Navigating Out-of-Distribution Electricity Load Forecasting during COVID-19: Benchmarking energy load forecasting models without and with continual learning
This figure illustrates the impact of the first COVID-19 lockdown (indicated by the red region) on the electricity load of a building complex located in Melbourne’s Central Business District (top graph). Concurrently, it presents the effect on the average human mobility throughout the city, as measured by pedestrian count (bottom graph). The diagram clearly shows significant shifts in distributions during the lockdown, which do not fully revert to their pre-lockdown state even after restrictions are lifted
This is the official repo of the following paper: Navigating Out-of-Distribution Electricity Load Forecasting during COVID-19: Benchmarking energy load forecasting models without and with continual learning. In BuildSys 2023. [ArXiv] [Slides] [Talk].
This is a follow up paper on our previous work: Continually learning out-of-distribution spatiotemporal data for robust energy forecasting. In ECML PKDD ADS 2023. [ArXiv] [SharedIt pdf] [Poster] [Slides] [Talk].
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.
This repo contains the complete results, while the paper only contains some selection of the results. This also includes perfomrnace during the training and validation sets, and as well as the standard deviations for all results.
The complete results is here: Results_2_BuildSys23.csv. Note that _u is the mean while _s is the standard deviation. Some models are deterministic and do not have standard deviations.
If I have time, I might upload the hyperparameter and other stuff, like training time and memory use, as well.
Exponential smooting (ES) is not included as it is exactly the same as CopyLastHour.
Unfortunately, the data and the code are private and cannot be published. This might change in the future. [Written on 2023 09 18]
Arian Prabowo, Kaixuan Chen, Hao Xue, Subbu Sethuvenkatraman, and Flora D. Salim. 2023. Navigating Out-of-Distribution Electricity Load Forecasting during COVID-19: Benchmarking energy load forecasting models without and with continual learning. In Proceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys '23). Association for Computing Machinery, New York, NY, USA, 41–50. https://doi.org/10.1145/3600100.3623726
BibTex:
@inproceedings{prabowo2023energyBuildSys,
author = {Prabowo, Arian and Chen, Kaixuan and Xue, Hao and Sethuvenkatraman, Subbu and Salim, Flora D.},
title = {Navigating Out-of-Distribution Electricity Load Forecasting during COVID-19: Benchmarking Energy Load Forecasting Models without and with Continual Learning},
year = {2023},
isbn = {9798400702303},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3600100.3623726},
doi = {10.1145/3600100.3623726},
booktitle = {Proceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation},
pages = {41–50},
numpages = {10},
keywords = {benchmarking, continual learning, energy use, electricity use, out-of-distribution, timeseries forecasting},
location = {Istanbul, Turkey},
series = {BuildSys '23}
}
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
BibTex:
@InProceedings{prabowo2023energyECMLPKDD,
author="Prabowo, Arian and Chen, Kaixuan and Xue, Hao and Sethuvenkatraman, Subbu and Salim, Flora D.",
editor="De Francisci Morales, Gianmarco and Perlich, Claudia and Ruchansky, Natali and Kourtellis, Nicolas and Baralis, Elena and Bonchi, Francesco",
title="Continually Learning Out-of-Distribution Spatiotemporal Data for Robust Energy Forecasting",
booktitle="Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track",
year="2023",
publisher="Springer Nature Switzerland",
address="Cham",
pages="3--19",
isbn="978-3-031-43430-3"
}