Forecasting building energy usage is essential for promoting sustainability
and reducing waste, as it enables building managers to optimize energy
consumption and reduce costs. This importance is magnified during anomalous
periods, such as the COVID-19 pandemic, which have disrupted occupancy patterns
and made accurate forecasting more challenging. Forecasting energy usage during
anomalous periods is difficult due to changes in occupancy patterns and energy
usage behavior. One of the primary reasons for this is the shift in
distribution of occupancy patterns, with many people working or learning from
home. This has created a need for new forecasting methods that can adapt to
changing occupancy patterns. Online learning has emerged as a promising
solution to this challenge, as it enables building managers to adapt to changes
in occupancy patterns and adjust energy usage accordingly. With online
learning, models can be updated incrementally with each new data point,
allowing them to learn and adapt in real-time. Another solution is to use human
mobility data as a proxy for occupancy, leveraging the prevalence of mobile
devices to track movement patterns and infer occupancy levels. Human mobility
data can be useful in this context as it provides a way to monitor occupancy
patterns without relying on traditional sensors or manual data collection
methods. We have conducted extensive experiments using data from six buildings
to test the efficacy of these approaches. However, deploying these methods in
the real world presents several challenges.

本研究探讨了在流行期间建筑能源使用的预测方法，使用在线学习和人类迁移数据作为新的预测方法，以适应变化的入住模式，并在六个建筑上进行了广泛的实验。