  In traditional deep learning algorithms, one of the key assumptions is that
the data distribution remains constant during both training and deployment.
However, this assumption becomes problematic when faced with
Out-of-Distribution periods, such as the COVID-19 lockdowns, where the data
distribution significantly deviates from what the model has seen during
training. This paper employs a two-fold strategy: utilizing continual learning
techniques to update models with new data and harnessing human mobility data
collected from privacy-preserving pedestrian counters located outside
buildings. In contrast to online learning, which suffers from 'catastrophic
forgetting' as newly acquired knowledge often erases prior information,
continual learning offers a holistic approach by preserving past insights while
integrating new data. This research applies FSNet, a powerful continual
learning algorithm, to real-world data from 13 building complexes in Melbourne,
Australia, a city which had the second longest total lockdown duration globally
during the pandemic. Results underscore the crucial role of continual learning
in accurate energy forecasting, particularly during Out-of-Distribution
periods. Secondary data such as mobility and temperature provided ancillary
support to the primary forecasting model. More importantly, while traditional
methods struggled to adapt during lockdowns, models featuring at least online
learning demonstrated resilience, with lockdown periods posing fewer challenges
once armed with adaptive learning techniques. This study contributes valuable
methodologies and insights to the ongoing effort to improve energy load
forecasting during future Out-of-Distribution periods.
