Energy Consumption Optimization and User Comfort Management in Residential Buildings Using a Bat Algorithm and Fuzzy Logic

Abstract

Energy management in residential buildings has grabbed the attention of many scientists for the last few years due to the fact that the residential sector consumes the highest amount of total energy produced by different energy resources. To manage the energy in residential buildings effectively, an efficient energy control system is required, capable of decreasing the total energy consumption without compromising the user-preferred environment inside the building. In the literature, many approaches have been proposed to achieve the goals of minimizing the energy consumption and maximizing the user preferred comfort by keeping different parameters under consideration, but all these methods face some problems in resolving the issue properly. The bat algorithm is one of the most recently introduced optimization approaches that has drawn the attention of researchers to apply it for solving different types of optimization problems. In this paper, the bat algorithm is applied for energy optimization in residential buildings, which is one of the most focused optimization problems in recent years. Three environmental parameters, namely temperature, illumination and air quality are bat algorithm inputs and optimized values of these parameters are the outputs. The error difference between the environmental parameters and optimized parameters are inputs of the fuzzy controllers which give energy as output which in turn change the status of the concerned actuators. It is proven from the experimental results that the proposed approach has been effectively successful in managing the whole energy consumption management system.

An Ontology-Based Framework for Building Energy Management with IoT

“…Set the values of various thresholds and parameters in the BMK module for comparison. 6. Compare the data (parameters, energy, etc.)…”

“…Traditional building energy management techniques mostly evaluate the information coming from various sensors and then take different actions according to the objectives of the optimization framework (cost minimization, human comfort maximization, etc.) [6][7][8][9][10]. Such frameworks generally focus on specific building systems and fail to evaluate the complete scenario, and therefore, result in more energy wastage as well as more inconvenience.…”

An Optimized Fuzzy Logic Control Model Based on a Strategy for the Learning of Membership Functions in an Indoor Environment

“…Novel heating, ventilation, and air conditioning (HVAC) systems are needed to provide passengers with a comfortable environment in electric vehicles. With the help of a new artificial control method, passenger comfort in electric vehicles can be improved [1,2].…”

“…Fuzzy logic and neural networks together have interested researchers in many areas of research [3,12]. The applications of fuzzy logic and the neural network are increasing tremendously in different fields such as consumer products [5] and industrial process control [12], risk index assessment [11], energy optimization [1], and so forth. Mostly, fuzzy logic control systems have been used where the exact mathematical model for controlling applications is unknown, but the behavior of the application can be identified based on the experience [13].…”

Investigating Primary Factors Affecting Electricity Consumption in Non-Residential Buildings Using a Data-Driven Approach

“…Energy consumption in the building sector has significantly increased in the last few decades. Energy is an essential part of our lives, and almost all things in some way are associated with electricity [3,4]. According to a report issued by the US Energy Information Administration (EIA), 28% growth in the global energy demand may occur until 2040 [5].…”