A feedforward neural network based indoor-climate control framework for thermal comfort and energy saving in buildings

Abstract

Building air-conditioning and mechanical ventilation (ACMV) systems are responsible for significant energy consumption and yet, dissatisfaction with the thermal environment is prevalent among the occupants, revealing a widespread disparity between energy-efficiency and indoor thermal-comfort in buildings. This paper presents an indoor-climate control framework that bridges this gap between energy and comfort. The framework comprises two main components: a thermal-comfort prediction model, and an optimization algorithm termed as the optimal air temperature (OAT) algorithm; they collectively act as an intelligent mediator between the occupant and the ACMV system. Firstly, the ACMV energy consumption is modelled as a function of air temperature, and three operating frequencies of cooling components using a feedforward neural network. Secondly, the thermal-comfort prediction model predicts the thermal state index (TSI: Cool-Discomfort/Comfort/Warm-Discomfort). Thirdly, depending on the predicted TSI, the OAT algorithm locates the optimal operating state such that Comfort state is achieved using the minimum ACMV energy consumption. Proposed framework exhibits an energy saving potential of 36.5%. It is found that 25 °C is the ideal air temperature for desired comfort with minimum energy expense in the tropical buildings. Additionally, six different TSI predictive models including two general and four personal comfort models are implemented to validate the framework. The study is substantiated with extensive real human experiments in controlled thermal environment. The proposed method is scalable for its applicability with any comfort-prediction model, and adaptive for its data-driven architecture. It exhibits the potential to achieve both occupant-comfort and energy-saving through integration with the Internet-of-Things for realizing comfort-energy balanced buildings.