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Explainable AI for Energy Prediction and Anomaly Detection in Smart Energy Buildings

Published: 15 November 2023 Publication History

Abstract

In recent years, the advancement of Artificial Intelligence (AI) and Advanced Metering Infrastructure (AMI) has led to the development of data-driven methods for energy prediction and anomaly detection. These methods provide automated decision support to building operators in managing and preventing energy loss. Despite the advantages of having sophisticated data-driven models, one major drawback is their lack of transparency, which limits their widespread use. The paper explores the use of the SHapely Additive exPlanations (SHAP), an explainable AI algorithm, to enhance transparency in energy prediction and anomaly detection models. Energy prediction is treated as a regression task, while anomaly detection as a binary classification. The study employs LightGBM models for both anomaly detection and energy prediction, which are tested on a large dataset containing hourly smart metering data from over 200 real buildings. The energy prediction model achieves an R2 score of 0.975, while the anomaly detection model obtains an AUC-ROC score of 0.942. These models are augmented with SHAP value-based visualizations, which provide both local and global explanations of these models, offering valuable insights into the factors influencing their predictions. Additionally, the present study introduces a framework that seamlessly integrates feature transformations within the model, while SHAP operates on the interpretable feature space, enhancing the explanations provided by SHAP values.

References

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Manoj Gulati and Pandarasamy Arjunan. 2022. LEAD1. 0: a large-scale annotated dataset for energy anomaly detection in commercial buildings. In Proceedings of the Thirteenth ACM International Conference on Future Energy Systems. 485–488.
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Srinivas Katipamula and Michael R Brambley. 2005. Methods for fault detection, diagnostics, and prognostics for building systems—a review, part I. Hvac&R Research 11, 1 (2005), 3–25.
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Scott M Lundberg and Su-In Lee. 2017. A unified approach to interpreting model predictions. Advances in neural information processing systems 30 (2017).
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R Machlev, L Heistrene, M Perl, KY Levy, J Belikov, S Mannor, and Y Levron. 2022. Explainable Artificial Intelligence (XAI) techniques for energy and power systems: Review, challenges and opportunities. Energy and AI 9 (2022), 100169.
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Clayton Miller, Anjukan Kathirgamanathan, Bianca Picchetti, Pandarasamy Arjunan, June Young Park, Zoltan Nagy, Paul Raftery, Brodie W Hobson, Zixiao Shi, and Forrest Meggers. 2020. The building data genome project 2, energy meter data from the ASHRAE great energy predictor III competition. Scientific data 7, 1 (2020), 368.
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Saleh Seyedzadeh, Farzad Pour Rahimian, Ivan Glesk, and Marc Roper. 2018. Machine learning for estimation of building energy consumption and performance: a review. Visualization in Engineering 6 (2018), 1–20.

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BuildSys '23: Proceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation
November 2023
567 pages
ISBN:9798400702303
DOI:10.1145/3600100
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 the author(s) 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].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 November 2023

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Author Tags

  1. Anomaly Detection
  2. Energy Prediction
  3. LightGBM
  4. XAI

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  • Short-paper
  • Research
  • Refereed limited

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BuildSys '23

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Overall Acceptance Rate 148 of 500 submissions, 30%

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