LAUSR

This paper presents a comparison of the impact of various unsupervised ensemble learning methods on electricity load forecasting. The electricity load from consumers is simply aggregated or optimally clustered to more predictable groups by cluster analysis. The clustering approach consists of efficient preprocessing of data obtained from smart meters by a model-based representation and the K-means method. We have implemented two types of unsupervised ensemble learning methods to investigate the performance of forecasting on clustered or simply aggregated load: bootstrap aggregating based and the newly proposed density-clustering based. Three new bootstrapping methods for time series analysis methods were newly proposed in order to handle the noisy behaviour of time series. The smart meter datasets used in our experiments come from Australia, London, and Ireland, where data from residential consumers were available. The achieved results suggest that for extremely fluctuating and noisy time series the forecasting accuracy improvement through the bagging can be a challenging task. However, our experimental evaluation shows that in most of the cases the density-based unsupervised ensemble learning methods are significantly improving forecasting accuracy of aggregated or clustered electricity load.