Review and outlook on reinforcement learning: Its application in agricultural energy internet - Xueqian Fu; Jing Zhang; Xiang Bai; Xinyue Chang; Yixun Xue (IET Renewable Power Generation)
Abstract Agricultural Energy Internet (AEI), representing a key evolutionary direction in the integrated energy landscape of rural regions, holds a vital position in advancing the electrification of agricultural sectors. However, the disjointed control between agricultural loads and grid operations hinders the collaborative development of agriculture and energy. Addressing these issues, this paper investigates the current applications of artificial intelligence in the fields of agriculture and energy. The authors examine the evolutionary path of AEI, particularly emphasizing the critical technologies emerging from the intersection of agriculture, energy, and digital networks. Furthermore, the authors examine the critical technologies of reinforcement learning in the context of smart grid applications. In response to the challenges posed by low energy efficiency in rural areas, a reinforcement learning framework is proposed for coordinating fisheries, agriculture, livestock farming, and rural distribution networks. This framework provides a clear pathway for the application of reinforcement learning in AEI. This research acts as a conduit, merging agricultural and energy domains to promote a cohesive progression that markedly aids in the enhancement of rural electrification and the adoption of sustainable energy methodologies through reinforcement learning.