An Efficient Approach for Energy Consumption Optimization and Management in Residential Building Using Artificial Bee Colony and Fuzzy Logic

Abstract

The energy management in residential buildings according to occupant’s requirement and comfort is of vital importance. There are many proposals in the literature addressing the issue of user’s comfort and energy consumption (management) with keeping different parameters in consideration. In this paper, we have utilized artificial bee colony (ABC) optimization algorithm for maximizing user comfort and minimizing energy consumption simultaneously. We propose a complete user friendly and energy efficient model with different components. The user set parameters and the environmental parameters are inputs of the ABC, and the optimized parameters are the output of the ABC. The error differences between the environmental parameters and the ABC optimized parameters are inputs of fuzzy controllers, which give the required energy as the outputs. The purpose of the optimization algorithm is to maximize the comfort index and minimize the error difference between the user set parameters and the environmental parameters, which ultimately decreases the power consumption. The experimental results show that the proposed model is efficient in achieving high comfort index along with minimized energy consumption.

Dynamic User Preference Parameters Selection and Energy Consumption Optimization for Smart Homes Using Deep Extreme Learning Machine and Bat Algorithm

“…For the last few decades, many algorithms inspired by natural behaviors have been developed for solving many hard optimization problems. The optimization algorithms that can be used for the optimization of energy consumption are; evolutionary algorithms [78], bat algorithm [20], bee colony algorithm [79], genetic algorithm [46], harmony search [80], ant colony algorithm [81] [82], Firefly algorithm [83], fruit fly algorithm [84], and particle swarm optimization (PSO) [64]. Due to their applications in a variety of problems, they are also known as multi-purpose algorithms.…”

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

“…The objective of the technique was to improve the comfort index and decrease energy consumption. Wahid et al [67] carried out experimentation for improvement of the comfort index and energy consumption reduction in residential buildings using an artificial bee colony and fuzzy logic. They have focused on integrating the fitness function of an artificial bee colony with user comfort index and energy consumption.…”

Artificial Bee Colony Optimization of NOx Emission and Reheat Steam Temperature in a 1000 MW Boiler

“…Artificial bee colony (ABC) is a well-known intelligent optimization algorithm proposed in 2005 [19]. It has been applied more and more on engineering optimization in recent years [20][21][22][23] because of its simplicity to implement as it uses fewer control parameters [24,25] and robust ability to get out of a local minimum [26]. In this section, a multiobjective version of ABC (MO-ABC) [25] is used to optimize NOx emission and reheat steam temperature.…”