Synthesis of model predictive control based on neural network for energy consumption enhancement in building

Agouzoul, Abdelali; Simeu, Emmanuel; Tabaa, Mohamed

doi:10.1016/j.aeue.2023.155021

Cited by 3 publications

(2 citation statements)

References 32 publications

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“…[40] used data-driven techniques for HVAC behavior emulation, leading to more significant energy savings. Our previous work [13] aimed to explore the feasibility of leveraging ANNs to forecast zone temperatures and facilitate proactive control of HVAC system controllers. This pioneering approach sought to conserve energy by integrating ANNs as an internal model within the MPC framework.…”

Section: Control Methods For Buildingsmentioning

confidence: 99%

“…By delving into these aspects, our research aims not only to reduce the demand for heating and cooling, which collectively constitute the majority of energy consumption in the building sector, but also to monitor and manage the thermal comfort of occupants. This focus on ensuring occupant comfort, coupled with the robustness of our proposed solution, distinguishes our work from a previous study [13] that concentrated on the feasibility of the proposed solution to reduce the energy furnished by HVAC systems. Finally, we want to catalyze the practical advances that will foster a future in which buildings guarantee both energy efficiency and the well-being of their occupants.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Control Methods For Buildingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Agouzoul,

Simeu,

Tabaa

2024

Sustainability

Self Cite

View full text Add to dashboard Cite

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Economic model predictive control for building HVAC system: A comparative analysis of model-based and data-driven approaches using the BOPTEST Framework

Zheng,

Wang,

Wang

2024

Applied Energy

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A Thermal Model for Rural Housing in Mexico: Towards the Construction of an Internal Temperature Assessment System Using Aerial Thermography

Moctezuma-Sánchez,

Espinoza Gómez,

López-Sosa

et al. 2024

Buildings

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Estimating energy flows that affect temperature increases inside houses is crucial for optimizing building design and enhancing the comfort of living spaces. In this study, a thermal model has been developed to estimate the internal temperature of rural houses in Mexico using aerial thermography. The methodology used in this study considered three stages: (a) generating a semi-experimental thermal model of heat transfer through roofs for houses with high infiltration, (b) validating the model using contact thermometers in rural community houses, and (c) integrating the developed model using aerial thermography and Python 3.11.4 into user-friendly software. The results demonstrate that the thermal model is effective, as it was tested on two rural house configurations and achieved an error margin of less than 10% when predicting both maximum and minimum temperatures compared to actual measurements. The model consistently estimates the internal house temperatures using aerial thermography by measuring the roof temperatures. Experimental comparisons of internal temperatures in houses with concrete and asbestos roofs and the model’s projections showed deviations of less than 3 °C. The developed software for this purpose relies solely on the fundamental thermal properties of the roofing materials, along with the maximum roof temperature and ambient temperature, making it both efficient and user-friendly for rural community management systems. Additionally, the model identified areas with comfortable temperatures within different sections of a rural community, demonstrating its effectiveness when integrated with aerial thermography. These findings suggest the potential to estimate comfortable temperature ranges in both rural and urban dwellings, while also encouraging the development of public policies aimed at improving rural housing.

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Synthesis of model predictive control based on neural network for energy consumption enhancement in building

Cited by 3 publications

References 32 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

Economic model predictive control for building HVAC system: A comparative analysis of model-based and data-driven approaches using the BOPTEST Framework

A Thermal Model for Rural Housing in Mexico: Towards the Construction of an Internal Temperature Assessment System Using Aerial Thermography

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