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

Abstract

Sustainable energy development consists of design, planning, and control optimization problems that are typically complex and computationally challenging for traditional optimization approaches. However, with developments in artificial intelligence, bio-inspired algorithms mimicking the concepts of biological evolution in nature and collective behaviors in societies of agents have recently become popular and shown potential success for these issues. Therefore, we investigate the latest research on bio-inspired approaches for smart energy management systems in smart homes, smart buildings, and smart grids in this paper. In particular, we give an overview of the well-known and emerging bio-inspired algorithms, including evolutionary-based and swarm-based optimization methods. Then, state-of-the-art studies using bio-inspired techniques for smart energy management systems are presented. Lastly, open challenges and future directions are also addressed to improve research in this field.

Application of Bio and Nature-Inspired Algorithms in Agricultural Engineering

“…For example, a 5% reduction in fuel use translated to 1647-1866 L of fuel or US$734 and US$851 [70]. Intelligent IoT systems with BIAs can enable smart farms to achieve better energy savings [64,73]. The need to invest in alternative and effective energysaving systems was justified by the growth in the precision agriculture market and the adverse effect of CO 2 pollution on farming, given it exacerbated climate change.…”

“…For example, the recent iterations transformed ACOA into one of the most versatile swarm-intelligence-based algorithms with a broad array of agricultural applications owing to the desirable pheromone smoothing approaches, evaporation, and aging [19]. The principle has broad application in agriculture, food processing [48], smart energy management [73], and engineering [1,138]. The system is grounded on the principle that ants in nature tend to follow the shortest path while moving forward [19]; this helps to prevent any possibility of stagnation from the starting point to the endpoint (source of food) and back.…”

Analyzing energy consumption of nature-inspired optimization algorithms

“…Existing nature-inspired algorithms research primarily addresses the following areas of research: optimization [1, 2, 60, 61] using metaheuristics [62] or heuristics [63] approaches; greening processes, for example greening the supply chain [64], smart energy management [65], data center energy efficiency [66]; energy efficiency [67] and energy optimization [68] in wireless sensor network clustering. Our critical literature review has shown that to date, there is no research on energy efficient natureinspired algorithms and thus, our research aims to address this identified gap.…”

Intelligent Speed Control and Performance Investigation of a Vector Controlled Electric Vehicle Considering Driving Cycles

“…This fact has significantly challenged researchers to explore new research axes and to propose new solutions toward the elaboration of an efficient and ecological vehicle. Control of electric propulsion systems [9][10][11], intelligent energy management [12][13][14], intelligent transportation and vehicular communication systems [15][16][17], autonomous vehicles [18], vehicle chassis systems [19], power electronics [20], power train architecture [21], vehicle safety [22], and machine control [23] are among the biggest current trends in automotive engineering. Driveline architecture is a key factor for improving the performance of HEVs.…”