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全球科研项目数据库2026版

Carbon emission oriented next generation building energy management system

项目来源

日本学术振兴会基金(JSPS)

项目主持人

趙大放

项目受资助机构

大阪大学

立项年度

2024

立项时间

未公开

项目编号

24K20901

研究期限

未知 / 未知

项目级别

国家级

受资助金额

4810000.00日元

学科

ウェブ情報学およびサービス情報学関連

学科代码

未公开

基金类别

若手研究

关键词

HVAC ; Carbon Neutral ; ZEB ; Carbon emission ; BEMS

参与者

未公开

参与机构

大阪大学，大学院情報科学研究科

项目标书摘要：Progress is smooth due to a strong research base from prior single-HVAC frameworks published in top journals.On-site experiments in campus settings confirmed energy savings and comfort,supporting scalability.Industry partnerships with HVAC firms enable practical testing.JSPS funding and high-performance computers ensure resource availability.Regular team reviews and interdisciplinary collaboration minimize delays,aligning with project goals.Developed a hierarchical BEMS framework to cut building carbon emissions via optimized HVAC control,balancing top-level carbon quotas with local optimizations.Published 7 papers in 2024,including IEEE PESGM,ACM e-Energy,Energies,and Applied Energy,for the topic of advancing temperature forecasting,thermal load disaggregation,and comfort-aware HVAC aggregation.Moreover,the On-site experiments achieved 74%energy reduction in single-room HVAC scheduling,with building-level tests validating the framework.Collaborated with HVAC manufacturers for real-world implementation.Refine hierarchical BEMS with advanced carbon prediction and fair quota allocation using advance machine learning technologies.Scale experiments to diverse buildings with HVAC manufacturers.Acquire more simulation workstations and edge controllers for complex tests.Target publications in Applied Energy and ACM BuildSys/e-Energy by 2026.Secure grants for expanded experiments.Initiate global partnerships to adapt framework to regional carbon regulations,supporting 2050 neutrality goals.Reason:Progress is smooth due to a strong research base from prior single-HVAC frameworks published in top journals.On-site experiments in campus settings confirmed energy savings and comfort,supporting scalability.Industry partnerships with HVAC firms enable practical testing.JSPS funding and high-performance computers ensure resource availability.Regular team reviews and interdisciplinary collaboration minimize delays,aligning with project goals。Outline of Research at the Start:Carbon emission reduction has become an important issue all over the world.Exist Building Energy Management System(BEMS)mainly focus on electricity cost,energy usage and thermal comfort,but carbon emission-oriented BEMS has seldom been studied.This project aims to reduce building carbon emission through optimized control of large-scale HVAC systems,while considering occupant’s preferences and requirements。

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  • 1.Power-constrained VRF system optimization using symbolic regression for multiple zones environment

    • 关键词：
    • Air conditioning;Constrained optimization;Energy utilization;Identification (control systems);Multiple zones;Multivariable systems;Power control;Predictive control systems;Regression analysis;Energy;Energy-consumption;Flow systems;Global energy;Model-predictive control;Power constraints;Refrigerant flow;Symbolic regression;Thermal;Variable refrigerant flow
    • Theint Thu, Theint;Kato, Kenshiro;Zhao, Dafang;Nishikawa, Hiroki;Taniguchi, Ittetsu;Onoye, Takao
    • 《Energy and Buildings》
    • 2025年
    • 347卷
    • 期刊

    The rapid growth in global energy consumption highlights the urgency of doubling energy efficiency improvements by 2030. Heating, ventilation, and air-conditioning (HVAC) systems, which account for nearly half of building energy use, represent a critical target for optimization. Conventional HVAC control strategies, however, often suffer from inefficient power allocation, high peak demand, and compromised thermal comfort, especially under dynamic occupancy and environmental conditions. Existing multi-zone control methods often overlook peak power constraints and are not designed to optimize energy use under variable occupancy conditions, resulting in suboptimal energy performance. This study proposes a symbolic regression-based model predictive control (MPC) framework to address these challenges. The framework optimizes energy consumption and thermal comfort for multi-zone variable refrigerant flow (VRF) systems while addressing peak power constraints to reduce energy costs and improve thermal comfort. The method is evaluated under three operating priorities, ω = 0.1, 0.5, and 0.9, across varying power constraints. Simulation results demonstrate that the proposed method consistently outperforms a decentralized MPC state-of-the-art (SOTA) baseline, achieving up to 16 % energy savings under a 30 % power constraint, with average temperature deviations (ATD) remaining within comfortable bounds (∘C). Even under tight energy constraints, the framework maintains stable control performance, outperforming existing methods that fail to adequately manage peak loads. Compared to rule-based and model-based MPC approaches, the proposed method is more flexible and robust, as it does not require detailed system identification or extensive training data. These results highlight the method's potential as a scalable and energy-efficient solution for contributing to global energy efficiency goals. © 2025 The Authors

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