Analyzing the impact of COVID-19 on the electricity demand in Austin, TX using an ensemble-model based counterfactual and 400,000 smart meters

Computational Urban Science - Tập 3 - Trang 1-16 - 2023
Ting-Yu Dai1, Praveen Radhakrishnan1, Kingsley Nweye1, Robert Estrada1, Dev Niyogi1, Zoltan Nagy1
1Department of Civil, Environmental and Architectural Engineering, The University of Texas at Austin, Austin, USA

Tóm tắt

The COVID-19 pandemic caused lifestyle changes and has led to the new electricity demand patterns in the presence of non-pharmaceutical interventions such as work-from-home policy and lockdown. Quantifying the effect on electricity demand is critical for future electricity market planning yet challenging in the context of limited smart metered buildings, which leads to limited understanding of the temporal and spatial variations in building energy use. This study uses a large scale private smart meter electricity demand data from the City of Austin, combined with publicly available environmental data, and develops an ensemble regression model for long term daily electricity demand prediction. Using 15-min resolution data from over 400,000 smart meters from 2018 to 2020 aggregated by building type and zip code, our proposed model precisely formalizes the counterfactual universe in the without COVID-19 scenario. The model is used to understand building electricity demand changes during the pandemic and to identify relationships between such changes and socioeconomic patterns. Results indicate the increase in residential usage , demonstrating the spatial redistribution of energy consumption during the work-from-home period. Our experiments demonstrate the effectiveness of our proposed framework by assessing multiple socioeconomic impacts with the comparison between the counterfactual universe and observations.

Tài liệu tham khảo

Abdeen, A., Kharvari, F., & Gunay, B. (2021). The impact of the covid-19 on households’ hourly electricity consumption in canada. Energy and Buildings, 250, 111280. https://doi.org/10.1016/j.enbuild.2021.111280 Abu-Rayash, A., & Dincer, I. (2020). Analysis of the electricity demand trends amidst the covid-19 coronavirus pandemic. Energy Research & Social Science, 68, 101682. https://doi.org/10.1016/j.erss.2020.101682 Abulibdeh, A. (2021). Modeling electricity consumption patterns during the covid-19 pandemic across six socioeconomic sectors in the state of qatar. Energy Strategy Reviews, 38, 100733. https://doi.org/10.1016/j.esr.2021.100733 Austin, C. O. (2021, December 22). Data Library | AustinTexas.gov. https://www.austintexas.gov/page/data-library Bahmanyar, A., Estebsari, A., & Ernst, D. (2020). The impact of different covid-19 containment measures on electricity consumption in europe. Energy Research & Social Science, 68, 101683. https://doi.org/10.1016/j.erss.2020.101683 Berg, B., Malekpour Koupaei, D., Cetin, K., & Passe, U. (2022). Impact of the COVID-19 pandemic on single family homes’ electricity consumption in the rural Iowa. EasyChair. Bielecki, S., Skoczkowski, T., Sobczak, L., Buchoski, J., Maciag, L., & Dukat, P. (2021). Impact of the lockdown during the covid-19 pandemic on electricity use by residential users. Energies, 14, 980. https://doi.org/10.3390/en14040980 Birol, F. (2020, April 14). The coronavirus crisis reminds us that electricity is more indispensable than ever. International energy Agency.https://www.iea.org/commentaries/the-coronavirus-crisis-reminds-us-that-electricity-is-more-indispensable-than-ever Bureau, A. T. (2020). Effects of novel coronavirus (covid-19) on civil aviation: economic impact analysis. Montréal: International Civil Aviation Organization (ICAO) Chen, T. & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785–794) Chihib, M., Salmerón-Manzano, E., Chourak, M., Perea, A., & Manzano-Agugliaro, F. (2021). Impact of the covid-19 pandemic on the energy use at the university of almeria (spain). Sustainability, 13, 5843. https://doi.org/10.3390/su13115843 Elnakat, A., Gomez, J. D., & Booth, N. (2016). A zip code study of socioeconomic, demographic, and household gendered influence on the residential energy sector. Energy Reports, 2, 21–27. Freund, Y., & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139. Fu, X., & Zhai, W. (2021). Examining the spatial and temporal relationship between social vulnerability and stay-at-home behaviors in new york city during the covid-19 pandemic. Sustainable Cities and Society, 67, 102757. García, S., Pajero Matos, A., Personal, E., Guerrero, J., Biscarri, F., & León, C. (2021). A retrospective analysis of the impact of the covid-19 restrictions on energy consumption at a disaggregated level. Applied Energy, 287, 116547. https://doi.org/10.1016/j.apenergy.2021.116547 Gaspar, K., Gangolells, M., Casals, M., Pujadas, P., Forcada, N., Macarulla, M., & Tejedor, B. (2021). Assessing the impact of the covid-19 lockdown on the energy consumption of university buildings. Energy and Buildings, 257, 111783. https://doi.org/10.1016/j.enbuild.2021.111783 Harputlugil, T., & de Wilde, P. (2021). The interaction between humans and buildings for energy efficiency: A critical review. Energy Research & Social Science, 71, 101828. Ho, T. K. (1995). Random decision forests. In Proceedings of 3rd international conference on document analysis and recognition (vol. 1, pp 278–282). IEEE Inoue, H., & Todo, Y. (2020). The propagation of economic impacts through supply chains: The case of a mega-city lockdown to prevent the spread of covid-19. PloS ONE, 15(9), e0239251. Intelligent Environments Laboratory, Department of Civil, Architectural and Environmental Engineering, The University of Texas at Austin. (2021, December 22). Covid-19 impact in Austin, TX. http://covid19atx.net/ International Energy Agency (IEA). (2021, May 2). Global energy demand to plunge this year as a result of the biggest shock since the second world war. https://www.iea.org/news/global-energy-demand-to-plunge-this-year-as-a-result-of-the-biggest-shock-since-the-second-world-war Kaye, A. D., Okeagu, C. N., Pham, A. D., Silva, R. A., Hurley, J. J., Arron, B. L., Sarfraz, N., Lee, H. N., Ghali, G. E., Gamble, J. W., et al. (2021). Economic impact of covid-19 pandemic on healthcare facilities and systems: International perspectives. Best Practice & Research Clinical Anaesthesiology, 35(3), 293–306. Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). Lightgbm: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 30 Merrifield, A. L., Brunner, L., Lorenz, R., Medhaug, I., & Knutti, R. (2020). An investigation of weighting schemes suitable for incorporating large ensembles into multi-model ensembles. Earth System Dynamics, 11(3), 807–834. Olu-Ajayi, R., Alaka, H., Sulaimon, I., Sunmola, F., & Ajayi, S. (2022). Building energy consumption prediction for residential buildings using deep learning and other machine learning techniques. Journal of Building Engineering, 45, 103406. Prol, J. L., & Sungmin, O. (2020). Impact of covid-19 measures on short-term electricity consumption in the most affected eu countries and usa states. Iscience, 23(10), 101639. Qasem, S. N., Samadianfard, S., Sadri Nahand, H., Mosavi, A., Shamshirband, S., & Chau, K.-W. (2019). Estimating daily dew point temperature using machine learning algorithms. Water, 11(3), 582. Robinson, C., Dilkina, B., Hubbs, J., Zhang, W., Guhathakurta, S., Brown, M. A., & Pendyala, R. M. (2017). Machine learning approaches for estimating commercial building energy consumption. Applied Energy, 208, 889–904. Robinson, C., Lindley, S., & Bouzarovski, S. (2019). The spatially varying components of vulnerability to energy poverty. Annals of the American Association of Geographers, 109(4), 1188–1207. Shan, Y., Ou, J., Wang, D., Zeng, Z., Zhang, S., Guan, D., & Hubacek, K. (2021). Impacts of covid-19 and fiscal stimuli on global emissions and the paris agreement. Nature Climate Change, 11(3), 200–206. Zimmerman, N., Presto, A. A., Kumar, S. P., Gu, J., Hauryliuk, A., Robinson, E. S., Robinson, A. L., & Subramanian, R. (2018). A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring. Atmospheric Measurement Techniques,11(1), 291–313.