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Energy Policy Driven by AI Towards Sustainable Future
Abstract
The current decade is a turning point in the energy sector's transition, with the introduction of green energy along with the optimization of efficacy through machine learning and artificial intelligence (AI). As a result, competitive policies are needed to manage multifaceted tasks on one platform. When energy policies fail to accomplish both energy as well as climate targets over their entire lifecycle, the socioeconomic ramifications can be profound. These deficiencies are said to result from poor decision-making and insufficient incentives, which should encourage equity, equality, fairness, and inclusivity in energy policy and decision-making regarding projects. This chapter seeks to assess the many obstacles posed by the emergence of AI in the energy industry. Specifically, the study addresses (1) the development stage decision-making process, 2) the execution stage implementation management process, (3) integrating deep learning, machine learning and data science in the energy systems, and (4) the substantiality requirements of energy systems.
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