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A bi-level advanced control framework for large-scale control of buildings with system-level impact 认领 引用
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作者 Dylan Wald Olga Doronina +7 位作者 Kathryn Johnson Ryan King Michael Sinner Kevin Griffin Rohit Chintala Deepthi Vaidhynathan Jibonananda Sanyal Marc Day 《Energy and AI》 EI CSCD 2026年第1期255-274,共20页
Increased electricity consumption combined with new forms of generation is testing the reliability of our grid infrastructure.This work describes a method to improve the reliability of the grid through large-scale adv... Increased electricity consumption combined with new forms of generation is testing the reliability of our grid infrastructure.This work describes a method to improve the reliability of the grid through large-scale advanced building control.This paper develops a bi-level distributed control framework to shift the load of 153 buildings to achieve a system-level objective of tracking a power reference signal.This bi-level control is based on the previously-developed ANPV-MPC,a predictive controller that uses a Bayesian neural network to generate an accurate control model and adapt to changing conditions over time.By shifting the building electricity demand to better match the available power,the grid system supplying the buildings is more reliable as evidenced by the analysis of node voltages across an IEEE 13-bus distribution system.The proposed bi-level control framework tracks the system-level power reference with enough accuracy to regulate node voltages across the IEEE 13-bus distribution system within ANSI limits of±5%.Additionally,the adaptive nature of ANPV-MPC allows each building across the system to adapt to changing conditions,further amplifying the system-level reliability. 展开更多
关键词 Model predictive control Building modeling Building HVAC control Distributed control Grid reliability Bayesian neural networks Online learning
A neural-network-enhanced parameter-varying framework for multi-objective model predictive control applied to buildings 认领 引用 被引量：1
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作者 Dylan Wald Olga Doronina +7 位作者 Kathryn Johnson Ryan King Michael Sinner Kevin Griffin Rohit Chintala Deepthi Vaidhynathan Jibonananda Sanyal Marc Day 《Energy and AI》 EI CSCD 2025年第3期655-672,共18页
Management of the electrical grid is becoming more complex due to the increased penetration of alternative energy generation technologies and a broadening diversity of electric loads.This complexity creates challenges... Management of the electrical grid is becoming more complex due to the increased penetration of alternative energy generation technologies and a broadening diversity of electric loads.This complexity creates challenges in balancing demand and generation that can increase the potential for grid instabilities.One effective way to address this issue is to leverage previously unexploited demand flexibility through advanced control strategies.In this work,we propose an advanced control method,called adaptive neural parameter-varying model predictive control(ANPV-MPC),to control the temperature and energy consumption of a building via its Heating,Ventilation,and Air Conditioning system.ANPV-MPC combines key ideas in varying parameter-control,adaptive control,and online learning strategies to bridge the gap between computationally efficient linear model predictive control and more accurate nonlinear model predictive control.The novelty in ANPV-MPC is the use of a physics-inspired Bayesian neural network to estimate the coefficients of the parameter-varying linear control model.The Bayesian neural network additionally provides uncertainty estimates,triggering online training to capture evolving building system conditions.We show that ANPV-MPC can approximate the building system dynamics with a 28.39%higher accuracy than traditional linear model predictive control,resulting in 36.23%better control performance without increasing complexity of the optimal control problem.ANPV-MPC also adapts in real time to previously unseen conditions using online learning,further improving its performance. 展开更多
关键词 Bayesian neural networks Online learning Parameter-varying control Model predictive control Adaptive control Building supervisory HVAC control
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