Improved Electricity Load Forecasting via Kernel Spectral Clustering of Smart Meters

This paper explores kernel spectral clustering methods to improve forecasts of aggregated electricity smart meter data. The objective is to cluster the data in such a way that building a forecasting models separately for each cluster and taking the sum of forecasts leads to a better accuracy than building one forecasting model for the total aggregate of all meters. To measure the similarity between time series, we consider wavelet feature extraction and several positive-definite kernels. To forecast the aggregated meter data, we use a periodic autoregressive model with calendar and temperature information as exogenous variable. The data used in the experiments are smart meter recordings from 6,000 residential customers and small-to-medium enterprises collected by the Irish Commission for Energy Regulation (CER). The results show a 20% improvement in forecasting accuracy, where the highest gain is obtained using a kernel with the Spearman's distance. The resulting clusters show distinctive patterns particularly during hours of peak demand.