Metaheuristics‐optimized deep learning to predict generation of sustainable energy from rooftop plant microbial fuel cells

• Published in: International journal of energy research Vol. 46; no. 15; pp. 21001 - 21027
• Main Authors: Chou, Jui‐Sheng, Cheng, Tsung‐Chi, Liu, Chi‐Yun, Guan, Chung‐Yu, Yu, Chang‐Ping
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Inc 01.12.2022; Hindawi Limited
• Subjects: Accuracy; Algorithms; Animal learning; Aquatic plants; Artificial intelligence; Artificial neural networks; Biochemical fuel cells; building energy management; Capacity; Clean energy; Computation; computer vision‐based deep learning; Deep learning; Energy; Energy technology; Environmental factors; Freshwater plants; Fuel cells; Fuel technology; green energy conversion and forecasting; Heuristic methods; jellyfish search optimizer; Machine learning; Marine invertebrates; Mathematical models; Microorganisms; Model accuracy; Neural networks; Optimization; Optimization algorithms; Parameters; plant microbial fuel cell; Renewable energy; Solar energy; Solar energy conversion; Sustainability; Sustainable energy; Typha angustifolia; Urban environments; wireless sensor network application

Summary Plant microbial fuel cells (PMFCs) are an emergent green‐energy technology that continuously converts solar energy into electricity. Placing PMFCs on the roofs of urban buildings can help to create green urban environments even as they generate power. The power generation performance of PMFC...