The integration of artificial intelligence (AI) is critically transforming the renewable energy sector. This review synthesizes AI's role in optimizing solar and wind energy systems, focusing on power forecasting, system optimization, and predictive maintenance. The research goal was to systematically analyze how diverse AI techniques enhance these critical aspects. Key findings indicate AI's capacity to substantially improve short-term solar irradiance and wind power forecasts (e.g., via SARIMAX, long short-term memory (LSTM), and hybrid deep learning models), dynamically manage energy flow in smart grids and microgrids, optimize maximum power point tracking (MPPT) in photovoltaic (PV) systems, and enable proactive maintenance through anomaly detection in wind turbines using IoT-integrated AI. Key conclusions reveal that AI significantly enhances the efficiency, reliability, and economic viability of solar photovoltaic and wind power generation, offering superior adaptability and predictive capabilities over traditional methods. While AI is important for the global transition to cleaner energy, persistent challenges related to data quality and availability, model interpretability, and cybersecurity must be addressed to fully unlock its potential in practical renewable energy applications.

Solar energy deployment increasingly competes with prime agricultural lands, creating conflicts between energy goals and food security. To resolve these competing demands, our study identified where agrivoltaic systems—combining solar energy and agricultural production on the same land—should be strategically deployed across the Philippines. Using geospatial analysis which integrates terrain suitability, solar photovoltaic (PV) potential, and crop compatibility with shade-tolerant crops, we identified 10.09 million has of cropland suitable for agrivoltaics, representing 81.8% of the nation's agricultural land. Regions in the Mindanao island emerged as premier agrivoltaic deployment zones, combining maximum crop compatibility (15 shade-tolerant crops), high solar PV potential (683-687 MW), and substantial suitable areas (587,000-715,000 has). These findings provide actionable recommendations for strategic agrivoltaic deployment that advances both food security and renewable energy goals in the Philippines simultaneously.

This review examines the integration of climate-smart aquaculture (CSAq) as a strategy to enhance the resilience and sustainability of global aquaculture and coastal agriculture in the face of climate change. CSAq encompasses innovations such as integrated multi-trophic aquaculture (IMTA), genetic advancements, renewable energy integration, and optimized water management, all aimed at minimizing environmental impacts while maintaining productivity. As climate change introduces threats like ocean acidification, temperature fluctuations, and extreme weather events, CSAq offers adaptive solutions critical for preserving marine ecosystems, reducing greenhouse gas emissions, and sustaining food security. The review emphasizes that the successful adoption of CSAq is contingent upon supportive policies, cross-sectoral collaboration, and socio-economic considerations, including gender inclusivity and community involvement. As aquaculture's role in food security continues to grow, CSAq provides a pathway for mitigating climate impacts while promoting sustainable development. This review underscores the necessity of climate-smart approaches for building resilient food systems that can adapt to a changing climate and sustain livelihoods in vulnerable coastal regions.

Achieving a just and accelerated renewable energy (RE) transition in the Philippines requires not only technological innovation but also coherent and cross-sectoral policy alignment. Non-energy policies can facilitate or hinder the RE development. Non-energy policies, particularly those governing land use, permitting, and environmental regulation, and other significantly shape the feasibility of RE deployment. However, the analyses and evidences on implications of the non-energy policies on RE development are scarce, especially in the context of developing countries. This study provides a comprehensive, stakeholder-informed assessment of 43 national-level policy instruments across five domains in the Philippines: Energy Policy and Regulation, Climate Change and Sustainability, Environmental and Natural Resource Conservation, Agriculture and Rural Development, and Land Use and Property Rights. In this study, using a modified Sustainable Development Goals (SDG) interaction framework, stakeholders from academia, government, industry, and non-governmental organizations evaluated each policy's influence on RE development using a seven-point scale. Weighted average (WA) scores were computed to determine whether policies act as enablers or constraints. Results show that energy and climate policies are strongly supportive due to clear mandates and institutional coordination, whereas land governance and agrarian reform policies are viewed as restrictive because of procedural uncertainty and tenure risks. Environmental policies are generally enabling but raise permitting concerns. Divergent stakeholder perceptions underscore the need for inclusive and transparent governance. The study concludes that accelerating the RE transition will depend on harmonizing institutional mandates, reforming land-use frameworks, enabling decentralized systems, and strengthening technical and governance capacity across all sectors.

The growing demand for sustainable electricity in emerging economies necessitates hybrid systems that leverage local renewable resources while remaining economically viable. This study optimizes and evaluates photovoltaic–biogas (PV–BG) hybrid systems for Rosso, Mauritania, through a techno-economic and environmental framework. HOMER Pro was used for baseline modeling, while Grey Wolf Optimizer (GWO) and Whale Optimization Algorithm (WOA) refined both on-grid and off-grid designs. The optimal on-grid configuration—801 kW PV, 100 kW BG generator, and 408 kW converter—achieved a Levelized Cost of Energy (LCOE) of $0.041/kWh, Net Present Cost (NPC) of $1.89 M, Payback Period (PP) of 6.6 years, Internal Rate of Return (IRR) of 14%, and Return on Investment (ROI) of 11%. GWO and WOA further reduced LCOE to $0.038/kWh and $0.036/kWh and NPC to $1.81 M and $1.77 M, shortening PP to 6.4 and 6.1 years. Environmental analysis showed an annual offset of 1,220 tCO2 and a 100% renewable fraction. The results provide a scalable framework for hybrid energy planning, supporting policy development and investment strategies toward low-carbon power systems.

Artificial Intelligence (AI) is transforming the prediction and optimization of advanced energy materials by enabling accurate, scalable modeling beyond traditional methods. This review evaluates recent AI applications—including Graph Neural Networks (GNNs), Convolutional and Recurrent Neural Networks (CNNs, RNNs), tree-based ensembles, and Gaussian Process Regression (GPR)—for forecasting performance metrics such as overpotential, conductivity, capacity, and degradation. GNNs achieved R2 > 0.90 in structure-sensitive tasks; LSTM models predicted battery degradation with <10% error; and tree-based models balanced accuracy (MAE < 0.15 V) with interpretability. GPR excelled in low-data regimes via uncertainty quantification. Hybrid and physics-informed models improved generalizability and data efficiency. While challenges remain in data quality and integration with experiments, emerging strategies like autonomous labs and generative design offer promising advances. This review provides comparative benchmarks and highlights pathways for robust AI-driven materials discovery.

High voltage engineering has evolved rapidly, driven by the growing need for efficient energy transmission and the integration of renewable energy into modern power grids, including urban areas. Innovations such as HVDC systems are central to this transformation, ensuring that grids can handle the increasing complexity and demand for sustainable energy. However, challenges remain, especially when it comes to coordinating insulation in hybrid AC/DC systems and maintaining the resilience of the overall infrastructure. This review looks at how Artificial Intelligence (AI) can help tackle these challenges, focusing on its role in fault detection, predictive maintenance, and improving system reliability. By comparing traditional methods with AI-driven solutions, we highlight how AI can enhance the scalability, efficiency, and adaptability of power systems. With AI, utilities can predict and prevent faults, optimize grid performance, and seamlessly integrate renewable energy sources into both rural and urban environments. Our goal is to provide insights for researchers, industry professionals, and policymakers on how AI can be harnessed to build more sustainable, resilient, and reliable energy systems. The insights shared here aim to help shape the future of power grids, positioning AI as a key player in the transition to cleaner, more efficient energy solutions.

The integration of artificial intelligence (AI) into computational materials science (CMS) has introduced powerful approaches for accelerating the discovery and optimization of advanced energy materials. As energy demands shift toward renewable systems, the development of efficient materials for batteries, fuel cells, and electrocatalysts becomes increasingly critical. This paper systematically reviews recent AI methodologies applied within CMS, particularly those leveraging density functional theory (DFT), molecular dynamics (MD), and kinetic Monte Carlo (KMC) simulations. Emphasis is placed on the use of machine learning (ML) models, including supervised learning, deep learning, and hybrid strategies for property prediction, structure optimization, and inverse design. The review categorizes current applications across key energy technologies and discusses how AI is reshaping material screening and development pipelines. It concludes with an outlook on future directions, highlighting the need for standardized datasets, interpretable models, and physics-informed frameworks to improve predictive accuracy and facilitate AI adoption in practical materials research.

This study assesses the potential of advanced materials, specifically graphene, perovskites, and nanostructured ceramics to enhance the energy efficiency, durability, and environmental resilience of 5G and 6G communication systems deployed in harsh environments. A comparative evaluation was conducted based on electrical conductivity, thermal stability, mechanical strength, optical performance, and corrosion resistance, drawing on recent experimental data and life-cycle analyses. Graphene demonstrates electrical conductivity near 10^8 S/m and thermal conductivity up to 5000 W/m-K, enabling transistors with 200 times higher speeds and coatings reducing corrosion by over 90%. Perovskite-based devices achieve solar cell efficiencies up to 34% and optical modulators operating at 170 Gbps. Nanostructured ceramics offer low dielectric loss and stability above 1000°C, supporting high-frequency operation in challenging conditions. Integrating these materials is projected to extend device lifespans by up to 40% and reduce energy and cooling demands by 30%. These findings indicate that adopting advanced materials can significantly improve the performance and sustainability of next-generation communication infrastructure.

Climate change is intensifying maritime risks, including sea level rise, stronger storms, and disrupted ocean currents—posing threats to coastal infrastructure, navigation, and security. Traditional monitoring systems lack the predictive capabilities and coverage to address these evolving challenges. This paper explores the integration of Artificial Intelligence (AI) and satellite-based Earth observation as a next-generation early warning system (EWS) for maritime security. We assess observed and projected hazard trends, propose an AI-enhanced system architecture, and evaluate readiness in climate-vulnerable geographies. The findings highlight actionable strategies to improve forecasting, risk detection, and climate resilience in coastal and maritime domains.