A Sparse Coding Approach to Household Electricity Demand Prediction in Smart Grid

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 to load forecasting at the city level and larger system aggregates, load forecasting for individual households is a much harder problem as the load is 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 dataset of 5000 households in a joint project with EPB of Chattanooga, for the period September 2011 to August 2013. We obtain 10% improvements in the accuracy of predicting next-day total load and next-week total load in this difficult problem. We also evaluate more classical forecasting methods on this forecasting problem, including ARIMA, Holt-Winters smoothing, and ridge regression. Finally, we demonstrate a simple application of the load forecasting algorithm for raising consumers' awareness of their electricity usage through an application prototype for monthly electricity budgeting.