Air conditioning (AC) plays a major role in building energy management because it generally requires a large amount of energy to maintain indoor thermal comfort. The main objective of this study is to develop a novel method for scheduling AC operations to minimize energy costs and ensure the thermal comfort of occupants under uncertainty. The key challenge is the uncertainty and variability in time-series data and their serial dependence in determining AC operation. To address this challenge, we propose an optimization-informed learning approach that integrates unsupervised and supervised learning techniques with a stochastic optimization model. This method derives energy-efficient and thermal comfort-aware AC operation schedules through a comprehensive interpretation of uncertainties and variabilities in time-series data. Numerical experimental results demonstrate that the proposed approach can reduce energy costs by up to 15.6% and decrease thermal comfort violations by up to 63.6% compared to the Deep Q-learning method, while also reducing energy costs by 1.8% and decreasing thermal comfort violations by 37.5% compared to the forecast data-driven AC scheduling method.

中文翻译：

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在不确定和动态环境下，将监督和无监督学习应用于提高能源效率和热舒适性的空调调度


空调 （AC） 在建筑能源管理中起着重要作用，因为它通常需要大量能源来维持室内热舒适度。本研究的主要目的是开发一种安排交流运行的新方法，以最大限度地降低能源成本并确保在不确定性下居住者的热舒适度。关键挑战是时间序列数据的不确定性和可变性，以及它们在确定交流操作时的序列依赖性。为了应对这一挑战，我们提出了一种优化知情学习方法，该方法将无监督和监督学习技术与随机优化模型相结合。该方法通过对时间序列数据中的不确定性和可变性进行全面解释，得出节能且热舒适感知的空调运行计划。数值实验结果表明，与深度 Q-learning 方法相比，所提出的方法可以将能源成本降低高达 15.6%，将热舒适性违规减少高达 63.6%，同时与预测数据驱动的 AC 调度方法相比，还可以将能源成本降低 1.8%，将热舒适性违规降低 37.5%。