Application of supervised and unsupervised learning for enhancing energy efficiency and thermal comfort in air conditioning scheduling under uncertain and dynamic environments
IF 6.6 2区 工程技术Q1 CONSTRUCTION & BUILDING TECHNOLOGY
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引用次数: 0
Abstract
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.
期刊介绍:
An international journal devoted to investigations of energy use and efficiency in buildings
Energy and Buildings is an international journal publishing articles with explicit links to energy use in buildings. The aim is to present new research results, and new proven practice aimed at reducing the energy needs of a building and improving indoor environment quality.