The global transition toward sustainable energy sources has prompted a surge in the integration of renewable energy systems (RES) into existing power grids. To improve the efficiency, reliability, and economic viability of these systems, the synergistic application of artificial intelligence (AI) methods has emerged as a promising avenue. This study presents a comprehensive review of the current state of research at the intersection of renewable energy and AI, highlighting key methodologies, challenges, and achievements. It covers a spectrum of AI utilizations in optimizing different facets of RES, including resource assessment, energy forecasting, system monitoring, control strategies, and grid integration. Machine learning algorithms, neural networks, and optimization techniques are explored for their role in complex data sets, enhancing predictive capabilities, and dynamically adapting RES. Furthermore, the study discusses the challenges faced in the implementation of AI in RES, such as data variability, model interpretability, and real-time adaptability. The potential benefits of overcoming these challenges include increased energy yield, reduced operational costs, and improved grid stability. The review concludes with an exploration of prospects and emerging trends in the field. Anticipated advancements in AI, such as explainable AI, reinforcement learning, and edge computing, are discussed in the context of their potential impact on optimizing RES. Additionally, the paper envisions the integration of AI-driven solutions into smart grids, decentralized energy systems, and the development of autonomous energy management systems. This investigation provides important insights into the current landscape of AI applications in RES.

Population increase and industrialization has resulted in high energy demand and consumptions, and presently, fossil fuels are the major source of staple energy, supplying 80% of the entire consumption. This has contributed immensely to the greenhouse gas emission and leading to global warming, and as a result of this, there is a tremendous urgency to investigate and improve fresh and renewable energy sources worldwide. One of such renewable energy sources is biogas that is generated by anaerobic fermentation that uses different wastes such as agricultural residues, animal manure, and other organic wastes. During anaerobic digestion, hydrolysis of substrates is regarded as the most crucial stage in the process of biogas generation. However, this process is not always efficient because of the domineering stableness of substrates to enzymatic or bacteria assaults, but substrates’ pretreatment before biogas production will enhance biogas production. The principal objective of pretreatments is to ease the accessibility of the enzymes to the lignin, cellulose, and hemicellulose which leads to degradation of the substrates. Hence, the use of pretreatment for catalysis of lignocellulose substrates is beneficial for the production of cost-efficient and eco-friendly process. In this review, we discussed different pretreatment technologies of hydrolysis and their restrictions. The review has shown that different pretreatments have varying effects on lignin, cellulose, and hemicellulose degradation and biogas yield of different substrate and the choice of pretreatment technique will devolve on the intending final products of the process.

The accelerating pace of climate change poses unprecedented challenges to global ecosystems and human societies. In response, this study reviews the power of Artificial Intelligence (AI) to develop advanced predictive models for assessing the multifaceted impacts of climate change. The study used the PRISMA framework to find, assess, and combine research on using AI in predicting climate change impacts. Integrating AI techniques, such as machine learning algorithms and predictive analytics, into climate modeling provides a robust framework for understanding and projecting the complex dynamics associated with global climate change. These models exhibit a high capacity for data collection, analyzing intricate patterns and integration, including their relationships within the datasets. They enable quick and accurate predictions of future climate scenarios, scenarios testing, historical eventualities, their magnitude, and adaptation. However, challenging issues like data gaps, especially in interconnected systems such as the atmosphere, are associated. Also, AI insight translation into an actionable recommendation recognizable by the policymakers, including ethical usage, is an emerging concern. Therefore, further advances to circumvent these will include the integration of AI with physical models, developing hybrid models, and generating synthetic climatic datasets to enhance data quality and gaps. Also, AI tools are being developed to aid decision-making for policy integration. AI-based predictive modeling is restructuring and bringing reformative change to the understanding of and approach toward climatic change through AI model development. AI guarantees an unfailing plan and a resilient future with sustainable approaches that empower scientists, policymakers, and communities.

Enzymatic hydrolysis of lignocellulose feedstocks has been observed as the rate-limiting stage during anaerobic digestion. This necessitated the need for pretreatment before anaerobic digestion for an effective and efficient process. Therefore, this study investigated the impact of acidic pretreatment on Arachis hypogea shells, and different conditions of H₂SO₄ concentration, exposure time, and autoclave temperature were considered. The substrates were digested for 35 days at a mesophilic temperature to assess the impact of pretreatment on the microstructural organization of the substrate. For the purpose of examining the interactive correlations between the input parameters, response surface methodology (RSM) was used. The result reveals that acidic pretreatment has the strength to disrupt the recalcitrance features of Arachis hypogea shells and make them accessible for microorganisms' activities during anaerobic digestion. In this context, H₂SO₄ with 0.5% v. v⁻¹ for 15 min at an autoclave temperature of 90 °C increases the cumulative biogas and methane released by 13 and 178%, respectively. The model's coefficient of determination (R²) demonstrated that RSM could model the process. Therefore, acidic pretreatment poses a novel means of total energy recovery from lignocellulose feedstock and can be investigated at the industrial scale.