Short-Term Load Forecasting Using AMI Data

Accurate short-term load forecasting (STLF) is essential for the efficient operation of the power sector. Forecasting load at a fine granularity such as hourly loads of individual households is challenging due to higher volatility and inherent stochasticity. At the aggregate levels, such as monthly load at a grid, the uncertainties and fluctuations are averaged out; hence predicting load is more straightforward. This article proposes a method called forecasting using matrix factorization (FMF) for STLF. FMF only utilizes historical data from consumers’ smart meters to forecast future loads (does not use any noncalendar attributes, consumers’ demographics or activity patterns information, etc.) and can be applied to any locality. A prominent feature of FMF is that it works at any level of user-specified granularity, both in the temporal (from a single hour to days) and spatial dimensions (a single household to groups of consumers). We empirically evaluate FMF on three benchmark data sets and demonstrate that it significantly outperforms the state-of-the-art methods in terms of load forecasting. The computational complexity of FMF is also substantially less than known methods for STLF, such as long short-term memory neural networks, random forest, support vector machines, and regression trees.