Time-Constrained Nature-Inspired Optimization Algorithms for an Efficient Energy Management System in Smart Homes and Buildings

Abstract

This paper proposes two bio-inspired heuristic algorithms, the Moth-Flame Optimization (MFO) algorithm and Genetic Algorithm (GA), for an Energy Management System (EMS) in smart homes and buildings. Their performance in terms of energy cost reduction, minimization of the Peak to Average power Ratio (PAR) and end-user discomfort minimization are analysed and discussed. Then, a hybrid version of GA and MFO, named TG-MFO (Time-constrained Genetic-Moth Flame Optimization), is proposed for achieving the aforementioned objectives. TG-MFO not only hybridizes GA and MFO, but also incorporates time constraints for each appliance to achieve maximum end-user comfort. Different algorithms have been proposed in the literature for energy optimization. However, they have increased end-user frustration in terms of increased waiting time for home appliances to be switched ON. The proposed TG-MFO algorithm is specially designed for nearly-zero end-user discomfort due to scheduling of appliances, keeping in view the timespan of individual appliances. Renewable energy sources and battery storage units are also integrated for achieving maximum end-user benefits. For comparison, five bio-inspired heuristic algorithms, i.e., Genetic Algorithm (GA), Ant Colony Optimization (ACO), Cuckoo Search Algorithm (CSA), Firefly Algorithm (FA) and Moth-Flame Optimization (MFO), are used to achieve the aforementioned objectives in the residential sector in comparison with TG-MFO. The simulations through MATLAB show that our proposed algorithm has reduced the energy cost up to 32.25% for a single user and 49.96% for thirty users in a residential sector compared to unscheduled load.

Intelligent Controllers and Optimization Algorithms for Building Energy Management Towards Achieving Sustainable Development: Challenges and Prospects

“…The metaheuristic optimization algorithm is based on the procedure of randomization and local search, which leads to the optimization and path of global search [231]. The classification of metaheuristics optimization algorithms implemented for BEMS is shown in Fig.…”

A comparison of metaheuristics for the optimal capacity planning of an isolated, battery-less, hydrogen-based micro-grid

“…In view of the continuously evolving landscape of the swarm-based metaheuristic optimization algorithms, this sub-section seeks to investigate the performance of the modified variants of the MFOA, when applied to the optimal equipment capacity planning problem of the MG system laid out in Section 2. To this end, seven modified versions of the MFOA were identified and applied to the problem at hand, namely (i) the improved MFOA (IMFOA) [58], (ii) the moth swarm algorithm (MSA) [59], (iii) the hybrid GA-MSA (HGA-MSA) [60], (iv) the timeconstrained GA-MFOA (TGA-MFOA) [61], (v) the hybrid simulated annealing algorithm-MFOA (HSAA-MFOA) [62], (vi) the hybrid water cycle-MFOA (HWC-MFOA) [63], and (vii) the hybrid SSA-MFOA (HSSA-MFOA) [64]. These algorithms have been confirmed as superior to the original MFOA using either a standard set of benchmark (test) functions and/or test-case engineering optimization problem(s).…”

Bio-Inspired Approaches for Smart Energy Management: State of the Art and Challenges

“…A Wind-Driven Bacterial Foraging algorithm, which combines a wind-driven algorithm and a bacterial foraging algorithm, has been implemented to systematically schedule IoT-based appliances in the smart home to eliminate PAR, decrease energy expenditure, and increase consumer comfort [104]. Some other studies have also applied hybrid bio-inspired approaches to solve different issues in EMS [105][106][107][108][109]. The hybrid algorithms can enhance the convergence and computational time of energy optimization and scheduling problems.…”