Abstract
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 (FMF) for short-
term load forecasting (STLF). 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 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 datasets 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