A Comparative Analysis of Data-Driven Based Optimization Models for Energy-Efficient Buildings. Issue 2 (17th February 2023) - Verma, Anurag; Prakash, Surya; Kumar, Anuj
Abstract : Energy-efficient and sustainable buildings have become a specialized research theme for energy planning and the green building industry. Rapidly increased energy consumption in the residential area has attracted researchers' attention to minimize energy consumption without affecting occupant comfort. Accordingly, an effective energy management system is required for energy-efficient buildings to utilize energy resources efficiently and maintain desired comfort. In this paper, data-driven based optimization models are compared to maximize the comfort index and minimize energy consumption simultaneously. Heating-cooling systems are used to maintain indoor thermal comfort, which consumes energy according to the environmental temperature and indoor temperature difference. Therefore, the environmental temperature parameter is optimized using optimization techniques Genetic Algorithm (GA), Bat, Neural Network Algorithm (NNA), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC). The main objective of the optimization is to reduce the gap/error between the temperature specified by the user and the environmental temperature. Afterward, the discrepancy between optimized temperature and actual temperature is fed to the designed ML (Machine Learning) based controller. The controller's output is further provided to coordinator agents, which give the power to the actuators accordingly. The linearity of the proposed model improves the performance of the ML algorithm. The obtained dataset from fuzzy temperature controller has been used to design an ML controller. The experimental results show that the designed system significantly improves energy efficiency and occupant comfort for energy-efficient buildings. The BAT model has shown effectiveness in achieving a high comfort index with minimum power consumption compared to other considered models.