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Optoelectronic devices are emerging as a cornerstone of advanced renewable energy technologies, offering innovative routes for energy harvesting, conversion, and management with high efficiency and versatility. This review summarizes recent advances in the semiconductor materials engineering field, device configurations, and light–matter interaction mechanisms that underpin advanced optoelectronic systems for solar energy harvesting, solar-driven chemical conversion, and smart grid integration, among others. Emphasis is placed on the breakthroughs achieved in the perovskite and hybrid photovoltaics, photoelectrochemical energy conversion, and nanostructured optoelectronic platforms that enable much-increased light absorption, reduced recombination losses, and scalable large-scale fabrications. Moreover, the challenges closely linked with long-term stability, environmental durability and benevolence, and worldwide deployment are critically addressed, together with the emerging opportunities in AI design, tandem device technological solutions, integrated energy systems, and machine learning approaches for optimizing device performance, thermal management, and energy storage capabilities. Finally, the present review concludes by outlining the future research directions that could accelerate the transition toward high-performance, cost-effective, and sustainable optoelectronic solutions responsive to global renewable energy requirements. Full article

Electric Vehicle Charging Stations (EVCSs) have become increasingly important due to the growing penetration of electric vehicles (EVs) and renewable-based power generation. However, challenges such as fluctuating renewable energy availability, increasing charging demand, power losses, operational cost, and charging delays continue to affect overall grid performance and stability. To address these issues, this study proposes a hybrid Goat Optimization Algorithm–Hierarchical Multi-Granularity Interaction Graph Convolutional Network (GOA–HMGIGCN) framework for intelligent smart grid energy management and EV charging coordination. The proposed framework combines the Goat Optimization Algorithm (GOA) for optimal EVCS placement and charging scheduling with the Hierarchical Multi-Granularity Interaction Graph Convolutional Network (HMGIGCN) for forecasting renewable generation, charging demand, and load variations. The framework was implemented and evaluated in MATLAB/Simulink R2024a using the IEEE 14-bus smart grid test system under varying operating conditions. Simulation results demonstrated that the proposed framework achieved superior performance compared with the Coot Optimization Algorithm–Fractional Backpropagation Physics-Informed Neural Network (COA-FBPINN), Dingo Optimization Algorithm–Convolutional Hypergraph Graph Neural Network (DOA-CHGNN), Self-Feedback Feedforward Artificial Neural Network (SFFANN), Deep Neural Network (DNN), and Golden Jackal Optimization–Attention-Based Probabilistic Convolutional Neural Network (GJO-APCNN) techniques by attaining the lowest operational cost of USD 1561, the highest efficiency of 99.2%, the minimum power loss of 10.6 kW, and the shortest charging time of 32 min. In addition, the proposed framework and overall grid reliability, confirming its effectiveness for intelligent renewable-integrated smart grid applications. Full article

The worldwide shift to electric mobility has intensified in recent years owing to heightened apprehensions over greenhouse gas emissions, energy security, and the necessity for sustainable transportation systems. Electric vehicles (EVs) are acknowledged as a viable alternative for diminishing reliance on fossil fuels and enhancing energy efficiency in the transportation sector. While affluent nations have achieved considerable advancements in electric vehicle adoption and charging infrastructure, numerous developing countries still encounter significant technical and infrastructural obstacles that hinder extensive EV integration. In South Africa, these difficulties are exacerbated by ongoing electrical supply limitations, deteriorating transmission and distribution facilities, and recurrent load shedding, which heighten worries about the dependability and stability of the national power grid. The rising adoption of electric vehicles adds extra electrical demands to power systems, especially at the distribution network level, where most of the charging takes place. Disorganized EV charging can substantially modify current load patterns, leading to heightened peak demand, voltage variations, transformer overload, and network congestion. The technical consequences are especially significant in South Africa, where the power grid functions with constricted generation capacity and minimal reserve margins. Various mitigating measures have been suggested to tackle these difficulties, including intelligent charging, demand-side management, time-of-use pricing, and vehicle-to-grid technologies. This paper establishes a basic theoretical framework through an extensive literature review to investigate the technological problems related to electric vehicle adoption in South Africa, while assessing the environmental and economic ramifications for sustainable urban transportation systems. Full article

Greenhouse microclimate regulation is challenging due to nonlinear coupling among temperature, humidity, soil moisture, and light intensity, which limits the effectiveness of conventional threshold-based and PID control strategies under time-varying environmental disturbances. This paper presents a forecast-augmented ensemble control framework that combines Random Forest, Gradient Boosting, and Support Vector Machine classifiers with one-hour-ahead weather forecasts for closed-loop greenhouse microclimate regulation. The proposed system was deployed and validated in a working greenhouse cultivating cucumber (cv. ‘Madora F1’) over 28 consecutive days. Sensor measurements and forecast inputs were processed through a unified preprocessing pipeline, while control actions were generated through majority voting and executed on Raspberry Pi 4B edge hardware with a worst-case inference latency below 18 ms. The proposed framework achieved a temperature RMSE of 0.83 °C during field deployment. For reference, RMSE values of 3.21 °C and 1.94 °C were obtained for the threshold-based and PID baseline controllers, respectively, under the adopted disturbance-consistent evaluation protocol. Compliance rates reached 96.4% for temperature, 94.1% for relative humidity, and 97.2% for soil moisture across 40,320 resampled observation intervals (60 s analysis grid) derived from the original 10 s acquisition stream. Integration of short-term weather forecasts enabled anticipatory irrigation management, reducing irrigation pump operation by 18% without compromising soil-moisture compliance and yielding an estimated annual energy saving of 158 kWh per greenhouse zone. Unlike prediction-oriented greenhouse artificial-intelligence studies, the proposed approach implements a deployable forecast-augmented closed-loop control architecture validated under continuous real-world greenhouse operation. Full article

Data centers have become essential infrastructure for digital services, while their rapidly growing electricity demand makes coordinated workload and power management an important optimization problem. This paper studies the multi-data-center operation problem under time-of-use electricity pricing and formulates it as a multi-data-center mixed-integer nonlinear programming model (MDC-MINLP). The model jointly represents binary task scheduling decisions, including temporal workload shifting and spatial task migration, and continuous power-side variables, including device-level utilization, IT and auxiliary power consumption, energy storage dynamics, grid power procurement, and quality-of-service constraints. The objective is to minimize the total operating cost by integrating electricity purchasing cost, IT operation loss, storage degradation cost, and migration cost. To solve the resulting large-scale discrete–continuous coupled problem, an Adaptive Large Neighborhood Search algorithm with Hierarchical Collaboration (HC-ALNS) is proposed. HC-ALNS reconstructs feasible task action sets, employs a surrogate objective for fast candidate screening, performs accurate power-layer evaluation for selected solutions, and adaptively adjusts search intensity according to convergence behavior. Numerical results show that HC-ALNS reduces the total operating cost by 3.67% and achieves better convergence and solution quality than NSGA-II and PSO. These findings demonstrate that the proposed MDC-MINLP and HC-ALNS provide an effective mathematical optimization framework for coordinated computation–power scheduling. Full article

Towards achieving carbon neutrality, it is important to produce carbon-free hydrogen from renewables at an acceptable cost. At the same time, power retailers that own renewables must manage their imbalances between planned and actual generation. This paper proposes an economically viable carbon-free hydrogen method for such retailers, utilizing both positive imbalances of renewables and electricity from the market with non-fossil certificates. The proposed method enables geographically flexible hydrogen production through the power grid while utilizing renewable imbalances within actual power business operations. This paper develops solutions to an optimization problem that minimizes the hydrogen variable cost and offsets the imbalances using an electrolyzer and a battery while accounting for imbalance uncertainty. The case study in Tokyo, Japan demonstrates that imbalance compensation reduces the hydrogen variable cost by 30%. The minimum levelized cost of hydrogen (LCOH) is approximately 60 JPY/Nm${}^3$ when the electrolyzer operates at a 40% capacity factor. Furthermore, sensitivity analysis of market prices indicates that the LCOH can decline to 50 JPY/Nm${}^3$ under lower price conditions. The results suggest that market-independent cost components, such as wheeling and renewable energy charges and non-fossil certificates, remain major obstacles to further reducing hydrogen costs. Full article

Spatially explicit socioeconomic data are critical for regional analysis, yet they remain scarce at subnational scales in many African contexts. This study presents a transparent and reproducible open-data framework to generate high-resolution gridded Gross Domestic Product (GDP) and derived socioeconomic and energy indicators. The approach combines gridded population and Night-Time Light (NTL) through the LitPop method to downscale provincial GDP to 1 km resolution for the Inkomati-Usuthu Water Management Area (IUWMA) in South Africa. The resulting GDP dataset is subsequently used as a spatial proxy to disaggregate compensation of employees, gross capital formation, fixed capital stock, net exports, gross operational surplus and sectoral Total Final Energy Consumption (TFEC). Results show strong consistency with official provincial GDP totals, with deviations ±0.4% after 2017. In 2024, LitPop allocated 4.26 billion constant 2015 USD to the IUWMA, equivalent to 16% of Mpumalanga’s GDP, compared with 47.3% under area-based allocation and 51.3% under population-based allocation. These differences reveal the strong influence of spatially concentrated industrial and energy-intensive activity. The workflow provides a scalable and replicable solution to generate coherent gridded socioeconomic datasets for WEF Nexus modelling, although estimates remain proxy-based and sensitive to NTL-related biases, particularly the overrepresentation of highly illuminated industrial assets and the underrepresentation of less luminous activities. Full article

Accurate solar irradiance forecasting is important for efficient planning of solar energy systems, renewable energy integration, and data-driven energy management in smart cities. This becomes more essential in regions with limited measured data availability and varying climatic conditions, where reliable forecasting can support urban energy planning and smart grid operation. Pakistan faces a scarcity of available solar data and has varying climatic conditions, which makes it ideal for such a study. This study utilizes nine geographically diverse stations to develop a benchmark framework for direct one-step-ahead hourly solar irradiance forecasting. The dataset was subjected to data preprocessing, feature engineering, and multi-model evaluation. A staged approach was adopted for feature selection, starting from a base model comprising three input variables: extraterrestrial radiation, solar zenith angle, and relative humidity. Features were added in an incremental order, which resulted in an optimized four-variable input set through the addition of a lagged clearness index to the base model. The forecasting models evaluated in this study, using these input variables, were ANN, NAR, NARX, LSTM, GRU, SARIMA, and Prophet. Deep learning models outperformed the other considered approaches, with LSTM showing the best overall benchmark performance with an average RMSE of 92.93 W/m², MAE of 66.56 W/m², and R-Squared of 0.872. The performance trends were broadly consistent across the evaluated stations, indicating stable behaviour within the adopted dataset and experimental setup. The study shows that a compact and physically interpretable input feature set, used with recurrent deep learning models, provides an effective solution for hourly solar irradiance forecasting, especially in locations with varying climatic conditions. The proposed benchmark can support smart city applications related to distributed solar generation, energy-aware urban planning, and intelligent operation of renewable-rich power systems. Full article

Waste-to-energy (WtE) systems are frequently proposed as complementary waste-management strategies; however, their climate performance depends on the interaction between thermodynamic efficiency, material circularity, and electricity-system characteristics. Existing life-cycle assessments generally provide static comparisons between landfill and WtE but rarely identify the operating conditions under which WtE remains environmentally competitive. To address this gap, a parametric life cycle–energy framework was developed by integrating attributional LCA with an analytical energy model capable of evaluating critical efficiency thresholds under varying recovery rates and electricity-grid conditions. Four representative thermoplastics (PET, HDPE, PP, and LDPE) were evaluated using ReCiPe 2016 Midpoint (H) in SimaPro under Mexican electricity conditions ($E{F}_{\mathrm{grid}}=0.444$ kg CO₂eq/kWh). Results indicate that total life-cycle climate impacts are dominated by upstream polymer production, whereas end-of-life management contributes only marginally to overall GWP. Critical-efficiency analysis revealed strong sensitivity to both recovery rate and electricity-grid carbon intensity. For PET, the minimum efficiency required for WtE to outperform landfill increased from 13.1% to 73.5% across the evaluated scenarios, whereas HDPE remained competitive at efficiencies below 1.3%. Monte Carlo simulations (10,000 realizations) further demonstrated that avoided emissions decline systematically with increasing recovery rates, with LDPE exhibiting the highest mean avoided emissions (1735 kg CO₂eq) and PET the lowest (811 kg CO₂eq). These results demonstrate that WtE climate performance is governed primarily by residual waste availability and electricity-system evolution rather than thermodynamic efficiency alone. Consequently, WtE should be interpreted as a transitional residual-waste management strategy whose long-term climate relevance decreases as material circularity and electricity-grid decarbonization advance. Full article

The increasing penetration of renewable energy sources and converter-interfaced loads has intensified the need for fast and reliable grid-support services. Although electric vehicle (EV) battery chargers have emerged as promising resources for Vehicle-to-Grid (V2G) applications, existing solutions typically focus on individual services such as virtual inertia or frequency regulation, while limited attention has been given to the coordinated provision of multiple ancillary services within a unified framework. Furthermore, the use of batteries alone for fast frequency support may accelerate battery degradation due to frequent high-power transients. To address these challenges, this paper proposes a hybrid energy storage-based EV battery charger architecture and a coordinated multi-timescale control strategy capable of simultaneously providing virtual inertia support, long-term frequency regulation, reactive power compensation, and harmonic mitigation. The proposed approach utilizes a DC-link capacitor to deliver fast inertial response while the battery supplies sustained frequency support, thereby reducing battery stress and improving energy management efficiency. An enhanced frequency estimation method based on a phase-locked loop combined with a low-pass filter is also introduced to improve dynamic performance. Simulation results demonstrate the effectiveness of the proposed strategy under various grid disturbances. The system achieves an equivalent virtual inertia constant of approximately 1.85 s and delivers up to 786 W of transient inertial support within 80 ms during frequency events. The enhanced frequency estimation method significantly reduces transient overshoot, while harmonic compensation limits the grid current and voltage total harmonic distortion to 1.50% and 3.23%, respectively. In addition, the controller provides up to 400 VAR of reactive power support during voltage disturbances while maintaining stable battery operation. These results demonstrate that the proposed EV battery charger can function as a multifunctional grid-support resource, enhancing frequency stability, voltage regulation, power quality, and overall V2G capability in future smart grids. Full article

Accurate power load forecasting is essential for efficient electricity grid management, yet capturing cross-variate dependencies in multivariate time series remains a persistent challenge. Recent channel-independent methods based on Transformer and recurrent architectures have achieved strong forecasting performance, but they discard potentially useful information from correlated variates such as weather conditions and neighboring consumption zones. In this paper, we propose RWKV-CVM, a lightweight extension of the RWKV-TS architecture that introduces a trainable Cross-Variate Mixing (CVM) module to selectively incorporate inter-variate information while preserving the linear time complexity of the backbone. The CVM module is a gated, row-stochastic mixing matrix—initialized from the training set absolute Pearson correlations and modulated by a single learned scalar gate that is applied to the normalized input series before patching, adding only 65 trainable parameters to the backbone. We evaluate the method under a single unified harness (three random seeds, consistent normalization, and re-executed DLinear, iTransformer and RWKV-TS baselines) on three settings: the Tetouan city power consumption dataset forecast jointly for all three zones at horizons up to 72 h (including the operationally relevant 24 h day-ahead and 48 h two-day-ahead horizons) and the ETTh1 and Weather benchmarks under a  10 %  few-shot protocol. Averaged over horizons, RWKV-CVM attains the lowest mean MSE on all three datasets (Tetouan all-zone  0 . 0427 , ETTh1  0 . 640 , Weather  0 . 250 ), narrowly ahead of the strongly-tuned baselines and its own RWKV-TS backbone. The advantage is modest, is concentrated at longer horizons, and is selective across target zones; on several individual horizons and in the full-data regime, a baseline is preferable, and we report these cases explicitly. These results indicate that a controlled, lightweight injection of cross-variate information can improve multivariate load forecasting on average without sacrificing computational efficiency. Full article

Resource allocation for cognitive Vehicle-to-Everything (V2X) networks is challenging due to dynamic spectrum sharing, strong interference coupling, and stringent latency constraints for safety-critical Vehicle-to-Vehicle (V2V) traffic. Although recent Multi-Agent Reinforcement Learning (MARL) approaches report promising gains, many evaluations are conducted at limited and fixed network scales, which restricts insights into scalability under dense spectrum reuse. This paper investigates cooperative multi-agent learning for interference-aware and deadline-constrained V2X resource management. We propose a Q-value Transformation (QTRAN)-based value decomposition framework under centralized training with decentralized execution (CTDE) for joint resource-block and power allocation among V2V agents. The proposed approach is implemented in a realistic V2V/V2I simulator incorporating Manhattan grid mobility, fast fading, explicit cross-tier and co-channel interference, and per-link payload/deadline dynamics. Beyond communication-level performance, improved timely delivery of V2V safety messages can support cooperative maneuvering, collision avoidance, platooning, and infrastructure-assisted traffic management. Extensive simulations across varying numbers of V2V agents benchmark QTRAN against independent learning baselines including MARL and centralized single-agent learning (SARL). Results show that QTRAN improves performance compared with the selected learning baselines and enhances the throughput–reliability trade-off under interference-coupled spectrum reuse. For instance, at $N_{\mathrm{V}2\mathrm{V}}=20$, QTRAN achieves a V2V rate of $0.194\pm 0.004$ and a V2I rate of $9.117\pm 0.213$, while reaching a V2V success rate of $0.812\pm 0.017$ with a low Deadline Miss Ratio of $0.001\pm 0.000$. At higher density ($N_{\mathrm{V}2\mathrm{V}}=50$), QTRAN sustains strong reliability (V2V success rate of $0.719\pm 0.006$ and Completion Ratio of $0.716\pm 0.006$) while maintaining competitive infrastructure throughput (V2I rate of $9.251\pm 0.114$). These results indicate that QTRAN effectively captures non-linear interference interactions, enabling coordinated decentralized spectrum and power decisions under the adopted density-based evaluation setting, thereby enhancing V2V reliability and throughput in cognitive Internet of Vehicles. Full article

Industrial control systems (ICS), which manage critical infrastructure such as power grids and water treatment, are increasingly exposed to cyber threats and operational faults as their connectivity to external networks grows. AI-based anomaly detection has emerged as a key defense, yet three limitations restrict its practical deployment: (i) detected anomalies are treated uniformly without distinguishing between transient faults and intentional attacks, hindering tailored incident response; (ii) the trade-off between detection accuracy and the false-positive rate burdens experts with extensive manual triage and delays prompt action; and (iii) prevailing feature-attribution Explainable AI (XAI) techniques such as SHAP and LIME produce fragmented sensor-level explanations and fail to capture correlations among sensors in time-series data, undermining trust in model decisions. To address these gaps, this paper proposes a graph-based deep learning framework that (a) defines anomaly types in terms of the anomalous-sensor ratio measured before and after smoothing—which operationalizes the correlation-maintenance principle that faults keep coupled sensors jointly anomalous while attacks isolate them—enabling explicit separation of faults, attacks, false positives, and false negatives; (b) identifies ambiguous decisions near the detection threshold as candidate false alarms via dynamic threshold smoothing; and (c) provides correlation-aware graph visualizations for intuitive interpretation. Experiments on the Secure Water Treatment (SWaT) dataset center on this post-detection layer: built on a standard graph-based detector (F1-score 0.787 at Top-K = 10) that serves only as the substrate, the categorization separates faults from attacks, and the subsequent ambiguity analysis identifies false negatives with 83% precision and false positives with 73% precision. By separating attacks from faults and surfacing high-likelihood false alarms together with intuitive sensor-correlation explanations, the proposed approach reduces analyst workload and supports more reliable, prioritized incident response in ICS environments. Full article

Accurate forecasting of hourly electricity consumption is critical for smart grid management, energy market operations, national policy planning, and—particularly for import-dependent economies such as Türkiye—energy security. This study presents, to the best of the authors’ knowledge, the first systematic benchmark of four state-of-the-art time series architectures—TimesNet, PatchTST, iTransformer, and Temporal Fusion Transformer (TFT)—conducted specifically on a national-scale Turkish multivariate energy dataset from the Energy Exchange Istanbul (EPİAŞ), covering 72,322 hourly observations across 15 generation, consumption, and market-clearing price variables from January 2018 to April 2026. While benchmark studies of Transformer-based architectures exist on general time-series datasets, no prior work has applied this specific combination of architectures to the EPİAŞ dataset under unified experimental conditions with an explicit energy-security interpretation. All models were trained under standardized preprocessing (StandardScaler), a 24 h lookback window, and systematic hyperparameter optimization. Experimental results demonstrate that iTransformer achieves the best predictive performance (MAE = 521.34 MWh, RMSE = 748.12 MWh, R² = 0.9881, MAPE = 1.34%), followed by TFT (R² = 0.9863) and PatchTST (R² = 0.9844). TimesNet, while the most computationally efficient, achieves an R² of 0.9791. Beyond predictive benchmarking, this study situates the findings within Türkiye’s energy security agenda: the dataset captures fossil fuel dependency, the growing share of domestic renewables, and market-clearing price dynamics shaped by geopolitical shocks, including the Russo–Ukrainian war and evolving EU–Türkiye energy relations. Comprehensive analysis of model architectures, attention mechanisms, temporal feature importance, and computational efficiency is provided. These findings establish a rigorous baseline for deploying modern sequence models in large-scale, real-time national energy forecasting systems that serve both market-efficiency and strategic-energy-autonomy objectives. The results specifically highlight how high-fidelity forecasting can serve as a risk-mitigation tool against geopolitical supply disruptions by quantifying the impact of domestic renewable integration. Full article

Land-use/cover change threatens the ecological integrity of the Miombo region of south-central Africa. In Angola, Miombo ecosystems are of high ecological and socio-economic importance, providing rural populations with woody and non-timber forest products. Fire plays an important role in regional agricultural and silvicultural land-use systems. This study contextualised Copernicus land-cover classes at the regional level to analyse LULC transition pathways and their association with fire occurrence in Central Angola. LULC change was assessed using a post-classification comparison approach combined with pixel-based trajectory analysis. Fire activity was analysed using MODIS-derived ignition points, burned-area data, and a hexagonal-grid aggregation approach. At the same time, spatial clustering was assessed using hot spot analysis based on the Getis-Ord Gi* statistic. Differences in mean fire size among LULC transition classes were tested using the Kruskal–Wallis test followed by Dunn’s post hoc test. The results indicate a gradual reduction in forest cover and conversion to Cultivated Land, associated with the expansion of agricultural frontiers and urban areas. Fire activity was highest in areas affected by LULC conversion, with seasonal patterns varying notably among classes. Mean fire size differed by more than two orders of magnitude among transition types. Overall, fire activity was strongly associated with areas undergoing land-cover transition, highlighting the need to integrate fire management into sustainable land-use policies for long-term Miombo conservation. Full article