Learning-based Model Predictive Control for Smart Building Thermal Management

Eini, Roja; Abdelwahed, Sherif

doi:10.1109/honet.2019.8908098

Cited by 9 publications

(6 citation statements)

References 14 publications

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“…Moreover, Ref. [39] demonstrated the superiority of a learning-based MPC approach over conventional MPC for smart building thermal management. The simulation framework introduced by Ref.…”

Section: Control Methods For Buildingsmentioning

confidence: 99%

Advancing Sustainable Building Practices: Intelligent Methods for Enhancing Heating and Cooling Energy Efficiency

Agouzoul,

Simeu,

Tabaa

2024

Sustainability

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Our work is dedicated to enhancing sustainability through improved energy efficiency in buildings, with a specific focus on heating and cooling control and the optimization of thermal comfort of occupants. With an energy consumption of more than 60% in buildings, HVAC systems are the biggest energy users. By integrating advanced technology, data algorithms, and digital twins, our study aims to optimize energy performance effectively. We have developed a Neural Network-based Model Predictive Control (NNMPC) to achieve this goal. Leveraging technologies such as MQTT communication, Wi-Fi modules, and field-programmable gate arrays will enhance scalability and flexibility. Our findings demonstrate the efficacy of the NNMPC system deployed on the PYNQ board for reducing sensible thermal energy usage for both cooling and heating purposes. Compared to traditional On/Off control systems, the NNMPC achieved an impressive 40.8% reduction in heating energy consumption and a 37.8% decrease in cooling energy consumption in 2006. In comparison to the On/Off technique, the NNMPC demonstrated a 25.6% reduction in annual heating energy consumption and a 28.8% drop in annual cooling energy consumption in the simulated year of 2017. We observed that, across all strategies and platforms, there were no instances where the Predicted Mean Vote (PMV) fell below −0.5. However, a significant proportion of PMV values (ranging from 65% to 83%) were observed between −0.5 and 0.5, signifying a high level of occupant comfort. Additionally, for PMV values between 0.5 and 1.0, percentages ranged from 16% to 33% for both years. Importantly, the NNMPC exhibited notable efficiency in maintaining occupants’ comfort within this range, requiring less energy while ensuring highly satisfactory environments.

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“…Moreover, Ref. [39] demonstrated the superiority of a learning-based MPC approach over conventional MPC for smart building thermal management. The simulation framework introduced by Ref.…”

Section: Control Methods For Buildingsmentioning

confidence: 99%

Advancing Sustainable Building Practices: Intelligent Methods for Enhancing Heating and Cooling Energy Efficiency

Agouzoul,

Simeu,

Tabaa

2024

Sustainability

View full text Add to dashboard Cite

show abstract

“…A study compared the occupancy prediction model with and without a machine learning algorithm and showed that the accuracy was significantly improved and 30% energy saving can be achieved with the proposed algorithm [44]. Another study using a learning-based model predictive control (MPC) technique achieved significant energy savings, with 40.56% less cooling and 16.73% less heating power while keeping occupants comfortable [45].…”

Section: The Use Of Machine Learning Methods In Occupancy Predictionmentioning

confidence: 99%

A review on occupancy prediction through machine learning for enhancing energy efficiency, air quality and thermal comfort in the built environment

Zhang

Calautit

2022

Renewable and Sustainable Energy Reviews

120

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“…On the topic of learning-based approaches, the work of Eini and Abdelwahed [28] is noteworthy for its integration of Artificial Neural Networks (ANNs) with the MPC framework, resulting in notable energy savings and improved occupant comfort. Their method offers a proof of concept that resonates with our proposal's objective to optimize energy management while maintaining thermal comfort.…”

Section: Model Predictive Control For Building Climate Managementmentioning

confidence: 99%

A User Based HVAC System Management Through Blockchain Technology and Model Predictive Control

Olivieri,

Volpe,

Mangini

et al. 2024

IEEE Trans. Automat. Sci. Eng.

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This paper introduces an innovative approach to designing a user-based Heating, Ventilation, and Air-Conditioning (HVAC) system management connected with the District Energy Management System. By classifying the users into dynamic energy consumption classes to reward energy efficiency and penalize excessive use, users can modify their behavior to pass to a less expensive and more virtuous consumption class. To this aim, a blockchain platform determines the rewards and penalties and, by a K-means clustering algorithm, categorizes users into respective groups. Then, a Class Follower Problem is formulated and solved by a Model Predictive Control (MPC) strategy integrated with a Long Short-Term Memory network as a predictive model. If the users follow the suggestions proposed by the controller, i.e., the thermostat set-points and the time intervals in which the HVAC system must be switched off or on, the users can be located in a more virtuous consumption class. A case study conducted within an energy district in Bari (Italy) shows how the proposed architectural framework tuned thermal regulation in intelligent buildings while concurrently achieving energy optimization.Note to Practitioners-This paper addresses the challenge of efficiently managing HVAC systems in smart districts through a novel blockchain-based framework and an optimization strategy solved by an MPC approach. The objective is to incentivize users to optimize their energy consumption by introducing dynamic Consumption Classes that reward energy efficiency and penalize inefficient utilization. For practitioners, this strategy translates to a granular level of energy management that not only adapts to individual behaviors but also aligns with broader sustainability goals. Integrating the blockchain platform ensures a transparent and secure method for managing and recording energy usage. At the same time, adopting MPC with Long Short-Term Memory Networks offers accurate forecasts and adjustments to enhance system responsiveness. Although the study focuses on HVAC systems, the principles may be extended to other energy-intensive applications, providing a comprehensive tool for energy management and user engagement in smart cities. Future research could integrate renewable energy sources and explore the implications of user-driven adjustments on the overall energy distribution and efficiency.

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Learning-based Model Predictive Control for Smart Building Thermal Management

Cited by 9 publications

References 14 publications

Advancing Sustainable Building Practices: Intelligent Methods for Enhancing Heating and Cooling Energy Efficiency

Advancing Sustainable Building Practices: Intelligent Methods for Enhancing Heating and Cooling Energy Efficiency

A review on occupancy prediction through machine learning for enhancing energy efficiency, air quality and thermal comfort in the built environment

A User Based HVAC System Management Through Blockchain Technology and Model Predictive Control

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