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. © 2013 IEEE.