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 Forecasting using Matrix
Factorization (FMF). 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 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 RMSE over Regression
Tree and Support Vector Machine, respectively and up to 36% and 73.2%
improvement in MAPE over Random Forest and Long Short-Term Memory neural
network, respectively