A multi-discipline predictive intelligent control method for maintaining the thermal comfort on indoor environment

Abstract

The emergence and application of soft computing have significantly changed the methods to solve engineering problems. For the indoor environmental control, various machine learning methods have been used, attempting to replace the traditional engineering methods. However, the recent single data-driven machine learning approach can hardly meet the requirement, since engineering problems still require prior knowledge to extract features and set up restrictions, while this knowledge should be obtained from engineering analysis. In this case, this paper has proposed an integrated multi-discipline method combining machine learning with engineering analysis, to implement predictive intelligent indoor environmental control in terms of thermal comfort and energy consumption. This method includes three parts, i.e., environmental modelling and simulation, producing an environmental prediction model, and creating an intelligent control agent and system. Firstly, a physical model is created to simulate the indoor environment and analysed through computational fluid dynamics, whose results can guide the setup of sensors in the indoor environment for collecting real-time data. Then, a machine learning method support vector regression is used to create an environmental prediction model for key parameters within the indoor environment, based on the collected data. Finally, a reinforcement learning method is used to train an intelligent agent for the intelligent control on the indoor environment, together with a system for implementation. Experiments and evaluations are carried out in a case study within an office, demonstrating the proposed method’s feasibility, which provides a more efficient and effective intelligently predictive control on the indoor environment considering the balance of thermal comfort and energy efficiency.