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Predictive maintenance scheduling optimization of building heating, ventilation, and air conditioning systems

机译：建筑加热，通风和空调系统的预测维护调度优化

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摘要

We present optimization methods for predictive maintenance scheduling of building heating, ventilation, and air conditioning (HVAC) systems via mixed-integer programming. The optimization framework we introduce is composed of optimization models and parameter generation methods. Optimization models with time discretized into daily time periods are developed to enable the consideration of slow operation-dependent degradation during long horizons while maintaining computational efficiency. To improve the quality of the obtained maintenance schedules, system operation is simultaneously optimized. Parameter generation methods are introduced to provide parameters for constructing operation-related constraints. The proposed optimization framework can account for various HVAC systems with complex configurations. We show the quality of the generated operation-related parameters, and we provide medium-horizon case studies of central plants to show the model performance. (C) 2020 Elsevier B.V. All rights reserved.
机译：我们通过混合整数编程提供了建筑加热，通风和空调（HVAC）系统的预测维护调度优化方法。我们介绍的优化框架由优化模型和参数生成方法组成。开发了与日常时间段离散定期时间的优化模型，以便在长视野期间能够考虑慢操作依赖性的降解，同时保持计算效率。为了提高所获得的维护时间表的质量，同时优化系统操作。引入参数生成方法以提供用于构建与操作相关的约束的参数。所提出的优化框架可以考虑具有复杂配置的各种HVAC系统。我们展示了所产生的操作相关参数的质量，我们提供了中央植物的中地平线案例，以显示模型性能。 （c）2020 Elsevier B.v.保留所有权利。

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