Role of Artificial Intelligence in Renewable Energy Management for Sustainable Development

1. Introduction

The correlation between energy generation and consumption is essential in determining the extent of economic development within a given nation. The use and supply of energy are intricately linked to pressing global concerns, including but not limited to global warming and environmental challenges. These challenges encompass a wide range of issues such as air pollution, deforestation, ozone depletion, acid rain, greenhouse gas emissions, water and land use, loss of biodiversity, and the release of radioactive substances (Salim et al., 2018). In order to achieve a hopeful and more sustainable energy future, it is imperative for humanity to collectively confront the difficulties that arise in the realms of environmental, economic, and social repercussions. In order to address the negative impacts associated with traditional energy sources such as coal, oil, and natural gas, which are known to hinder sustainable development, there has been a global shift towards the utilization of clean energy resources, albeit with certain limitations. Renewable energy, such as hydroelectric power, geothermal energy, tidal energy, solar, and wind energy, is the sole viable and realistic alternative to traditional sources of energy (Ediger, 2019). The utilization of renewable energy sources has the potential to mitigate environmental, economic, and social concerns. These alternatives are recognized for their ability to facilitate the adoption of environmentally sustainable technologies, lower electricity expenses, generate employment opportunities, enhance public health, and foster community development, particularly in rural regions and developing nations. Moreover, they contribute to the reduction or elimination of hazardous emissions, including sulfur dioxide, carbon monoxide, and carbon dioxide (Kumar, 2020).

The utilization of renewable energy sources has the potential to enhance the diversity of the energy production industry, provide a sustainable and enduring energy supply, and mitigate both local and global emissions. It has the potential to offer economically attractive options for fulfilling specialized electricity service needs, particularly in developing countries and rural areas, while also presenting chances for local manufacturing. Artificial intelligence (AI) is extensively utilized across several aspects of renewable energy, including design, enhancement, evaluation, operation, distribution, and regulation. The primary objective of this study is to elucidate the artificial intelligence methodologies employed within the domain of renewable energy (Asif & Muneer, 2007). The increasing availability of computer resources, advanced tools, and improved data collection methods has led to the growing integration of artificial intelligence (AI) in several domains of renewable energy systems (REs). The current methodologies employed in the energy industry for design, control, and maintenance have demonstrated a tendency to yield relatively inaccurate results. Moreover, the utilization of artificial intelligence (AI) for the execution of these tasks has resulted in enhanced levels of accuracy and precision, positioning it as a leading technology in this domain. Artificial intelligence (AI) has emerged as a prominent field of study in recent decades due to its capacity to enhance the quality and productivity of automated systems (Bryson, 2019). By employing advanced training techniques and providing a comprehensive set of instructions, this system enables them to acquire knowledge, engage in logical thinking, and make decisions in a manner analogous to human cognition.