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ANN-based model predictive control for optimizing space cooling management

delete2025-05-29
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OA
AI
G
Giuseppe Aruta
F
Fabrizio Ascione *
N
Nicola Bianco
T
Teresa Iovane
G
Gerardo Maria Mauro
DOI：10.1016/j.energy.2025.136469delete
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摘要

摘要

En 中文
本研究将人工神经网络整合到模拟与优化框架中，以实现对住宅空间制冷的模型预测控制（MPC）。基于天气预报，该框架提供每日规划周期内的最优设定点调度方案，以降低能耗、成本及居住者的热不适感。采用多目标优化方法，旨在最小化系统运行成本及一种新型函数——舒适度惩罚函数，该函数量化全天潜在的居住者不适小时数。采用遗传算法进行优化，而前馈神经网络被训练以复制和预测建筑-设备系统的行为。前馈神经网络被训练用于预测室内温度和制冷负荷，其准确性与建筑模型输出相比表现出良好的效果。在获得帕累托前沿后，最优解与典型的夏季控制策略进行比较。结果表明，在不影响其他目标的情况下，潜在节能最高可达49%，或同时改善两个目标，制冷成本降低30%，舒适度惩罚（乌托邦准则）降低27%。这些发现表明，当设计合理时，代理模型能够准确预测建筑-设备动态，并以最小的计算成本提供可靠的优化结果。
Keyword:
Artificial neural networks
Model predictive control
Thermal comfort
Genetic algorithm
HVAC systems
Multi-objective optimization

期刊

Energy 封面图
Energy
IF:
9.4
论文数:
4.2W
被引数:
20.2W

机构

U
Univ Napoli Federico II
学者数:
445
论文数: 200
被引数: 59
引用论文

引用论文

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