Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1145/3486611.3491127acmconferencesArticle/Chapter ViewAbstractPublication PagesbuildsysConference Proceedingsconference-collections
short-paper

Toward explainable and interpretable building energy modelling: an explainable artificial intelligence approach

Published: 17 November 2021 Publication History

Abstract

Building energy modelling is essential for the operation and optimization of energy systems. Existing efforts have focused on the model fidelity, which however should not be the only concern. Due to the lack of adequate understanding and trust in the model, many accurate energy models have not been deployed in the real world. In this article, we introduce explainability and interpretability into the building energy model. We first use partial dependence plots to explain and quantify the importance of features at a fine-grained level and reveal different importance change patterns with different feature values. We also use a surrogate-based approach to interpret the internal mechanism and decision-making process of the model. We show that the local mechanism interpretation is feasible when the surrogate is both accurate and explainable. Explanation and interpretation are visualized, to provide system users with intuitive and informative insights with multi-disciplinary discussions. Our research promotes the operation and optimization of building energy systems and supports the widespread adoption of energy systems based on machine learning.

References

[1]
Q. Xiao, C. Li, Y. Tang, and X. Chen, "Energy efficiency modeling for configuration-dependent machining via machine learning: A comparative study," IEEE Transactions on Automation Science and Engineering, 2020.
[2]
P. Arjunan, K. Poolla, and C. Miller, "Energystar++: Towards more accurate and explanatory building energy benchmarking," Applied Energy, vol. 276, p. 115413, 2020.
[3]
H. Wang, R. Cai, B. Zhou, S. Aziz, B. Qin, N. Voropai, L. Gan, and E. Barakhtenko, "Solar irradiance forecasting based on direct explainable neural network," Energy Conversion and Management, vol. 226, p. 113487, 2020.
[4]
C. Miller, "What's in the box?! towards explainable machine learning applied to non-residential building smart meter classification," Energy and Buildings, vol. 199, pp. 523--536, 2019.
[5]
W. Zhang, Y. Wen, K. J. Tseng, and G. Jin, "Demystifying thermal comfort in smart buildings: An interpretable machine learning approach," IEEE Internet of Things Journal, 2020.
[6]
J. Kruse, B. Schäfer, and D. Witthaut, "Revealing drivers and risks for power grid frequency stability with explainable ai," arXiv preprint arXiv:2106.04341, 2021.
[7]
W. Zhang, W. Hu, and Y. Wen, "Thermal comfort modeling for smart buildings: A fine-grained deep learning approach," IEEE Internet of Things Journal, vol. 6, no. 2, pp. 2540--2549, 2018.
[8]
C. Miller, P. Arjunan, A. Kathirgamanathan, C. Fu, J. Roth, J. Y. Park, C. Balbach, K. Gowri, Z. Nagy, A. D. Fontanini et al., "The ashrae great energy predictor iii competition: Overview and results," Science and Technology for the Built Environment, vol. 26, no. 10, pp. 1427--1447, 2020.

Cited By

View all
  • (2024)Grey-Box Method for Urban Building Energy Modelling: Advancements and PotentialsEnergies10.3390/en1721546317:21(5463)Online publication date: 31-Oct-2024
  • (2024)An Advanced Explainable Belief Rule-Based Framework to Predict the Energy Consumption of BuildingsEnergies10.3390/en1708179717:8(1797)Online publication date: 9-Apr-2024
  • (2024)A Future Direction of Machine Learning for Building Energy Management: Interpretable ModelsEnergies10.3390/en1703070017:3(700)Online publication date: 1-Feb-2024
  • Show More Cited By

Index Terms

  1. Toward explainable and interpretable building energy modelling: an explainable artificial intelligence approach

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    BuildSys '21: Proceedings of the 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation
    November 2021
    388 pages
    ISBN:9781450391146
    DOI:10.1145/3486611
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 17 November 2021

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. energy modelling
    2. explainable artificial intelligence
    3. machine learning
    4. smart buildings
    5. smart grid

    Qualifiers

    • Short-paper

    Funding Sources

    • National Research Foundation (NRF) via the Behavioural Studies in Energy, Water, Waste and Transportation Sectors
    • National Research Foundation (NRF) via the Green Buildings Innovation Cluster
    • Nanyang Technological University (NTU) via the Data Science \& Artificial Intelligence Research Centre @ NTU

    Conference

    BuildSys '21
    Sponsor:

    Acceptance Rates

    BuildSys '21 Paper Acceptance Rate 28 of 107 submissions, 26%;
    Overall Acceptance Rate 148 of 500 submissions, 30%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)65
    • Downloads (Last 6 weeks)2
    Reflects downloads up to 20 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2024)Grey-Box Method for Urban Building Energy Modelling: Advancements and PotentialsEnergies10.3390/en1721546317:21(5463)Online publication date: 31-Oct-2024
    • (2024)An Advanced Explainable Belief Rule-Based Framework to Predict the Energy Consumption of BuildingsEnergies10.3390/en1708179717:8(1797)Online publication date: 9-Apr-2024
    • (2024)A Future Direction of Machine Learning for Building Energy Management: Interpretable ModelsEnergies10.3390/en1703070017:3(700)Online publication date: 1-Feb-2024
    • (2024)Enhancing smart grid electricity prediction with the fusion of intelligent modeling and XAI integrationInternational Journal of ADVANCED AND APPLIED SCIENCES10.21833/ijaas.2024.05.02511:5(230-248)Online publication date: May-2024
    • (2024)Explainable Reinforcement Learning for Optimizing Electricity Costs in Building Energy Management2024 3rd International Conference on Energy Transition in the Mediterranean Area (SyNERGY MED)10.1109/SyNERGYMED62435.2024.10799400(1-6)Online publication date: 21-Oct-2024
    • (2024)ForecastExplainer: Explainable household energy demand forecasting by approximating shapley values using DeepLIFTTechnological Forecasting and Social Change10.1016/j.techfore.2024.123588206(123588)Online publication date: Sep-2024
    • (2023)Artificial Inteligence Impact on Buildings Energy Efficiency2023 7th International Conference on Computer, Software and Modeling (ICCSM)10.1109/ICCSM60247.2023.00020(56-61)Online publication date: 21-Jul-2023
    • (2022)Towards user-centered explainable energy demand forecasting systemsProceedings of the Thirteenth ACM International Conference on Future Energy Systems10.1145/3538637.3538877(446-447)Online publication date: 28-Jun-2022
    • (2022)Analysis of input parameters for deep learning-based load prediction for office buildings in different climate zones using eXplainable Artificial IntelligenceEnergy and Buildings10.1016/j.enbuild.2022.112521276(112521)Online publication date: Dec-2022

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media