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Autonomous Control of Indoor Climate for Commercial Buildings

商业建筑室内气候自主控制

基本信息

批准号：
1934322
负责人：
Warren Dixon
金额：
$ 45万
依托单位：
University of Florida
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-01 至 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1934322&HistoricalAwards=false
关键词：
Autonomous Control Indoor Climate Commercial

项目摘要

Buildings account for 45 percent of the total energy consumption in the United States (U.S.), and maintaining indoor climate, which includes heating, cooling, and ventilation, accounts for approximately half of that energy consumption. A low-cost option for reducing building energy usage is intelligent climate control, moving away from the prevalent "design for steady-state conditions" philosophy into one that exploits the constantly changing conditions a building operates in due to its occupants and the weather. The potential for intelligent climate control has been recognized for many years, especially for commercial buildings that have the requisite sensors and actuators. In particular, control algorithms that make decisions using real-time optimization have been shown to be highly promising. In spite of its promise, such "model-optimization" based control technologies have not been widely adopted by industry. The reason for this lack of translation to practice is the lack of autonomy of existing algorithms. Not only do they require expert human involvement in model creation, which have to be tuned for each building manually, they do not provide guarantees about the quality of real-time decisions. Addressing these weaknesses will lead to the wider adoption of intelligent building climate control technologies, which will contribute to the technological edge U.S. industries enjoy, and reduce the nation's energy usage.This research project seeks to make model+optimization based control of commercial buildings autonomous, thereby aiding wider adoption of such advanced technologies. The approach is to engineer both the modeling and optimization phases specifically for autonomy. The modeling approach is purely data driven so that it can be easily applied to any building. By using recently developed machine learning methods that guarantee certain beneficial model properties (e.g., stability), models can be updated over time purely from data without ever requiring a human expert to check the quality or suitability of the models. Similarly, the optimization problem is made convex by a choice of linear models so that real-time decision making can occur reliably without the optimizer getting stuck in a local minima or failing to converge. The reduction in accuracy due to the restriction to linear models is ameliorated by re-learning models over time, which is made possible by the autonomous data-driven nature of the model fitting algorithms. Finally, special care is taken to ensure that humidity and latent heat considerations, which are critical to hot humid climates, are taken into account both in the modeling and real-time optimization phases.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

建筑物占美国能源消耗总量的45%，以及维持室内气候，包括加热、冷却和通风，约占能源消耗的一半。减少建筑物能源使用的一个低成本选择是智能气候控制，从流行的“稳态条件设计”理念转变为利用建筑物因其居住者和天气而不断变化的运行条件。智能气候控制的潜力多年来一直受到认可，特别是对于具有必要传感器和执行器的商业建筑。特别是，使用实时优化做出决策的控制算法已被证明是非常有前途的。尽管其前景，这种基于“模型优化”的控制技术尚未被工业广泛采用。这种缺乏转化为实践的原因是现有算法缺乏自主性。它们不仅需要专家参与模型创建，必须手动调整每个建筑物，而且不能保证实时决策的质量。解决这些弱点将导致更广泛地采用智能建筑气候控制技术，这将有助于美国工业享受的技术优势，并减少国家的能源使用。本研究项目旨在使基于模型+优化的商业建筑控制自主化，从而帮助更广泛地采用这种先进技术。该方法是专门为自治设计建模和优化阶段。建模方法纯粹是数据驱动的，因此可以轻松应用于任何建筑。通过使用最近开发的机器学习方法，保证某些有益的模型属性（例如，稳定性），模型可以随着时间的推移纯粹从数据更新，而不需要人类专家检查模型的质量或适用性。类似地，通过选择线性模型使优化问题成为凸的，使得实时决策可以可靠地发生，而不会使优化器陷入局部最小值或无法收敛。由于对线性模型的限制而导致的准确性降低可以通过随着时间的推移重新学习模型来改善，这是由模型拟合算法的自主数据驱动性质所实现的。最后，特别注意确保在建模和实时优化阶段都考虑到对湿热气候至关重要的湿度和潜热因素。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。