Hour-Ahead Load Forecasting Using AMI Data

Accurate short-term load forecasting is essential for efficient operation of the power sector. Predicting load at a fine granularity such as individual households or buildings is challenging due to higher volatility and uncertainty in the load. In aggregate loads such as at grids level, the inherent stochasticity and fluctuations are averaged-out, the problem becomes substantially easier. We propose an approach for short-term load forecasting at individual consumers (households) level, called {\em Forecasting using Matrix Factorization} (\textsc{FMF}). \textsc{FMF} does not use any consumers' demographic or activity patterns information. Therefore, it can be applied to any locality with the readily available smart meters and weather data. We perform extensive experiments on three benchmark datasets and demonstrate that \textsc{FMF} significantly outperforms the computationally expensive state-of-the-art methods for this problem. We achieve up to $26.5 \%$ and $24.4 \%$ improvement in \textsc{RMSE} over Regression Tree and Support Vector Machine, respectively and up to $36 \%$ and $73.2 \%$ improvement in \textsc{MAPE} over Random Forest and Long Short-Term Memory neural network, respectively.