  Accurate short-term load forecasting 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 paper proposes a method called Forecasting
using Matrix Factorization (\textsc{fmf}) for short-term load forecasting
(\textsc{stlf}). \textsc{fmf} only utilizes historical data from consumers'
smart meters to forecast future loads (does not use any non-calendar
attributes, consumers' demographics or activity patterns information, etc.) and
can be applied to any locality. A prominent feature of \textsc{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 \textsc{fmf} on three benchmark datasets
and demonstrate that it significantly outperforms the state-of-the-art methods
in terms of load forecasting. The computational complexity of \textsc{fmf} is
also substantially less than known methods for \textsc{stlf} such as long
short-term memory neural networks, random forest, support vector machines, and
regression trees.
