Trends in Renewable Energy
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Machine Learning–Based Prediction of Daily Solar Radiation to Support Renewable Energy Development in Coastal Regions
No full textReliable 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
Multi-Parameter Based Models for Estimating Global Solar Radiation in Selected Locations in Ebonyi State, Southeastern Nigeria
Get PDFThis 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
Renewable Energy Revolution: Transforming Africa’s Energy Landscape through Solar, Wind, and Hydropower
Get PDFThis 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
Performance Variability of Polycrystalline Photovoltaic Modules under Monthly Climatic Fluctuations in Calabar, Nigeria
Get PDFThis 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
Get PDFIn 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
Get PDFUrban 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
The Impact of Operating Frequencies and Neural Network Training Program Properties on the Performance of Neural Network Topology Generator Methodology
Get PDFUntil 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
Forecasting CO2 Emissions from Libya’s Transport Sector
Get PDFThis paper presents an innovative approach to forecast carbon dioxide (CO2) emissions from the transport sector in Libya. The method combines machine learning algorithms with historical data and future estimates. The research built a model that took into account factors such as population growth, rates of car ownership, patterns of fuel consumption and government regulations in order to provide an accurate forecast of carbon dioxide (CO2) emissions over the next decade based on the Global Change Assessment Model (GCAM). The authors used a variety of statistical time series models to forecast future CO2 emissions from Libya's transportation sector. These models included the exponential smoothing model (ESM) and the autoregressive integrated moving average (ARIMA). The ARIMA model outperformed the ESM model, achieving an R2 of 0.931 and a root mean square error (RMSE) of 1.040 Mt CO2. The results of the study found that CO2 emissions from Libya's transport sector could increase by 27.98% and 57.99% in 2030 and 2050, respectively. The study proposed six transportation theories to reduce CO2 emissions from Africa's and Libya's transport sectors. The identified factors encompass price systems, land use planning, eco-driving, electric automobiles, bicycle infrastructure, and telecommuting. The authors also examined the needs to reduce CO2 emissions from Libya’s transport sector in order to meet the International Energy Agency’s ambitious targets for reducing CO2 emissions from the global transport sector. These needs arise due to increasing urbanization, population growth, underinvestment in public transportation infrastructure, and the increasing incidence and severity of heat waves. Additionally, hypothetical scenarios are presented to demonstrate the importance of further reducing CO2 emissions from these sectors to match the projections of global change assessment models.Citation: Eyime, E., & Ben, U. (2024). Forecasting CO2 emissions from Libya’s transport sector. Trends in Renewable Energy, 11(1), 1-23. doi:http://dx.doi.org/10.17737/tre.2025.11.1.0018
Assessment of Temporal Trend in Surface Air Temperatures across Some Selected Eco-Climatic Zones in Nigeria
Get PDFTemporal trends in surface air temperatures across some selected eco-climatic zones in Nigeria from 1981 to 2018 were assessed using the Merra-2 reanalysis dataset. A total of 15 stations spread across the eco-climatic zones in Nigeria were used for this study. The Mann-Kendall (M-K) trend test was used to detect direction, significance, coefficients of time trends, while the linear regression and the Sen’s slope trend tests were used to estimate the trend magnitudes. The M-K trend test showed that the surface air maximum temperature of 14 stations had monotonic increasing trends, of which 13 stations were statistically significant at the 0.01 significance level, and 1 station was statistically significant at the 0.05 significance level. However, the M-K trend test also showed that surface air minimum temperature for all the 15 stations (representing 100%), showed monotonic upward trends, statistically significant at the 0.01 significance level. The Sen's slope and linear trend tests showed higher trend magnitudes at most stations, particularly stations in the Guinea-wooded, Sudan and Sahel savannas. The estimated mean trend magnitudes of maximum and minimum air surface temperatures increased by approximately 0.035°C/year and 0.036°C/year, respectively. The estimated mean air surface temperature increased by approximately 0.036°C/year and approximately 1.4°C for Nigeria over the 38-year period. The study then presents a linear trend projection of mean air surface temperature increase in Nigeria of approximately 4.3°C by 2100. This is 0.2°C below maximum levels and within the range of approximately 1.5 to 4.5°C that global air surface temperature is projected to rise by 2100 in the Intergovernmental Panel on Climate Change (IPCC) 2007 report. The M-K and linear trend tests were fully consistent with the standardized time series anomaly plots. Mean annual values of the air surface temperatures showed latitudinal dependence. The manifestation of significant long-term trends at high confidence levels in the air surface temperatures over the period, provides a clear evidence that the climate of Nigeria is witnessing a possible human-induced radiative forcing and a strong tendency for the occurrences of climate-related extreme events and their resulting adverse implications. Citation: KING, L.E., Udo, S.O., Ewona, I.O., Amadi, S.O., Ebong, E.D., & Umoh, M.D. (2024). Assessment of Temporal Trend in Surface Air Temperatures across Some Selected Eco-Climatic Zones in Nigeria. Trends in Renewable Energy, 10, 132-158. doi:http://dx.doi.org/10.17737/tre.2024.10.1.0016
Evaluating the Impact of Renewable Energy Integration on Air Quality: A Study of Pollutant Reduction in an Urban city of Calabar
Get PDFThe characterization of air quality parameters was carried out in the coastal city of Calabar with the aim of reducing air pollutants in the atmosphere. Both mobile and stationary measurements were obtained. Mobile data were used for estimating air quality index and creating air quality map. The results show that the average concentration of ozone (O3), carbon monoxide (CO), sulfur dioxide (SO2) and nitrogen Oxides (NOx) was 0.34, 4.52, 0.53 and 0.96 ppm, respectively. The air quality index determined for each station showed that 82% of the stations were classified as “marginally polluted,” 14% were classified as “good,” and the remaining 4% were classified as “unhealthy” according to the U.S. air quality standards. Correlation analysis showed that wind speed had the highest correlation with SO2, R = -0.72, while temperature had a high correlation with ozone, R = -0.68. The 2016 polar plots show that CO sources are located in the south and southeast, NOx sources are located in the south and southwest, SO2 sources are located in the southwest, and O3 sources are located in the southeast. The 2017 polar plots show that CO sources are located in the northeast, NOx sources are located in the northwest, SO2 sources are located in the northeast, and O3 sources are located in the southwest. Citation: Udo, S., Umoh, M., & Ewona, I. (2024). Evaluating the Impact of Renewable Energy Integration on Air Quality: A Study of Pollutant Reduction in an Urban city of Calabar. Trends in Renewable Energy, 11(1), 24-51. doi:http://dx.doi.org/10.17737/tre.2025.11.1.0018