  Load forecasting is crucial for multiple energy management tasks such as
scheduling generation capacity, planning supply and demand, and minimizing
energy trade costs. Such relevance has increased even more in recent years due
to the integration of renewable energies, electric cars, and microgrids.
Conventional load forecasting techniques obtain single-value load forecasts by
exploiting consumption patterns of past load demand. However, such techniques
cannot assess intrinsic uncertainties in load demand, and cannot capture
dynamic changes in consumption patterns. To address these problems, this paper
presents a method for probabilistic load forecasting based on the adaptive
online learning of hidden Markov models. We propose learning and forecasting
techniques with theoretical guarantees, and experimentally assess their
performance in multiple scenarios. In particular, we develop adaptive online
learning techniques that update model parameters recursively, and sequential
prediction techniques that obtain probabilistic forecasts using the most recent
parameters. The performance of the method is evaluated using multiple datasets
corresponding with regions that have different sizes and display assorted
time-varying consumption patterns. The results show that the proposed method
can significantly improve the performance of existing techniques for a wide
range of scenarios.
