Abstract
Forecasting building energy usage is essential for promoting sustainability and reducing waste, as it enables building managers to adjust energy use to improve energy efficiency 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. Continual learning methods offer a powerful solution to address the challenge of catastrophic forgetting in online learning, allowing energy forecasting models to retain valuable insights while accommodating new data and improving generalization in out-of-distribution scenarios. 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 four buildings to test the efficacy of these approaches. However, deploying these methods in the real world presents several challenges.
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27 November 2023
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Acknowledgment
We highly appreciate Centre for New Energy Technologies (C4NET) and Commonwealth Scientific and Industrial Research Organisation (CSIRO) for their funding support and contributions during the project. We would also like to acknowledge the support of Cisco’s National Industry Innovation Network (NIIN) Research Chair Program. This research was undertaken with the assistance of resources and services from the National Computational Infrastructure (NCI), which is supported by the Australian Government. This endeavor would not have been possible without the contribution of Dr. Hansika Hewamalage and Dr. Mashud Rana.
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1.1 Ethical Statement
Data collection: the data used in this paper are a mixture of public and private data. For privacy reasons, the energy usage data cannot be made available publicly. The lockdown dates, and pedestrian data can be access publicly. Lockdown dates is by ABC, an Australian public news service https://www.abc.net.au/news/2021-10-03/melbourne-longest-lockdown/100510710 and the pedestrian data is from City of Melbourne, a municipal government http://www.pedestrian.melbourne.vic.gov.au/.
Statement of Informed Consent: This paper does not contain any studies with human or animal participants. There are no human participants in this paper, and informed consent is not applicable.
1.2 Ethical Considerations
There are several ethical considerations related to this paper.
1.2.1 Data Privacy.
The use of data from buildings may raise concerns about privacy, particularly if personal data such as occupancy patterns is being collected and analyzed. Although the privacy of individual residents, occupants, and users are protected through the building level aggregations, sensitive information belonging to building managers, operator, and owners might be at risk. To this end, we choose to further aggregate the few buildings into complexes and make it anonymous. Unfortunately, the implication is that we cannot publish the dataset.
1.2.2 Bias and Discrimination.
There is a risk that the models used to predict energy usage may be biased against certain groups of people, particularly if the models are trained on data that is not representative of the population as a whole. This could lead to discriminatory outcomes, such as higher energy bills or reduced access to energy for marginalized communities. We do acknowledge that the CBD of Melbourne, Australia is not a representative of energy usage in buildings in general, in CBD around the world, nor Australia. However, our contribution specifically tackle the shift in distributions, albeit only temporally and not spatially. We hope that our contributions will advance the forecasting techniques, even when the distributions in the dataset are not representative.
1.2.3 Environmental Impact.
This paper can make buildings more sustainable by improving energy usage forecasting, even during anomalous periods, such as the COVID-19 pandemic. Robust and accurate forecasting enables building managers to optimize energy consumption and reduce costs. By using contextual data, such as human mobility patterns, and continual learning techniques, building energy usage can be predicted more accurately and efficiently, leading to better energy management and reduced waste. This, in turn, can contribute to the overall sustainability of buildings and reduce their impact on the environment.
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Prabowo, A., Chen, K., Xue, H., Sethuvenkatraman, S., Salim, F.D. (2023). Continually Learning Out-of-Distribution Spatiotemporal Data for Robust Energy Forecasting. In: De Francisci Morales, G., Perlich, C., Ruchansky, N., Kourtellis, N., Baralis, E., Bonchi, F. (eds) Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track. ECML PKDD 2023. Lecture Notes in Computer Science(), vol 14175. Springer, Cham. https://doi.org/10.1007/978-3-031-43430-3_1
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