Deep-learning-based multi-timescale load forecasting in buildings: opportunities and challenges from research to deployment
Corresponding Author(s) : Sakshi Mishra
Future Energy,
Vol. 3 No. 4 (2024): November 2024 Issue
Abstract
Electricity load forecasting for buildings and campuses is becoming increasingly important as the penetration of distributed energy resources (DERs) grows. Efficient operation and dispatch of DERs require reasonably accurate predictions of future energy consumption in order to conduct near-real-time optimized dispatch of on-site generation and storage assets. Electric utilities have traditionally performed load forecasting for load pockets spanning large geographic areas, and therefore, forecasting has not been a common practice by buildings and campus operators. Given the growing trends of research and prototyping in the grid-interactive efficient buildings domain, characteristics beyond simple algorithm forecast accuracy are important in determining the algorithm’s true utility for smart buildings. Other characteristics include the overall design of the deployed architecture and the operational efficiency of the forecasting system. In this work, we present a deep-learning-based load forecasting system that predicts the building load at 1-hour intervals for 18 hours in the future. We also discuss challenges associated with the real-time deployment of such systems as well as the research opportunities presented by a fully functional forecasting system that has been developed within the National Renewable Energy Laboratory’s Intelligent Campus program.
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- Total Energy - Monthly Energy Review, U.S. Energy Information Administration,, 02 05 2020. [Online]. Available: https://www.eia.gov/totalenergy/data/monthly/index.php. [Accessed 02 05 2020].
- U. DOE, Buildings energy data book.
- Avalable: https://data.openei.org/submissions/144
- P. G. Jordan, “Global Markets” in Solar Energy Markets, 2014, pp. 127-133.
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- M. Neukomm, V. Nubbe and R. Fares, "Grid-interactive Efficient Buildings Technical Report Series," DoE Office of Energy Efficiency & Renewable Energy, 2019.
- B. Kroposki, E. Dall’Anese, A. Bernstein, Y. Zhang and B.-M. Hodge, "Autonomous Energy Grids," 2017.
- A. Afram and F. Janabi-Sharifi, "Theory and applications of HVAC control systems - A review of model predictive contol (MPC)," Building and Environment, vol. 72, pp. 343-355, 2014.
- E. Khanmirza, A. Esmaeilzadeh and A. H. D. Markazi, "Predictive control of a building hybrid heating system for energy cost reduction," Applied Soft Computing, vol. 46, pp. 407-23, 2016.
- T.-T. Nguyen, H.-J. Yoo and H.-M. Kim, "Analyzing the Impacts of System Parameters on MPC-Based Frequency Control for a Stand-Alone Microgrid," Energies, vol. 10, no. 4, 2017.
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- F. Zhang and X. Zhou, "Gray-Regression Variable Weight Combination Model for Load Forecasting," in 2008 International Conference on Risk Management & Engineering Management, Beijing, China, 2008.
- Y. Iwafune, Y. Yagita, T. Ikegami and K. Ogimoto, "Short-term forecasting of residential building load for distributed energy management," in 2014 IEEE International Energy Conference (ENERGYCON), Cavtat, Croatia, 2014.
- Itron, "Energy Forecasting". https://na.itron.com/
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- S. Mishra, A. Glaws and P. Palanisamy, "Predictive Analytics in Future Power Systems: A Panorama and State-Of-The-Art of Deep Learning Applications," in Optimization, Learning, and Control for Interdependent Complex Networks, SpringerLink, 2020, pp. 147-182.
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- ASHRAE, "Energy Standard for Buildings Except Low-Rise Residential Buildings, Standard 90.1-2019," 2019.
- T. Stoffel and A. Andreas, "NREL Solar Radiation Research Laboratory (SRRL): Baseline Measurement System (BMS)," National Renewable Energy Laboratory, Golden, Colorado (Data), 1981.
- "Project Haystack," 2020. [Online]. Available: https://project-haystack.org/.
- G. P. Henze, S. Pless, A. Petersen, N. Long and A. T. Scambos, "Control limits for building energy end use based on frequency analysis and quantile regression," Energy Efficiency, vol. 8, p. 1077–1092, 2015.
- S. Hochreiter and J. Schmihuber, "Long Short-Term Memory," Neural Computation, vol. 9, no. 8, pp. 1735-1780, 1997.
- S. Mishra and P. Palanisamy, "An Integrated Multi-Time-Scale Modeling for Solar Irradiance Forecasting Using Deep Learning," arxiv.org, 2019.
- Cloud-Architecture-Center, Google, "MLOps: Continuous delivery and automation pipelines in machine learning," 2021.
References
Total Energy - Monthly Energy Review, U.S. Energy Information Administration,, 02 05 2020. [Online]. Available: https://www.eia.gov/totalenergy/data/monthly/index.php. [Accessed 02 05 2020].
U. DOE, Buildings energy data book.
Avalable: https://data.openei.org/submissions/144
P. G. Jordan, “Global Markets” in Solar Energy Markets, 2014, pp. 127-133.
Energy-Spectral, During COVID-19 Lockdown, Office Buildings are Consuming Even More HVAC Energy Than When Fully Occupied, Spectral Energy, 2020. [Online]. Available: https://spectral.energy/news/covid19-office-building-energy-consumption-increasing/. [Accessed 25 07 2020].
P. Donohoo-Vallett, P. Gilman, D. Feldman, J. Brodrick, D. Gohlke, R. Gravel, A. Jiron, C. Schutte, S. Satyapal, T. Nguyen, P. Scheihing, B. Marshall and S. Harman, "Revolution Now The Future Arrives for Five Clean Energy Technologies," U.S. DOE, 2016.
M. Neukomm, V. Nubbe and R. Fares, "Grid-interactive Efficient Buildings Technical Report Series," DoE Office of Energy Efficiency & Renewable Energy, 2019.
B. Kroposki, E. Dall’Anese, A. Bernstein, Y. Zhang and B.-M. Hodge, "Autonomous Energy Grids," 2017.
A. Afram and F. Janabi-Sharifi, "Theory and applications of HVAC control systems - A review of model predictive contol (MPC)," Building and Environment, vol. 72, pp. 343-355, 2014.
E. Khanmirza, A. Esmaeilzadeh and A. H. D. Markazi, "Predictive control of a building hybrid heating system for energy cost reduction," Applied Soft Computing, vol. 46, pp. 407-23, 2016.
T.-T. Nguyen, H.-J. Yoo and H.-M. Kim, "Analyzing the Impacts of System Parameters on MPC-Based Frequency Control for a Stand-Alone Microgrid," Energies, vol. 10, no. 4, 2017.
Y. Iino, M. Murai, D. Murayama, I. Motoyama, S. Kuzusaka and K. Ueta, "Hybrid modeling with physical and JIT model for building thermal load prediction and optimal energy saving control," in ICCAS-SICE, Fukuoka, Japan, 2009.
F. Zhang and X. Zhou, "Gray-Regression Variable Weight Combination Model for Load Forecasting," in 2008 International Conference on Risk Management & Engineering Management, Beijing, China, 2008.
Y. Iwafune, Y. Yagita, T. Ikegami and K. Ogimoto, "Short-term forecasting of residential building load for distributed energy management," in 2014 IEEE International Energy Conference (ENERGYCON), Cavtat, Croatia, 2014.
Itron, "Energy Forecasting". https://na.itron.com/
Enverus, "Electric Gas and Load Forecast". https://www.enverus.com/
B. Sohlberg and Jacobsen, "Grey Box Modelling - Branches and Experiences," IFAC Proceedings Volumes, vol. 41, no. 2, pp. 11415-11420, 2008.
D. B. Crawley, L. K. Lawrie, F. C. Winkelmann, W. F. Buhl, Y. H. C. O. Pedersen, R. K. Strand, R. J. Liesen, D. E. Fisher, M. J.Witte and J. Glazer, "EnergyPlus: creating a new-generation building energy simulation program," Energy and Buildings, vol. 33, no. 4, pp. 319-331, 2001.
EERE, "EnergyPlus," energy.gov, 2020. [Online]. Available: https://www.energy.gov/eere/buildings/downloads/energyplus-0.
S. S. Kwok and E. W. M. Lee, "A study of the importance of occupancy to building cooling load in prediction by intelligent approach," Energy Conversion and Management, vol. 52, no. 7, pp. 2555-2564, 2011.
S. Mishra, A. Glaws and P. Palanisamy, "Predictive Analytics in Future Power Systems: A Panorama and State-Of-The-Art of Deep Learning Applications," in Optimization, Learning, and Control for Interdependent Complex Networks, SpringerLink, 2020, pp. 147-182.
J. Hwang, D. Suh and M.-O. Otto, "Forecasting Electricity Consumption in Commercial Buildings Using a Machine Learning Approach," Energies, vol. 13, no. 22, 2020.
I. S. Jahan, V. Snasel and S. Misak, "Intelligent Systems for Power Load Forecasting: A Study Review," Energies, vol. 13, no. 22, 2020.
T. Ciechulski and S. Osowski, "Deep Learning Approach to Power Demand Forecasting in Polish Power System," Energies, vol. 13, no. 22.
Y. Jin, H. Guo, J. Wang and A. Song, "A Hybrid System Based on LSTM for Short-Term Power Load Forecasting," Energies, vol. 13, no. 23, 2020.
G. Chitalia, M. Pipattanasomporn, V. Garg and S. Rahman, "Robust short-term electrical load forecasting framework for commercial buildings using deep recurrent neural networks," Applied Energy, vol. 278, 2020.
M. N. Fekri, H. Patel, K. Grolinger and V. Sharma, "Deep learning for load forecasting with smart meter data: Online Adaptive Recurrent Neural Network," Applied Energy, vol. 282, 2021.
N. Gautam, A. S. Mayal, V. S. Ram and A. Priya, "Short Term Load Forecasting Of Urban Loads Based On Artificial Neural Network," in 2nd International Conference on Power and Embedded Drive Control (ICPEDC), 2019, 2019.
J. Cui, Q. Gao and D. Li, "Improved Long Short-Term Memory Network Based Short Term Load Forecasting," Hangzhou, China, 2020.
S. Mishra, S. M. Frank, A. Petersen, R. Buechler and M. Slovensky, "Deep-Learning-Based, Multi-Timescale Load Forecasting in Buildings: Opportunities and Challenges from Research to Deployment (preprint)," vol. 1, no. 1, pp. 1 - 13, 2020.
D. Cutler, S. Frank, M. Slovensky, M. Sheppy and A. Petersen, "Creating an Energy Intelligent Campus: Data Integration Challenges and Solutions at a Large Research Campus," in ACEEE Summer Study on Energy Efficiency in Buildings, Pacific Grove, CA, 2016.
ASHRAE, "Energy Standard for Buildings Except Low-Rise Residential Buildings, Standard 90.1-2019," 2019.
T. Stoffel and A. Andreas, "NREL Solar Radiation Research Laboratory (SRRL): Baseline Measurement System (BMS)," National Renewable Energy Laboratory, Golden, Colorado (Data), 1981.
"Project Haystack," 2020. [Online]. Available: https://project-haystack.org/.
G. P. Henze, S. Pless, A. Petersen, N. Long and A. T. Scambos, "Control limits for building energy end use based on frequency analysis and quantile regression," Energy Efficiency, vol. 8, p. 1077–1092, 2015.
S. Hochreiter and J. Schmihuber, "Long Short-Term Memory," Neural Computation, vol. 9, no. 8, pp. 1735-1780, 1997.
S. Mishra and P. Palanisamy, "An Integrated Multi-Time-Scale Modeling for Solar Irradiance Forecasting Using Deep Learning," arxiv.org, 2019.
Cloud-Architecture-Center, Google, "MLOps: Continuous delivery and automation pipelines in machine learning," 2021.