Usefulness of unsupervised ensemble learning methods for time series forecasting of aggregated or clustered load

Abstract

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 gained from smart meters by a model-based representation and the K-means method. We have implemented two types of ensemble learning methods to investigate the performance of forecasting on clustered or simply aggregated load: bootstrap aggregating based and the newly proposed clustering based. Two new bootstrap aggregating 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 Ireland and Slovakia, where data from more than 3600 consumers were available in both cases. The achieved results suggest that for extremely fluctuate and noisy time series unsupervised ensemble learning is not useful. We have proved that in most of the cases when the time series are regular, unsupervised ensemble learning for forecasting aggregated and clustered electricity load significantly improves accuracy.