Functional clustering and linear regression for peak load forecasting

In this paper we consider the problem of short-term peak load forecasting using past heating demand data in a district-heating system. Our data-set consists of four separate periods, with 198 days in each period and 24 hourly observations in each day. We can detect both an intra-daily seasonality and a seasonality effect within each period. We take advantage of the functional nature of the data-set and propose a forecasting methodology based on functional statistics. In particular, we use a functional clustering procedure to classify the daily load curves. Then, on the basis of the groups obtained, we define a family of functional linear regression models. To make forecasts we assign new load curves to clusters, applying a functional discriminant analysis. Finally, we evaluate the performance of the proposed approach in comparison with some classical models. (C) 2009 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.