Journal of Energy Research and Reviews

The Next Generation of Power-grid Resilience: Long-horizon AI Planning under Deep Climate Uncertainty

2026-10-23T07:09:41+00:00

Power systems face climate-driven extremes, compound hazards, rapid electrification and renewable integration, and infrastructure decisions whose consequences persist for decades. These conditions often cannot be represented by historical patterns or well-defined probability distributions. Artificial intelligence (AI) is increasingly applied to forecasting, outage prediction, asset management, optimisation and resilience planning, yet most applications remain prediction-centric, whereas long-horizon grid planning takes place under deep uncertainty. This critical narrative review integrates literature from power-systems engineering, machine learning, climate-risk assessment, infrastructure resilience and decision-making under deep uncertainty to examine how AI can move from forecasting toward robust and adaptive planning. We identify a prediction–planning gap created by two mismatches: a temporal mismatch between the horizons over which AI is predictively competent and those over which infrastructure decisions operate, and an uncertainty mismatch between the stationarity assumptions of learned models and the non-stationary, deeply uncertain conditions of future grids. Distribution shift, rare extremes, model disagreement and optimisation fragility limit prediction-centric approaches. AI offers greater long-term value through scenario exploration, surrogate-accelerated simulation, stress testing, vulnerability discovery, robust multi-objective optimisation and adaptive decision support. We operationalise this shift through an AI-Enabled Adaptive Resilience Planning Framework of six iterative stages, propose a decision-performance evaluation scheme (robustness, regret, adaptability, option value, recovery, equity and tail cost), and set out eight research priorities. Next-generation grid resilience should judge AI not by how accurately it predicts one future, but by how well it supports robust, equitable, explainable and adaptable decisions across many plausible futures.

Application of Life Cycle Assessment to Reduce Energy Use and Carbon Emissions of Industrial Drying Process

2026-10-23T12:27:08+00:00

Industrial drying is an energy-intensive operation, and its environmental performance depends on the drying technology, energy source, material properties and boundaries used for assessment. This review examines the application of life cycle assessment (LCA) to energy consumption, carbon emissions and other environmental impacts associated with drying processes. Relevant publications were identified through a Web of Science search and examined using bibliometric analysis, keyword co-occurrence networks and comparisons of published applications. The review covers drying of fruits and vegetables, grains, coal, textiles, wood, sewage sludge, porous nanoscale materials, bioactive products, and paper and paperboard. Across these applications, LCA provides a framework for identifying environmental hotspots and assessing trade-offs between drying technologies, including alternatives based on heat pumps, renewable energy and process integration. The reviewed studies indicate that reductions in operating energy demand do not necessarily produce improvements across all environmental impact categories, particularly when equipment manufacture, upstream inputs and regional electricity supplies are considered. The literature also shows uneven coverage among material classes and differences in functional units, system boundaries, databases and impact assessment methods. These differences restrict direct comparisons between reported results. Greater consistency in assessment practice, improved inventory data and closer integration of experimental evidence with industrial operating conditions could strengthen the use of LCA in identifying opportunities for energy conservation and emission reduction in industrial drying process.

Forecasting Renewable Energy Shares Using ARIMA and a Grey-Box Model: Evidence from Four Developed and Developing Economies

2026-10-06T09:26:38+00:00

This study evaluates ARIMA and hybrid Grey-Box models for forecasting the share of renewable energy in total final energy consumption in Angola, Canada, France, and Nigeria. Annual World Bank data from 1990 to 2021 were analysed using exploratory analysis, stationarity testing, autocorrelation diagnostics, model-selection criteria, and forecast-error measures. Country-specific ARIMA models were fitted, and Random Forest models were then applied to lagged ARIMA residuals to capture additional nonlinear structure. The Grey-Box model produced lower reported mean squared error and mean absolute percentage error than the selected ARIMA model for all four countries. Forecasts for 2022–2026 indicated increasing renewable-energy shares in Angola, Canada, and France, while Nigeria showed comparatively little change. These findings suggest that residual-based integration of statistical and machine-learning models may improve short-term forecasting performance across heterogeneous national energy systems. Interpretation should remain cautious because the analysis is based on short annual time series and does not include additional explanatory variables.

Optimisation of Biogas Production from Plantain (Musa paradisiaca) Lignocellulosic Residues using Anaerobic Digestion

2026-10-12T09:53:39+00:00

The increasing generation of plantain (Musa paradisiaca) residues presents both a waste-management challenge and an opportunity for renewable-energy recovery. This study investigated the anaerobic digestion of plantain residues for biogas production and optimised the process using response surface methodology. Plantain peel and mixed plantain residues were characterised for physicochemical and microbiological properties before digestion. The effects of substrate concentration, inoculum concentration, pH and digestion time on biogas production were evaluated using a response surface experimental design. Biogas volume, methane concentration, volatile-solids reduction and other process parameters were determined during digestion. The results showed that the plantain residues contained substantial biodegradable organic matter, with volatile solids of 88.64–90.21% of total solids and carbon-to-nitrogen ratios of 37.00–39.41. The highest cumulative biogas production was obtained from the mixed plantain residue supplemented with cow-dung inoculum. The corresponding biogas yield was 0.61 L/g volatile solids, with a methane concentration of 62.30% and volatile-solids reduction of 74.52%. COD, BOD₅, total solids and organic carbon were reduced by 76.78%, 79.57%, 65.06% and 58.11%, respectively. The response surface model was statistically significant (p < 0.001), with an R² of 0.9970 and a non-significant lack of fit (p = 0.975). The predicted optimum conditions were a substrate concentration of 9.2%, inoculum concentration of 18.4%, pH of 6.72 and digestion time of 24.6 days, giving a predicted biogas yield of 0.63 L/g volatile solids. Experimental validation produced 0.61 L/g volatile solids, representing a prediction error of 3.17%. The findings demonstrate the potential of plantain residues as substrates for renewable biogas production and provide optimised conditions for improving digestion efficiency.