Machine Learning–Based Prediction of Daily Solar Radiation to Support Renewable Energy Development in Coastal Regions

Reliable estimation of surface solar radiation is essential for climate analysis and solar energy planning, particularly in data-limited regions such as coastal Africa. This study investigates the long-term variability of surface solar radiation and evaluates the performance of machine learning models for its prediction using a comprehensive reanalysis dataset spanning 1940–2024. Five radiation components—net surface solar radiation (SSR), clear-sky net radiation (SSRC), downward surface solar radiation (SSRD), clear-sky downward radiation (SSRDC), and total surface radiation (TSR)—were analyzed to quantify the influence of atmospheric attenuation caused by clouds, aerosols, and water vapor. Five machine learning algorithms—Linear Regression (LR), Gradient Boosting (GB), Random Forest (RF), k-Nearest Neighbours (KNN), and Artificial Neural Network (ANN)—were implemented and evaluated using train–test split, k-fold cross-validation, and leave-one-out validation. The results reveal strong interannual and multi-decadal variability in solar radiation, with clear-sky radiation consistently exceeding all-sky radiation, confirming the dominant role of atmospheric modulation in the region. Among the tested models, Linear Regression achieved near-perfect predictive performance (R²≈ 1.0) with the lowest error statistics, indicating that surface solar radiation over coastal Africa is largely governed by linear radiative processes. Gradient Boosting and Random Forest also demonstrated high accuracy (R² > 0.98), while the Artificial Neural Network showed poor generalization due to overfitting. The findings demonstrate that computationally efficient and physically interpretable machine learning models can reliably estimate long-term solar radiation in coastal Africa. This provides a robust scientific basis for solar resource assessment, photovoltaic system design, and climate-resilient renewable energy planning across the region.Citation: Umoh, M., Evans, U., Akpan, S., Otene, S., & Olanrewaju, A. (2026). Machine Learning–Based Prediction of Daily Solar Radiation to Support Renewable Energy Development in Coastal Regions. Trends in Renewable Energy, 12(1), 20-32. doi:http://dx.doi.org/10.17737/tre.2026.12.1.0019

Renewable Energy Revolution: Transforming Africa’s Energy Landscape through Solar, Wind, and Hydropower

This study examines how the plentiful solar, wind, and hydroelectric resources in Africa are transforming the continent's energy landscape. Africa faces significant challenges in achieving energy access and sustainability due to the growth of its population and urbanization. This analysis analyzes the transformational capacity of renewable energy, emphasizing pioneering initiatives like Morocco's Noor Ouarzazate Solar Complex and Kenya's Lake Turkana Wind Project. We examine the economic advantages of decentralized energy systems, such as mini-grids and pay-as-you-go solar solutions, which have empowered millions and invigorated local economies. Progress in hybrid systems and energy storage technologies is essential for improving grid stability and dependability. The success of this revolution depends on strong legislative frameworks, new financial structures, and regional collaboration to address infrastructural deficiencies and regulatory obstacles. This assessment highlights the need for inclusive strategies that include local people and tackle environmental issues related to large-scale projects. Africa is positioned to lead in renewable energy, underscoring the critical need for collaboration among governments, corporate sectors, and foreign partners to facilitate this transition. By using its extensive renewable resources, Africa may attain energy security, economic development, and environmental sustainability, therefore fostering a resilient future. Citation: Eyime, E., & Ushie, O. (2025). Renewable Energy Revolution: Transforming Africa’s Energy Landscape through Solar, Wind, and Hydropower. Trends in Renewable Energy, 11(2), 155-200. doi:http://dx.doi.org/10.17737/tre.2025.11.2.0018

The Impact of Operating Frequencies and Neural Network Training Program Properties on the Performance of Neural Network Topology Generator Methodology

Until now, Neural Network Topology Generator Methodology (NNTGM) has been theoretically proposed so that its generated overhead low-voltage broadband over power lines topologies (NNTGM OV LV BPL topologies) may populate the existing OV LV BPL topology classes. With reference to the OV LV BPL topology class maps, which are defined by the graphical combination of ACA and RMS-DS of the OV LV BPL topologies, and the NNTGM OV LV BPL topology footprints for given indicative OV LV BPL topologies, the impact on the relative position and the size of the NNTGM OV LV BPL topology footprints has been assessed for a number of factors that affect the preparation of the Topology Identification Methodology (TIM) OV LV BPL topology database being used during the NNTGM operation. In this companion paper, the effect of the operating frequencies and the Neural Network (NN) training program properties on the relative position and the size of the NNTGM OV LV BPL topology footprints is here examined. The effect study is supported by suitable Graphical Performance Indicators (GPIs).Citation: Lazaropoulos, A. (2025). The Impact of Operating Frequencies and Neural Network Training Program Properties on the Performance of Neural Network Topology Generator Methodology. Trends in Renewable Energy, 11(2), 237-254. doi:http://dx.doi.org/10.17737/tre.2025.11.2.0019

Performance Variability of Polycrystalline Photovoltaic Modules under Monthly Climatic Fluctuations in Calabar, Nigeria

This study presents a high-resolution, in-situ analysis of the monthly performance of a domestic polycrystalline photovoltaic (PV) module operating within Calabar’s humid monsoon climate, translating empirical field observations into practical design thresholds for residential applications and system installers. Using a digital plane-of-array solar irradiance meter in conjunction with an intelligent MPPT tracker, voltage, current, and power were monitored at 30-minute intervals between 06:00 and 18:00 from July to September. The analysis reveals a stable voltage plateau once plane-of-array solar irradiance exceeds approximately 200 W m⁻², corresponding to near-standard test condition (STC) voltages across all months. In contrast, achieving manufacturer-rated current during August–September would require irradiance levels exceeding 1000 W m⁻², which are seldom observed under real outdoor conditions. July exhibited the highest output current and power, while September recorded the lowest, despite August having the fewest sunshine hours. Notably, output power remained approximately uniform across the 0–450 W m⁻² irradiance range, irrespective of month. At irradiance levels above approximately 400 W m⁻², conversion efficiency follows the order July > August > September, with the module’s rated maximum power appearing unattainable in September under the observed operating conditions.Significance/Novelty: (i) Provides location-specific, month-resolved operating thresholds (≥200 W m⁻² for voltage stability; >1000 W m⁻² required to approach rated current in wetter months), bridging the gap between laboratory specifications and real-world tropical performance; (ii) identifies counterintuitive seasonal behavior, wherein greater sunshine duration does not necessarily yield higher output, thereby informing climate-responsive sizing of storage and inverter systems; and (iii) establishes actionable heuristics—uniform power response under low-to-moderate irradiance and month-dependent efficiency—that reduce performance uncertainty, mitigate premature system abandonment, and promote reliable PV deployment for energy access and net-zero transitions in southern Nigeria.Citation: Ogbulezie, J., Nwokolo, S., & Njok, A. (2025). Performance Variability of Polycrystalline Photovoltaic Modules under Monthly Climatic Fluctuations in Calabar, Nigeria. Trends in Renewable Energy, 12(1), 1-19. doi:http://dx.doi.org/10.17737/tre.2026.12.1.0019

Enhancing Cybersecurity in Energy Infrastructure: Strategies for Safeguarding Critical Systems in the Digital Age

In the digital age, energy infrastructure faces unprecedented cybersecurity challenges that threaten the stability and reliability of critical systems. This paper explores the current threat landscape, detailing prevalent cyber threats such as malware, ransomware, and phishing that target energy systems. It examines the technical, organizational, and regulatory challenges in securing these infrastructures, highlighting issues like legacy systems, lack of cybersecurity awareness, and stringent compliance requirements. The paper proposes comprehensive strategies for enhancing cybersecurity, emphasizing the implementation of advanced technologies such as artificial intelligence, machine learning, and blockchain. Best practices, including regular security audits, incident response planning, and employee training, are also discussed. Furthermore, the importance of collaborative efforts, such as public-private partnerships and information sharing networks, is underscored. The paper concludes with recommendations for energy organizations to strengthen their cybersecurity posture, ensuring the protection of critical systems and the continuity of operations in the face of evolving cyber threats. Citation: Ajayi, O., Alozie, C., & Abieba, O. (2025). Enhancing Cybersecurity in Energy Infrastructure: Strategies for Safeguarding Critical Systems in the Digital Age. Trends in Renewable Energy, 11(2), 201-212. doi:http://dx.doi.org/10.17737/tre.2025.11.2.0019

Design Optimization of Vortex-Induced Vibration Bladeless Wind Turbines for Urban Energy Harvesting

Urban environments require innovative solutions for clean energy generation, as conventional wind turbines are often limited by space and noise constraints. Vortex-Induced Vibration (VIV) bladeless wind turbines present a promising alternative by converting oscillatory motion into electricity without rotating blades. This study investigates the influence of mast height, upper diameter, and lower diameter on turbine flexibility and performance under the low wind speeds typical of urban settings. Computational fluid dynamics and static structural analysis in ANSYS, combined with Response Surface Methodology (RSM) and Central Composite Design (CCD), were used to develop an optimization model aimed at maximizing mast deflection—a critical factor in energy harvesting efficiency. Results reveal that mast height and upper diameter significantly influence deformation, while lower diameter has a relatively minor impact. The optimal configuration achieved a mast deflection of 9.15 mm, validating the model’s predictive accuracy. These findings provide practical design insights for enhancing bladeless wind turbine performance in urban settings, paving the way for more sustainable and space-efficient renewable energy solutions.Citation: Kwong, L., & Han, D. (2025). Design Optimization of Vortex-Induced Vibration Bladeless Wind Turbines for Urban Energy Harvesting. Trends in Renewable Energy, 11(2), 255-279. doi:http://dx.doi.org/10.17737/tre.2025.11.2.0019

Multi-Parameter Based Models for Estimating Global Solar Radiation in Selected Locations in Ebonyi State, Southeastern Nigeria

This study aims to develop hybrid empirical models for estimating global solar radiation in selected locations across Ebonyi State, Nigeria, to enhance photovoltaic energy generation and support climate change mitigation and adaptation. The research is designed to create empirical models for calibrating and modeling global solar radiation using meteorological parameters at Alex Ekwueme Federal University Ndufu-Alike, Ebonyi State University Abakaliki and Akanu Ibiam Federal Polytechnic Unwana, all in Ebonyi State, Southeastern Nigeria. The long term monthly mean daily global solar radiation on the horizontal surface, sunshine hours, relative humidity, minimum and maximum temperature at 2 m height for the period of 1984-2019 for the selected stations were obtained from the National Aeronautics and Space Administration (NASA) atmospheric science data centre.  A multi-parameter based model was used to estimate the global solar radiation in each of these locations using Angstrom-Prescott-Page Model. Model performance was evaluated using statistical metrics, including Mean Bias Error (MBE), Root Mean Square Error (RMSE), and Mean Percentage Error (MPE). Additionally, the correlation coefficient (r) and coefficient of determination (r2) were calculated. Results indicate that the developed empirical models demonstrate a high level of accuracy in estimating daily global solar radiation. Comparisons with existing models in the literature show that the locally calibrated models perform better on monthly and yearly timescales. Therefore, these models can be applied for solar radiation forecasting across Ebonyi State. However, routine recalibration is recommended, as climate variability over time may affect model stability and performance.Citation: Amadi, S. O., Eze, I. A., Enyi, V. S., Nwokolo, S. C., & Kalu, P. N. (2025). Multi-Parameter Based Models for Estimating Global Solar Radiation in Selected Locations in Ebonyi State, Southeastern Nigeria. Trends in Renewable Energy, 11(1), 213-236. doi:http://dx.doi.org/10.17737/tre.2025.11.2.0019

From Policy to Practice: Evaluating the Role of Private-Sector Champions Like Elon Musk in Shaping Trump's 2.0 Climate Agenda

This paper explores the prospective influence of private-sector leaders, particularly Elon Musk, on the formulation of climate and energy policy in a possible second term of President Donald Trump. Historically, the goals of the Trump administration have been viewed as opposed to environmental advocacy; yet, this analysis explores the potential for public-private partnerships that reconcile economic objectives with climate-positive results. Musk's enterprises—Tesla, SpaceX, and SolarCity—are transforming clean energy, and his impact on federal climate policies may initiate a new phase of environmentally sustainable economic development within conservative administration. This study analyzes case studies where Musk has collaborated with public authorities to tackle environmental and energy issues, highlighting practical strategies for partnership. Potential synergies are seen in sectors including renewable energy, electric vehicle infrastructure, and sustainability-oriented manufacturing, underscoring their alignment with Trump's economic plan. Additionally, the article examines the policy mechanisms—tax incentives, regulatory reforms, and investments in clean technology—that may encourage private sector participation in a nationally endorsed climate agenda. This article advocates for a worldview in which leaders like Musk advance environmental progress by harmonizing innovation with conservative regulatory frameworks while preserving traditional economic objectives. Ultimately, it advocates for a revolutionary public-private partnership paradigm that utilizes private sector expertise to tackle climate challenges. This viewpoint enhances the current dialogue on sustainable development and the changing function of private enterprises in public policy, proposing a future where economic growth and environmental stewardship coexist within U.S. federal climate policy.Citation: Nwokolo, S. (2024). From Policy to Practice: Evaluating the Role of Private-Sector Champions Like Elon Musk in Shaping Trump's 2.0 Climate Agenda. Trends in Renewable Energy, 11(1), 122-154. doi:http://dx.doi.org/10.17737/tre.2025.11.1.0019

Evaluation of PV-based Buck-Boost and SEPIC Converters for EV Charging Applications

In recent decades, environmental issues have become an area of greatest concern due to changes in global climate conditions. The transportation sector is a major contributor to carbon dioxide emissions, accounting for more than 22.9% of total carbon dioxide emissions. At present, most vehicles run on gasoline/diesel as fuel which is unsustainable and unviable as fossil fuels produce carbon emissions and fuel costs are rising. To address these issues, electric vehicles (EVs) offer an attractive solution as alternative to internal combustion engine vehicles that use electricity as an energy source. It is logical to use renewable energy to charge vehicles, which makes renewable energy an end-to-end clean energy source. In electric vehicles, energy conversion plays an important role. In the energy conversion process, alternating current (AC) can be converted to direct current (DC), or direct current can be converted to alternating current. In EV fast charging applications, DC-to-DC conversion is used, which requires DC-to-DC converters. In this paper, a detailed evaluation of the Buck-Boost and Single-Ended Primary Inductance Converters (SEPIC) with PV as input is analyzed for EV charging applications to make it end-to-end clean energy. For this purpose, a 5-by-5 PV system with a Buck-Boost, SEPIC converters with particle swarm optimization technique is considered, which is simulated in a MATLAB/SIMULINK environment. The simulation results showed that the ripples in output are minimal in SEPIC which supports the smooth and efficient charging of EV battery. Citation: K, J., & Chengaiah, C. (2024). Evaluation of PV-based Buck-Boost and SEPIC Converters for EV Charging Applications. Trends in Renewable Energy, 10(2), 159-169. doi:http://dx.doi.org/10.17737/tre.2024.10.2.0016

Development Status and Outlook of Hydrogen Internal Combustion Engine

Hydrogen energy is one of the best energy carriers for achieving carbon peak and carbon neutrality, with the characteristics of high energy and no pollution. The hydrogen internal combustion engine is one of the important forms of hydrogen energy utilization, with the significant advantages of high efficiency, high reliability, low cost and low emissions. In this paper, the characteristics of hydrogen internal combustion engines and hydrogen fuel cells were compared, and the industrialization prospects of hydrogen energy utilization in the future were analyzed. Focusing on the hydrogen internal combustion engine technology system, a comprehensive analysis was conducted on the technical issues and technical progress in hydrogen storage, combustion, NOx emissions, etc. of hydrogen internal combustion engines.Citation: Liu, M. (2024). Development Status and Outlook of Hydrogen Internal Combustion Engine. Trends in Renewable Energy, 10(3), 257-265. doi:http://dx.doi.org/10.17737/tre.2024.10.3.0017