The integration of artificial intelligence (AI) in the renewable energy sector has emerged as a disruptive force that has the potential to fundamentally alter the ways in which clean energy is produced, distributed, and used. Artificial intelligence (AI) technologies, such as machine learning, predictive analytics, optimization algorithms, and smart control systems, have enabled unprecedented improvements in renewable energy systems. These advancements include optimizing energy storage systems to increase grid dependability and stability as well as optimizing solar and wind turbine efficiency via the use of adaptive control and real-time data analysis. Artificial intelligence-driven predictive maintenance has decreased operational costs and downtime, extending the lifespan of renewable infrastructure. AI-powered energy management solutions encourage an energy-saving culture by giving consumers the ability to make informed decisions about how much energy they use.
Top1. Introduction
The relationship between energy production and consumption is crucial for assessing the level of economic growth in a particular country. Energy supply and use are closely related to urgent worldwide issues, such as environmental problems and global warming. These difficulties include a broad spectrum of problems, including greenhouse gas emissions, acid rain, deforestation, air pollution, ozone depletion, water and land usage, biodiversity loss, and radioactive material discharge (Salim et al., 2018). Humanity must face the challenges that emerge in the areas of environmental, economic, and social consequences if it is to attain a more optimistic and sustainable energy future. With certain restrictions, there has been a worldwide movement toward the use of clean energy resources to mitigate the negative effects of conventional energy sources like coal, oil, and natural gas, which are known to impede sustainable development. The only practical and affordable substitute for conventional energy sources is renewable energy, which includes hydroelectric power, geothermal energy, tidal energy, solar energy, and wind energy (Ediger, 2019). It is possible to address social, economic, and environmental issues by using renewable energy sources. These substitutes are acknowledged for their capacity to promote community development, especially in developing and rural areas, cut power costs, create jobs, improve public health, and ease the adoption of environmentally friendly technology. Additionally, they aid in the decrease or eradication of dangerous pollutants such as carbon dioxide, sulfur dioxide, and carbon monoxide (Kumar, 2020).
In addition to offering a long-lasting and sustainable energy supply, the use of renewable energy sources has the potential to increase the industry's diversification and reduce emissions on a local and worldwide scale. Particularly in developing nations and rural regions, it may provide economically appealing solutions for meeting specific electrical service demands. It may also create opportunities for local manufacturing. Numerous facets of renewable energy, including as design, development, assessment, operation, distribution, and regulation, heavily rely on artificial intelligence (AI). This study's major goal is to clarify the artificial intelligence techniques used in the renewable energy industry (Asif & Muneer, 2007). Artificial intelligence (AI) is becoming more and more integrated into many areas of renewable energy systems (REs) as a result of the expanding availability of computer power, sophisticated tools, and enhanced data gathering techniques. The design, control, and maintenance approaches now used in the energy business have shown a propensity to provide outcomes that are somewhat erroneous. Furthermore, the use of artificial intelligence (AI) in carrying out these duties has led to increased degrees of precision and accuracy, establishing it as a front-runner in this field of technology. Because it may improve the efficiency and quality of automated systems, artificial intelligence (AI) has been a popular area of research in recent decades (Bryson, 2019). This system uses sophisticated training methods and gives them a complete set of instructions so they may learn, reason, and make judgments in a way that is similar to human cognition.
Renewable energy is expected to overtake coal as the world's leading source of electricity production by the early 2025, making it the best source in this area. The estimate indicates that their part of the power mix will increase by 10 percentage points over the course of the predicted period, to reach a projected share of 38% by 2027. Only the renewable energy sector is expected to expand in the electrical generating industry; the proportions of coal, natural gas, nuclear, and oil generation are expected to decline. It is anticipated that the amount of electricity generated worldwide by solar photovoltaic (PV) and wind sources would more than double over the next five years. By 2027, it is projected that this trend would result in wind and solar PV together producing close to 20% of the world's total electricity production. Throughout the course of the projection period, the aforementioned variable technologies are expected to account for 80% of the increase in global renewable energy. As such, further paths for improving power system flexibility must be investigated. Dispatched renewable energy sources including hydropower, bioenergy, geothermal, and concentrated solar power have a vital role in incorporating solar photovoltaic (PV) and wind technologies into global electricity grids, but their growth is still limited (IEA, 2022).