vector regression algorithms, were more accurate than the benchmarks. The overall best prediction model in terms of accuracy and training time was linear regression using the set of 51 features.