Distributed artificial bee colony approach for connected appliances in smart home energy management system

Abstract

In this study, we propose a computational intelligence model for the Internet of Things applications by applying the concept of swarm intelligence (SI) into connected devices. Particularly, decentralized management of smart home energy management system (HEMS) is taken into account in which connected appliances, by sharing information with each other, make the individual decisions for optimizing electricity prices of smart HEMS. Specifically, the study includes two main issues: (a) We propose a framework for decentralized management in smart HEMS; and (b) artificial bee colony (ABC) algorithm, a typical algorithm of SI techniques, has been applied for connected appliances in terms of communication and collaboration with each other to optimize the performance of the energy management system. Moreover, regarding the implementation, we develop and simulate a connected environment of smart home systems to evaluate the proposed approach. The simulation indicates the promising results in terms of optimizing the load balancing problem comparing with the conventional approach of the decentralized management system in smart home applications. K E Y W O R D S artificial ant colony algorithm, connected appliances, decentralized optimization method, Internet of Things, smart home energy management system, swarm intelligence 1 | INTRODUCTION Utilizing Internet of Things (IoT) for the energy management system becomes an emergent issue nowadays. In particular, the research on home energy management system (HEMS) in the IoT system is an important step for developing smart home applications in which IoT technologies enable appliances to be connected for sharing information with each other (Al-Ali, Zualkernan, Rashid, Gupta, and Alikarar, 2017). In this regard, the energy consumption is able to be monitored and controlled based on the information from connected appliances. Among many approaches for improving the performance of energy management in a smart home application, optimal scheduling algorithm for the energy consumption of appliances becomes the main issue that has been introduced in recent works (

Flight scheduling incorporating bad weather conditions through big data analytics: A comparison of metaheuristics

“…The findings have shown that nABC is a promising tool for resolving UFLPs. Bui et al (2020), the artificial bee colony algorithm was used to optimize the performance of the energy management system. The smart home system has been developed and simulated to evaluate the proposed approach.…”

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

“…In [117], the authors presented a decentralized HVAC management scheme where every smart device is linked by the wire connections and collaboratively interacts with others. Likewise, in [63], the authors proposed a decentralized HEMS where smart appliances communicate with each other to perform scheduling optimization based on a distributed ABC algorithm. In [113], a novel IoE communication platform was introduced to enable peer-to-peer (P2P) communications among microgrids.…”

“…Then, the proposed approach reduced total cost by 5.44% compared with baseline ACO-based HEMS. Bui et al [63] proposed a dynamic and distributed ABC-based appliance scheduling method to minimize energy usage. By taking the IoT advantages, the appliances are connected and collaborated as a fully decentralized HEMS.…”

Special issue on new trends and challenges of bio‐inspired computational intelligence algorithms in massively complex systems

“…6. A different swarm intelligence algorithm is used in the study by Bui, Agbehadji, Millham, Camacho, & Jung (2020). In this paper, the problem that is faced is the decentralized management of smart home energy management system (HEMS).…”

“…The authors have compared standard ant colony optimization (ACO), standard genetic algorithm (GA), ACO‐GA, which is a hybrid meta‐heuristic that uses ACO as main approach and GA as a local search procedure, and GA‐ACO, which is a memetic algorithm using GA as main approach and ACO as local search. To test the performance of these approaches, the authors have used 15 real data sets. A different swarm intelligence algorithm is used in the study by Bui, Agbehadji, Millham, Camacho, & Jung (2020). In this paper, the problem that is faced is the decentralized management of smart home energy management system (HEMS).…”