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Decision Tree Variations and Online Tuning for Real-Time Control of a Building in a Two-Stage Management Strategy

Author

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  • Rémy Rigo-Mariani

    (Univ. Grenoble Alpes, CNRS, Grenoble INP, G2Elab, 21 Avenue des Martyrs, 38000 Grenoble, France
    CNRS@CREATE, 1 Create Way, Singapore 138602, Singapore)

  • Alim Yakub

    (CNRS@CREATE, 1 Create Way, Singapore 138602, Singapore)

Abstract

This study examines the use of data-driven controllers for near real-time control of an HVAC and storage system in a residential building. The work is based on a two-stage management with, first, a day-ahead optimal scheduling, and second, a near real-time adaptive control to remain close to the commitments made in the first stage. A Model Predictive Control (MPC) is adopted from previous works from the authors. The aim of this paper is then to explore lightweight controllers for the real-time stage as alternatives to MPC, which relies on computational-intensive modeling and optimization. Decision Trees (DTs) are considered for this purpose, offering understandable solutions by processing input data through explicit tests of the inputs with predefined thresholds. Various DT variations, including regular, regressors, and linear DTs, are studied. Linear DTs, with a minimal number of leaves, exhibit superior performance, especially when trained on historical MPC data, outperforming the reference MPC in terms of energy exchange efficiency. However, due to impracticalities, an offline training approach for the DTs is proposed, which sacrifices performance. An online tuning strategy is then introduced, updating the DT coefficients based on real-time observations, significantly enhancing performance in terms of energy deviation reduction during real-time operation.

Suggested Citation

  • Rémy Rigo-Mariani & Alim Yakub, 2024. "Decision Tree Variations and Online Tuning for Real-Time Control of a Building in a Two-Stage Management Strategy," Energies, MDPI, vol. 17(11), pages 1-17, June.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:11:p:2730-:d:1408250
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    References listed on IDEAS

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    1. Pinto, Giuseppe & Deltetto, Davide & Capozzoli, Alfonso, 2021. "Data-driven district energy management with surrogate models and deep reinforcement learning," Applied Energy, Elsevier, vol. 304(C).
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    3. Arroyo, Javier & Manna, Carlo & Spiessens, Fred & Helsen, Lieve, 2022. "Reinforced model predictive control (RL-MPC) for building energy management," Applied Energy, Elsevier, vol. 309(C).
    4. Klingler, Anna-Lena, 2017. "Self-consumption with PV+Battery systems: A market diffusion model considering individual consumer behaviour and preferences," Applied Energy, Elsevier, vol. 205(C), pages 1560-1570.
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