  The flexibility in electricity consumption and production in communities of
residential buildings, including those with renewable energy sources and energy
storage (a.k.a., prosumers), can effectively be utilized through the
advancement of short-term demand response mechanisms. It is known that
flexibility can further be increased if demand response is performed at the
level of communities of prosumers, since aggregated groups can better
coordinate electricity consumption. However, the effectiveness of such
short-term optimization is highly dependent on the accuracy of electricity load
forecasts both for each building as well as for the whole community. Structural
variations in the electricity load profile can be associated with different
exogenous factors, such as weather conditions, calendar information and day of
the week, as well as user behavior. In this paper, we review a wide range of
electricity load forecasting techniques, that can provide significant
assistance in optimizing load consumption in prosumer communities. We present
and test artificial intelligence (AI) powered short-term load forecasting
methodologies that operate with black-box time series models, such as
Facebook's Prophet and Long Short-term Memory (LSTM) models; season-based
SARIMA and smoothing Holt-Winters models; and empirical regression-based models
that utilize domain knowledge. The integration of weather forecasts into
data-driven time series forecasts is also tested. Results show that the
combination of persistent and regression terms (adapted to the load forecasting
task) achieves the best forecast accuracy.
