A Sparse Coding Approach to Household Electricity Demand Forecasting in Smart Grids

With the gradual deployment of smart meters in many cities around the world, new opportunities arise in reducing energy usage and improving consumers' information and control on their electricity consumption. Central to the provision of these newer services is the ability to accurately forecast the electricity demand of individual households. Compared with load forecasting at the city level and larger system aggregates, load forecasting for individual households is a much harder problem as the loads are much more stochastic and volatile. In this paper, we study the use of sparse coding for modeling and forecasting these individual household electricity loads. The proposed methods are tested on a data set of 5000 households in a joint project with electric power board of Chattanooga, for the period from September 2011 to August 2013. We obtain 10% improvements in the accuracy of forecasting next-day total load and next-week total load when we add sparse code features in ridge regression in this difficult problem. We also evaluate more classical forecasting methods on this forecasting problem, including autoregressive integrated moving average and Holt-Winters smoothing.