Yearly and seasonal models for electricity load forecasting

We present new approaches for building yearly and seasonal models for 5-minute ahead electricity load forecasting. They are evaluated using two full years of Australian electricity load data. We first analyze the cyclic nature of the electricity load and show that the autocorrelation function captures these patterns and can be used to extract useful features, as the data is highly linearly correlated. Using the selected feature sets, we then evaluate the predictive performance of four algorithms, representing different prediction paradigms. We found linear regression to be the most accurate and fastest algorithm, outperforming the industry model based on backpropagation neural networks and all baselines. Our results also show that there is no accuracy gain in building models for each season in comparison to building a single yearly model.