  Predicting the demand for electricity with uncertainty helps in planning and
operation of the grid to provide reliable supply of power to the consumers.
Machine learning (ML)-based demand forecasting approaches can be categorized
into (1) sample-based approaches, where each forecast is made independently,
and (2) time series regression approaches, where some historical load and other
feature information is used. When making a short-to-mid-term electricity demand
forecast, some future information is available, such as the weather forecast
and calendar variables. However, in existing forecasting models this future
information is not fully incorporated. To overcome this limitation of existing
approaches, we propose Masked Multi-Step Multivariate Probabilistic Forecasting
(MMMPF), a novel and general framework to train any neural network model
capable of generating a sequence of outputs, that combines both the temporal
information from the past and the known information about the future to make
probabilistic predictions. Experiments are performed on a real-world dataset
for short-to-mid-term electricity demand forecasting for multiple regions and
compared with various ML methods. They show that the proposed MMMPF framework
outperforms not only sample-based methods but also existing time-series
forecasting models with the exact same base models. Models trainded with MMMPF
can also generate desired quantiles to capture uncertainty and enable
probabilistic planning for grid of the future.
