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Research Article
Simulation of the Effects of the Thickness and the Bandgap of the Absorber on the Performance of the Quaternary Thin Film Solar Cell Based on Cu(In,Ga)Se2
Daouda Oubda*,
Sayouba Kabre,
Alain Diasso,
Boureima Ouedraogo,
Marcel Bawindsom Kebre,
Soumaila Ouedraogo,
Boureima Traore Issaka Sankara,
Adama Zongo,
Amidou Barry,
Boureima Sawadogo,
Pindewinde Sawadogo,
François Zougmore
Issue:
Volume 14, Issue 1, March 2026
Pages:
1-8
Received:
19 November 2025
Accepted:
13 January 2026
Published:
29 January 2026
Abstract: In the field of energy transformation, the share of renewable energies continues to grow and gives hope to fight against global warming. In the global electricity mix we have: 15% for hydropower, and 14.5% for other renewables, according to 2022 figures. Among renewable energies, photovoltaic (PV) solar energy is the most promising with very high record yields of around 29% theoretically, 27.3% experimentally for record-breaking PV solar cells in the laboratory and 22% in industrial production for a solar panel. The selenide, gallium, indium and copper (CIGS) sector is very promising, one of its major advantages coming that the fact the quaternary alloy Cu(In,Ga)Se2 is a material with an adjustable bandgap (Eg). The freely available and highly stable one-dimensional solar cell capacities simulation software, is the tool used for the simulation. Digital simulation is an essential tool because it makes it possible to predict the behavior of the solar device and to be able to estimate its performances. The study of effects of the thickness (WCIGS) and the gap of the CIGS absorber with a cadmium sulfide buffer layer of 30 nm shows that current-voltage density characteristic is enormously affected for WCIGS ≤1000 nm. We therefore note a significant decrease in the values of the short-circuit current density (JSC) and the open-circuit voltage (VOC) when WCIGS decreases. These results are explained by a significant reduction in the quantity of incident photons absorbed and an increase in the recombination rate of the charge carriers photogenerated in the absorber. VOC increases and JSC decreases with the increasing of the absorber gap, the increase in VOC is therefore linked to a significant reduction in the recombination rate at the CIGS/Mo interface and inside the space charge region. The maximum efficiency is 26.33% for Eg = 1.45 eV and WCIGS = 3000 nm.
Abstract: In the field of energy transformation, the share of renewable energies continues to grow and gives hope to fight against global warming. In the global electricity mix we have: 15% for hydropower, and 14.5% for other renewables, according to 2022 figures. Among renewable energies, photovoltaic (PV) solar energy is the most promising with very high r...
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Research Article
Short-Term Load Forecasting via Heterogeneous Ensemble Learning: A Study on Cross-Node Accuracy and Stability
Issue:
Volume 14, Issue 1, March 2026
Pages:
9-17
Received:
23 December 2025
Accepted:
14 January 2026
Published:
31 January 2026
Abstract: Short-term load forecasting (STLF) serves as a fundamental basis for the efficient operation of smart grids and energy management systems. Accurate nodal-level load prediction plays a vital role in optimizing power dispatching, reducing operational costs, and enhancing grid security. Variations in user behavior patterns, geographical location, and equipment characteristics among different electricity nodes typically result in load profiles that exhibit pronounced volatility and non-stationarity. Traditional single model forecasting approaches are highly sensitive to data distribution and often struggle to maintain consistently high predictive accuracy across all nodes. Specifically, individual models frequently lack the generalization capability required for diverse load types, leading to significant error variability when applied to nodes with contrasting properties. To address these limitations, this study proposes a highly robust heterogeneous ensemble learning framework. First, a forecasting repository comprising six differentiated base models is constructed to accommodate diversity in model errors. Subsequently, a stacking-based meta-learning strategy is applied to integrate the outputs of the base models, enabling the extraction of multi-dimensional temporal features. To comprehensively validate the effectiveness of the proposed method, extensive comparative experiments are conducted using datasets from ten electricity nodes with diverse distribution characteristics. Experimental results demonstrate that the ensemble model significantly outperforms the base models in terms of accuracy while exhibiting superior stability across various nodes.
Abstract: Short-term load forecasting (STLF) serves as a fundamental basis for the efficient operation of smart grids and energy management systems. Accurate nodal-level load prediction plays a vital role in optimizing power dispatching, reducing operational costs, and enhancing grid security. Variations in user behavior patterns, geographical location, and ...
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Research Article
Sensitivity Analysis of a Hybrid Renewable Energy System in an Oil Facility Using HOMER
Ukwu Alphonsus Obiabuchi*
,
Odutola Toyin Olabisi
Issue:
Volume 14, Issue 1, March 2026
Pages:
18-26
Received:
13 January 2026
Accepted:
26 January 2026
Published:
6 February 2026
Abstract: The increasing demand for reliable and cost-effective energy in oil production facilities has increased the need for optimized Hybrid Renewable Energy Systems (HRES). This study evaluates the technical and economic performance of a hybrid power system for an offshore oil facility using the Hybrid Optimization of Multiple Energy Resources (HOMER) software. The proposed system integrates solar photovoltaic (PV), diesel generators, battery storage, and wind energy. A two-dimensional sensitivity analysis was conducted by varying diesel fuel prices between $0.50–$1.00 per litre and inflation rates between 15–25% to assess their impact on system optimization and economic indicators. Simulation results show that the optimal configuration consists of 25.8 kW solar PV, a 50-kW diesel generator, 15 battery units, and a 22.6 kW converter, supplying the facility over a 20-year project lifetime. The Total Net Present Cost (NPC) increases significantly with rising fuel prices and inflation rates. At lower fuel prices and inflation rates, the PV/Diesel/Battery system is the most economical option, while higher inflation rates favour a PV/Diesel/Wind/Battery configuration. Spider plot analysis indicates that inflation rate has a stronger influence on NPC than fuel price, highlighting the importance of macroeconomic stability in long-term energy planning. These findings provide practical insights for energy planners and policymakers seeking sustainable and resilient power solutions for oil and gas facilities.
Abstract: The increasing demand for reliable and cost-effective energy in oil production facilities has increased the need for optimized Hybrid Renewable Energy Systems (HRES). This study evaluates the technical and economic performance of a hybrid power system for an offshore oil facility using the Hybrid Optimization of Multiple Energy Resources (HOMER) so...
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Research Article
Explainable Machine Learning for Well Integrity and Casing Failure Risk with Sparse Failure Labels
John Lander Ichenwo*,
Ogwu Philip
Issue:
Volume 14, Issue 1, March 2026
Pages:
27-33
Received:
13 January 2026
Accepted:
5 February 2026
Published:
24 February 2026
DOI:
10.11648/j.ajee.20261401.14
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Abstract: The integrity of the well is critical to efficient and safe oil and gas operations because few records exist of casing failure, and conditions in the downhole are complicated. In this study, an explainable machine learning (XML) framework is introduced to estimate the risk of casing failures in limited labelled wells. Which is based on measurements of pressure, temperature, annular pressure, cement bond quality and downhole vibration but integrates semi-supervised learning with gradient-boosted trees to predict the likelihood of failure. SHAP (Shapley Additive Explanations) values gives explanations on the feature level, and these values can be used to determine how operational parameters can contribute to risk predictions. Experimentation with synthetic and field-derived datasets shows that the XML framework has an accuracy of 91%, a precision of 0.87, a recall of 0.89, and a F1-score of 0.88. The critical predictors such as pressure differences in the annulus, low bond integrity of cement, and high vibration were quantitatively related with high risk of failures, whereas the risk scoring at the time gave a premonition to the known failures 2-6 months before the recorded failures. The improvement in the prediction performance and interpretability of the framework is evident when it is compared to the baseline models such as logistic regression and standard random forest. In low failure label environments, the technique fills the gap between risk assessment based on data and operational decision making by integrating the strong prediction with actionable information that enables operators to focus more on tracking, preventive actions, and to save on time on non-productive activities.
Abstract: The integrity of the well is critical to efficient and safe oil and gas operations because few records exist of casing failure, and conditions in the downhole are complicated. In this study, an explainable machine learning (XML) framework is introduced to estimate the risk of casing failures in limited labelled wells. Which is based on measurements...
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