Thermoelectric materials provide a solid-state solution for converting waste heat directly into usable electricity, offering a promising sustainable energy solution. However, to achieve higher efficiency in commercially viable thermoelectric devices for energy management, optimizing the interdependent electrical and thermal transport parameters remains a bottleneck. In this review, we comprehensively discussed the fundamental transport mechanisms governing thermoelectric materials and the recent advances in electronic and phonon engineering strategies developed to improve thermoelectric performance, including band engineering, phonon-transport manipulation, defect chemistry, entropy stabilization, and hierarchical microstructural design. We also discussed the intrinsically low-thermal and Phonon-Glass Electron-Crystal (PGEC) Materials. These strategies would help to design a high-efficiency thermoelectric material for sustainable energy management.
The demand for vehicles, coupled with the need to analyse emissions and efficiency on slopes, has prompted the need to assess the impact of terrain on the engine efficiency of electric vehicles (EVs) and their operations. This study aims to assess the operational efficiency of EVs in varied settings or on uneven terrain. The real-world performance of these vehicles involves many conditions in which they are expected to traverse inclined terrain, affecting their energy expenditure and operational efficiency. This study aims to analyse the impact of road inclination on EVs' energy expenditure and the energy used in regenerative braking. This study, based on Terrain’s Digital Surface Models, Geographic Information Systems (GIS), Machine Learning (ML) technologies, and the Analysis of Moment Structures (AMOS) software, assesses the impact of terrain, vehicle characteristics, and climatological factors on the energy performance of EVs. The study analyses energy usage patterns and recovery behaviour in Amman city streets through an analysis of road slopes alongside speed and vehicle weight effects under different traffic and weather conditions, and air conditioning (A/C) and lighting impacts on energy usage. This study also claims that climbing significantly increases energy expenditure, and descending allows for energy recovery through the brakes. EVs can regain 40% of the electricity used when driven downhill. Factors such as the vehicle's weight, vehicle aerodynamic design, and road slope affect energy consumption. Although at high speeds, aerodynamic design has a minor effect. The efficiency of the recovery systems will also decline as external weather conditions increase energy consumption and enhance rolling resistance. Traffic delays also increase energy consumption because of the repetitive acceleration and deceleration. The boosting technique allowed us to develop models to predict energy consumption and regeneration with high precision. The generated boosting models for energy consumption prediction surpass the R-squared regression model of 0.86, as the regression boosting model achieves 0.95. Gradient boosting also demonstrates a good capacity to analyse the nonlinear interactions of diverse variables. For energy vehicle designs, the route research slope and environmental vehicle system designs will provide energy efficiency solutions. The research work provides a starting point to revolutionize energy vehicle technologies and transport systems within a larger scope.
Artificial intelligence (AI) has become a fundamental technology for improving building energy management through data-driven prediction, intelligent optimization, and adaptive control. Although numerous reviews have examined individual AI techniques, a comprehensive application-oriented synthesis of recent advances remains limited. This study presents a systematic review of AI-driven building energy management research published between 2022 and 2024. Following a PRISMA-based screening procedure, approximately 150 peer-reviewed studies were selected and analyzed using a unified classification framework based on AI methodologies, application domains, building types, optimization objectives, datasets, and evaluation metrics. The quantitative analysis shows that residential buildings accounted for approximately 45% of the reviewed studies, reflecting the increasing availability of residential energy datasets and smart home applications, whereas research on office buildings exhibited a declining trend during the selected period. Machine learning and deep learning remained the dominant techniques for energy prediction and forecasting, while reinforcement learning and metaheuristic optimization algorithms were increasingly adopted for intelligent control and multi-objective optimization. Among optimization methods, NSGA-based algorithms appeared slightly more frequently than PSO-based approaches, highlighting the growing emphasis on balancing energy efficiency, thermal comfort, operational cost, and environmental performance. The review further demonstrates a clear transition from isolated prediction models toward integrated AI frameworks that combine forecasting, optimization, and real-time control. Despite these advances, several challenges continue to hinder practical implementation, including limited benchmark datasets, insufficient model transferability across buildings and climates, high computational complexity, limited interoperability with existing building management systems, and the scarcity of large-scale real-world validation studies. By integrating quantitative trend analysis, an application-based classification, a comparative evaluation of AI methodologies, and a critical assessment of current research gaps, this review provides practical guidance for selecting AI techniques and identifies promising directions for the next generation of intelligent, explainable, and scalable building energy management systems.
The building sector is one of the most energy-intensive sectors globally, and photovoltaic (PV) energy is widely adopted to meet residential energy demand in a low-carbon manner. However, the mismatch between PV production and household demand often leads to periods of surplus energy. This study aims to evaluate the potential of hydrogen-based energy systems to capitalize on this excess energy. To achieve this, a model that generates consistent, continuous, and diverse electrical and thermal load profiles for low-carbon residential buildings is coupled with a multi-energy system simulation. The simulation computes the performance of hydrogen components for different hydrogen utilization (Power-to-Power and Power-to-Gas) and energy mix configurations. The systems' energy, economic, and emission performance are analyzed using a multi-objective optimization process to evaluate their competitiveness and identify optimal sizing strategies. The results show that system performance varies significantly depending on the criteria, usage, and energy mix of the electrical grid. For Power-to-Power applications (using hydrogen to produce electricity and heat), the system excels in providing renewable energy but is not cost-competitive with the French electricity grid, nor does it significantly reduce emissions. However, it proves competitive in terms of life-cycle emissions considering most European energy mixes. For Power-to-Gas applications (producing hydrogen for sale), the hydrogen production is not economically competitive without subsidies but shows potential for reducing emissions, especially in carbon-intensive sectors like transport and industry.
Fuel cell technology is key in global energy transition within the hydrogen economy, with versatility in applications spanning transportation, industry, and stationary power generation. Nonetheless, high costs remain a significant barrier to widespread adoption, requiring rigorous techno-economic analyses to assess their commercial feasibility. This study reviews 151 techno-economic studies on fuel cell applications published between 2020 and 2024, identifying methodological trends and key enablers for commercialization. The reviewed studies cover diverse applications, including fuel cell-based integrated system, stand-alone fuel cell systems (6%), hybrid renewable energy systems (HRES) (21%), and transport-related applications (23%), fuel cell component (1%), with integrated system being the most studied (50%). A variety of identified methodological frameworks are developed to address different purposes, categorized into six groups. The most frequently used economic metrics are LCOE (31%), payback period (12%), and net present cost (NPC) (11%). The analysis highlights three key enablers for improving the economic feasibility of fuel cell applications: (1) Technological innovations, such as system optimization, hybridization, integration, and technical parameter improvements; (2) fuel sources, including hydrogen production, fuelling infrastructure, and alternative fuels; (3) government policy, including carbon pricing, renewable energy incentives, and government subsidies. A SWOT analysis identifies several future research directions, including targeted cost analysis of innovative materials, cost projections, and strategies to reduce reliance on government financial support. Aligned with the objectives of SDG-7 and SDG-13, this review provides actionable insights for stakeholders aiming to overcome economic barriers and accelerate the market adoption of fuel cell applications.
This paper proposes a novel ensemble-based Adaptive Neuro-Fuzzy Inference System (ANFIS) strategy for the Energy Management System (EMS) of Fuel Cell Hybrid Electric Vehicles (FCHEVs). Unlike conventional single-controller architectures, the proposed framework integrates multiple ANFIS models trained on diverse driving patterns, dynamically fused via a real-time, State of Charge (SOC)-dependent weighting mechanism. This adaptive approach allows the controller to effectively respond to load fluctuations, thereby enhancing system efficiency and resilience. To ensure robustness, the method is validated through comprehensive MATLAB/Simulink simulations under five distinct driving cycles: WLTP, NEDC, UDDS, Palermo, and Birmingham. Furthermore, the strategy explicitly incorporates semi-empirical degradation profiles for both the Proton Exchange Membrane Fuel Cell (PEMFC) and the lithium-ion battery. Simulation results demonstrate significant improvements in component longevity and charge sustenance. Specifically, the proposed strategy maintained a significantly higher final battery SOC compared to the non-adaptive baseline (improving final SOC from 0.485 to 0.719 under the Birmingham cycle). Most notably, the EMS successfully extends the fuel cell lifespan by approximately 14–24% in urban driving scenarios (Palermo and Birmingham). It achieves more than a twofold increase (107% extension) under WLTP conditions, with only a marginal increase in raw hydrogen consumption. By unifying adaptive learning with health-aware control, this work introduces a scalable EMS framework suitable for real-world operations. To the authors’ knowledge, this is the first study to combine ensemble ANFIS control with SOC-based weighting and integrated degradation modeling, offering a promising pathway toward durable electric mobility.
Frequency Response (FR) is critical for mitigating frequency fluctuations and maintaining stability in power systems, particularly as modern power systems integrate more renewable energy. This manuscript addresses the challenges of increased frequency variability and decreased FR capabilities. Focusing on three-region interconnected power systems, we examine the limitations of traditional FR analysis methods, comparing simulation and analytical methods, and emphasize the need for Regional Frequency Response (RFR) analytical modeling. We propose a regional active power-frequency model incorporating Fast Frequency Response (FFR) resources, leading to the development of a System Frequency Response (SFR) analytical model capable of resolving FR at the system’s Center of Inertia (COI). This model is further extended to a three-region framework, accounting for transmission line impacts and providing a three-RFR analytical model that offers solutions for FR in any region of the system. Case studies with modified WSCC test system and real-world provincial power system data show that the proposed three-RFR analytical model, with the consideration of FFR resources, effectively captures the Frequency Spatial Distribution Characteristics (FSDC) across regions within milliseconds, while reducing calculation errors of the analytical method by more than tenfold, thereby enhancing efficiency and accuracy in FR analysis. This work lays a foundation for improved frequency stability in modern power systems.
The high penetration of renewable energy sources (RESs) improves the sustainability of distribution-level energy systems, but their uncertain output creates operational challenges for coupled electricity and natural gas networks. To address this issue, this paper proposes a stochastic network-constrained peer-to-peer (P2P) energy-sharing framework for an integrated energy system (IES). The proposed framework coordinates PRs, flexible consumers, fixed loads, photovoltaic units, wind turbines (WNs), micro gas turbines (MGTs), power-to-gas (P2G) units, energy storage systems, and gas compressors (GRs) under distribution system operator supervision. Uncertainties in wind speed, solar irradiance, electricity demand, and natural gas demand are represented by 1000 generated scenarios, reduced using Kantorovich distance, and statistically validated through the T-test. The resulting mixed-integer nonlinear programming problem is solved using an outer approximation/equality relaxation/augmented penalty decomposition method. The framework is tested on the IEEE 33-bus distribution system coupled with a 15-node natural gas network. Numerical results show that the proposed method reduces PRs’ and flexible consumers’ operating cost by about 25%, increases the revenues of micro MGTs, WNs, and photovoltaic units by about 140%, 40%, and 80%, respectively, and decreases the GR supply cost by 10.3% compared with the benchmark without P2P trading. The proposed method also maintains voltage and gas-pressure feasibility and reduces solving time by about 61% compared with DICOPT.
This study aims to ensure the continuity of supplying the critical loads in electrical power systems through mobile energy storage systems (ESSs) powered by renewable energy sources, and to benchmark their performance against stationary alternatives. An integrated planning framework is developed to couple the electric network with road-transport logistics, extending conventional lifecycle costing to include both electrical network infrastructure and the costs of battery mobility. To enable realistic planning and operational analysis, a multi-regional power system planning and operation model and a battery transport and logistics model is jointly formulated and solved to represent the behavior of mobile and stationary ESSs within the power system. The empirical focus is on a real-world example of northwest and western Iran, evaluating mobile photovoltaic supported ESS setups versus stationary configurations for sustaining 50–100 kW critical loads during grid outages. The assessment accounts for seasonal resource variability, deployment windows, setup times, and inverter/battery constraints across four scenarios, and complements these analyses with projections for fixed solar farms in 2030, 2040, and 2050 under rising solar penetration. Results indicate that mobile systems deliver rapid, relocatable security for critical loads, whereas stationary assets provide scalable, planned robustness over longer horizons; together, they constitute a complementary pathway to reliable, low-carbon electricity supply.
According to the Paris Agreement, the transition from fossil fuels requires large-scale deployment of renewable energy, with wind energy being regarded as a critical component. The success of wind energy projects is highly dependent on the selection of the appropriate sites, because it directly influences the technical feasibility and long-term sustainability. The selection of a wind farm site is mandatory for the proper use of the wind resource potential. Many studies use decision-making methods like Analytic Hierarchy Process and Multi-Criteria Decision Making, often coupled with Geographic Information Systems for the wind farm site selection process. However, these methods focus mainly on expert judgement but rarely include statistical validation of technical parameters. The study focuses on a technique that uses review methodology and factor analysis was suggested to identify and evaluate technical parameters for the selection of the wind farm site. A systematic review of articles published from 2014 to 2025 was used to identify all relevant parameters. From the identified parameters, a factor analysis was performed on meteorological mast data to identify the most influential technical parameters. The study found that the wind speed potential had a significantly high centrality score and composite ranking score, followed by wind variability, wind direction features, and atmospheric conditions.
As wind turbines scale beyond 15 MW and blades exceed 100 m in length, ensuring structural integrity and operational reliability becomes increasingly complex. Accelerated development cycles and intensifying global market competition, driven by the emergence of new turbine manufacturers and design concepts, particularly from outside Europe, further heighten the need for systematic, risk-informed technology qualification (TQ). These trends introduce uncertainty by compressing the time available for design validation and by requiring project bids to be made on the basis of turbine models that may still be under development, often with unproven performance or limited field data. This paper presents a comprehensive TQ methodology for very large wind turbine blades, integrating a full Failure Modes, Effects, and Criticality Analysis (FMECA) with a tailored qualification plan aligned to DNV-RP-A203. The FMECA identifies both conventional and scale-induced failure modes, with a particular focus on leading edge erosion, spar cap failure, adhesive debonding, lightning damage, and, aeroelastic and flow-induced instabilities. Each high- and very-high-risk failure mode is mapped to specific qualification activities - such as simulation, laboratory testing, field trials, and in-service monitoring - creating a traceable risk mitigation pathway. The study serves as a reference for future blade developments, incorporating cross-sector insights from aerospace, offshore energy, and nuclear qualification practices to strengthen the methodology. Through focusing qualification efforts on the most critical risks and combining model-based analysis with targeted physical evidence, the proposed framework supports faster, safer certification of next-generation rotor technologies, enabling confident deployment of ultra-large blades in a competitive, rapidly evolving global wind energy market.
Extreme weather events and climate change increasingly affect the performance and reliability of renewable energy systems, posing significant challenges to the global clean energy transition. Despite the growing body of research on climate impacts across individual renewable-energy technologies, current knowledge remains fragmented across regions, technologies, and assessment methodologies, highlighting the need for a comprehensive synthesis of renewable-energy droughts and their future evolution under climate change. This critical review examines how climate variability and extreme events influence solar, wind, and hydropower systems, with particular emphasis on renewable-energy droughts and the indicators used to assess them, including the standardized renewable energy production index and capacity factor-based thresholds (e.g., 10% and 5%). Regionally, Europe experiences frequent winter wind droughts, with extreme events reducing energy production by more than 50% in southern regions, while solar droughts may affect up to 24% of days annually in northern Europe. In China, autumn wind droughts can persist for up to 15 days, and solar shortages may affect up to 63% of winter periods, increasing risks to grid stability. North Africa also exhibits pronounced wind and solar drought vulnerability, whereas hydropower systems worldwide are increasingly affected by hydrological droughts and altered river-flow regimes. Hybrid renewable-energy systems, energy storage, and stronger grid interconnections can substantially improve resilience to renewable-energy droughts. The review further shows that renewable-energy drought risks generally intensify under higher-emission climate scenarios, underscoring the need for standardized assessment frameworks, integrated climate-resilient planning, and greater consideration of socio-economic factors to support adaptation and future energy-system resilience.
Electric vehicles (EVs) and energy storage systems are prone to critical safety hazards associated with lithium-ion battery failures. The current fault detection tools have a fundamental trade-off: Model-based approaches cannot support aging-dependent dynamics, whereas deep learning methods generate too many false alarms and cannot estimate uncertainty of the predictions. To overcome these shortcomings, this paper proposes a novel hybrid framework integrating Adaptive Data-Driven Kalman Filtering (ADKF) with Transformer-based deep learning. The key innovation is a two-way, continuous feedback loop between the filter and the network. This is different from the earlier hybrid methods where the filter and the network work side by side without sharing the information, or the filter was used to prepare the network data in one direction only. The proposed transformer model learns adaptive state-space models from operational data, continuously refining the ADKF's predictions, while the ADKF simultaneously feeds its innovation sequence back to the transformer, providing optimal Bayesian filtering that reduces noise-induced false positives and quantifies prediction uncertainty, which is the principal shortcoming of transformer-only models. This two-way feedback loop is what allows the proposed transformer-based deep learning model to learn state-transition and observation mappings directly from data, supplying the Kalman filter with accurate, adaptive process models that evolve with battery aging rather than requiring manual re-tuning. When transformers detect faults, they can automatically adjust Kalman filter parameters, maintaining estimation accuracy during both normal and faulty operations. The proposed framework addresses five critical fault categories: two SOC-related anomaly types (drift, imbalance), SOH degradation (capacitor degradation), temperature faults (thermal anomalies), coupled multi-parameter faults, and sensor integrity issues. A comprehensive comparative study of F1-score, accuracy, error and time of fault detection with existing methods (CNN, LSTM, standalone transformer model) have been carried out to prove the efficacy of the proposed method. Compared with traditional method and other deep learning approaches, the proposed systems achieves 97.5% F1-score (10-fold cross-validated mean, 97.3% on the single held-out test split, 38% earlier fault detection, 55% fewer false positives, high-accuracy state estimation (SOC error <2%, SOH error <3%), 1.8x faster inference than ensemble and 67–71% faster adaptation to changing conditions. The proposed method not only achieves state-of-the-art performance, it also maintains computational feasibility for embedded Battery management systems by advancing safety and reliability in critical energy storage applications.
A dual-mode water-based thermal energy storage (TES) prototype, referred to as ELSA (Sensible and Latent Energy Storage System) and designed for differentiated seasonal building applications is presented. It operates as a liquid thermocline storage in winter mode (hot water thermocline storage) and as a water/ice phase change material (PCM) storage in summer mode (cold storage). The present paper study focuses exclusively on the water thermocline mode of the ELSA prototype, with main concern on the validation of the embedded instrumentation and the characterization of the thermocline behavior. Experimental results demonstrate typical thermocline temperature profiles and good radial and angular temperature homogeneity, supporting a predominantly one-dimensional axial thermocline behavior. Global energy balance, comparing inlet/outlet energy transfer with internally reconstructed energy, shows very good agreement, with discrepancies generally below 5%, thereby validating the embedded instrumentation (220 thermocouples). Experimental results highlight a three-steps process of thermocline: formation near the distributor, establishment and transport in the tank, with linear thickening rate. A study of the influence of mass flow rate indicates only limited impact on thermocline behavior and thickening (less than 8% of variations of thermocline thickness), except at low flow rate/high thermal losses (+40%). The influence of this operating temperature difference could not be reliably quantified because of regulation issues during the experiments.
This study presents a combined experimental and modeling investigation of cellulose-based evaporative cooling pads, focusing on the synergistic effects of inlet air psychrometric conditions and air velocity on cooling performance. Unlike conventional studies that examine temperature, humidity, and airflow separately, this work systematically analyzes their interactions and impact on air cooling and humidification. Experiments were conducted under controlled variations of dry-bulb temperature, relative humidity, water temperature, air velocity, and water flow rate. The results show that the maximum cooling efficiency (up to 90–95%) is achieved under moderate air temperature conditions (28–34 °C), inlet water temperature ranging from 22 to 25 °C, low to medium relative humidity (30–50%), and within the studied air velocity range of 0.27–0.53 m/s. where coupled sensible and latent heat transfer mechanisms are optimized. Air velocity enhances mass transfer by renewing the air boundary layer, while favorable psychrometric conditions drive efficient evaporation. Both humidification and temperature drop are highly sensitive to the interplay of these parameters, highlighting the importance of coordinated optimization. A one-dimensional heat and mass transfer model was developed using experimentally identified heat and mass transfer coefficients. The model demonstrates reliable performance prediction under the investigated inlet humidity conditions, with MAE and RMSE values below 7% for cooling efficiency, and shows excellent agreement with measured outlet air temperatures, with MAE and RMSE values below 1 °C, indicating very low pointwise errors. Overall, the model accurately reproduces the system behavior within the studied operating range. These findings provide practical guidance for designing and operating high-efficiency evaporative cooling systems, emphasizing the importance of simultaneously optimizing air psychrometric conditions and airflow to maximize cooling and humidity control in hot and dry climates.
Hydrothermal carbonization (HTC) has been proposed as a promising technology for the treatment of sewage sludge containing microplastics; however, the fate and transformation of persistent polymers during the process are not sufficiently understood. This study investigates the hydrothermal transformation of polyethene terephthalate (PET) and polystyrene (PS) microplastics at concentrations representative of sewage sludge processing, using distilled water as the sole reaction medium at 220 °C for 1, 2, and 3 h. A comprehensive multi-analytical approach was employed, including thermogravimetric analysis, scanning electron microscopy, Fourier-transform infrared spectroscopy, and Raman microspectroscopy for solid products. In addition, spectroscopic analysis of process liquid for pH, conductivity, total organic carbon, chemical oxygen demand, and phenol concentration was performed to evaluate the release of soluble organic polymers' degradation products. The results revealed different transformation pathways for PS and PET under hydrothermal conditions relevant to sewage sludge processing. PS retained its aromatic structure, showing moderate band broadening and a stable single-step thermal degradation profile (Tmax ∼415–417 °C) with negligible residue (<1%). In contrast, PET exhibited pronounced ester and aromatic band modifications, reduced thermal stability, a shift in the degradation onset from ∼402 °C to 246–284 °C, multi-step decomposition, and a decrease in the residue from 12.7% to ∼1.3% after hydrothermal conversion. Process water analyses further highlighted polymer-specific behaviour in organic load with increasing HTC temperature.
This paper presents new insight into assessing the efficiency of grid-forming control methods with respect to electrical distance and point of interconnection. Large loads such as modern data centers introduce operational challenges due to high power demand, rapid ramp rates, and stringent fault ride-through requirements. These characteristics influence the performance of grid-forming technologies, including virtual synchronous machines (VSMs) and droop-based controllers, under varying system conditions and transmission distances. The study examines these effects in a system predominantly supplied by synchronous generators using the IEEE 9-bus test network, with emphasis on long transmission lines. System stability as a function of electrical distance is assessed in terms of voltage regulation, frequency response, and fault ride-through capability, and is compared with conventional synchronous-generation-based behavior. Simulation results indicate that synchronous generators maintain superior fault ride-through performance, reducing disturbance impact by up to a factor of 20, while grid-forming converters provide enhanced voltage support and improved post-disturbance recovery through fast reactive-power control. The influence of grid topology and electrical distance on the effectiveness of voltage-control strategies is also analyzed. The findings highlight the benefits and limitations of grid-forming control in long-distance transmission systems and underscore the need for advanced control strategies to ensure stability as electrical distance increases.
Electricity theft remains a critical issue in many developing nations, contributing to non-technical losses and making essential financial strain for utility providers. To overcome this problem, this paper presents an integrated deep learning pipeline that combines complementary preprocessing, feature transformation, and feature fusion techniques to improve detection of electricity theft. The framework provides a structured pipeline in which missing values are addressed using the piecewise cubic Hermite interpolating polynomial method, and class imbalance is handled by the synthetic minority oversampling approach. By evaluating different approaches based on multiple criteria, principal component analysis is employed for dimensionality reduction. The one-dimensional electricity consumption data is transformed into two-dimensional: temporal domain features using the Gramian angular field and spatial domain features using the maximal overlap discrete wavelet transform. To extract significant structural information for anomaly detection, separate two-dimensional convolutional neural networks are applied to each domain. The determined features are fused and passed using a deep neural network classifier. Experimental outcomes show the strength of the proposed framework, achieving an accuracy of 98.96% and an area under the curve of 99.59%. This demonstrates the model’s capability to detect fraudulent consumption using advanced preprocessing, transformation, and deep learning frameworks, contributing to Sustainable Development Goal 7 by enhancing energy efficiency and reducing non-technical losses in smart grids.
Present solar photovoltaic (PV) systems incorporate a maximum power point tracking (MPPT) controller to harness the maximum power from solar arrays. The majority of the controllers perform well in the ideal conditions. However, due to the intermittent nature of the solar PVs, irradiance and temperature fluctuate rapidly, and the MPPT controller usually faces difficulties in tracking the maximum power point (MPP). In addition to this, steady-state error, oscillations during rapid variation of irradiance and temperature, and ripples in the PV voltage remain pertinent when the traditional MPPT controllers are applied. Based on recent research, an artificial intelligent method such as Adaptive Neuro-Fuzzy Inference System (ANFIS) along with a PID controller improves the tracking speed and reduces steady state error and oscillations during abrupt variation of the atmospheric conditions. However, tuning flexibility is limited to only modifying controller gains; hence lack of adaptability with ANFIS introduces computational complexity and lower transient performance. In this paper, an ANFIS-based fractional order proportional-integral (FOPI) controller is proposed to enhance the dynamic behavior of the MPPT using four Kyocera solar PV panels, configured as two panels in series and two in parallel. To validate the performance of the proposed controller, its performance is compared against a conventional incremental conductance (INC) method and an ANFIS-based proportional-integral (PI) controller. The proposed ANFIS-based FOPI controller exhibits the highest performance, as shown by the lowest root mean square error (RMSE) of 0.0475, integral time-weighted absolute error (ITAE) of 19.51, ripples in voltage of 0.38%, and transient instability of 0.01 s, which are significant when compared to previous studies. Tracking and energy extraction efficiency of the proposed controller are calculated as 99.23% and 97.7% respectively. Moreover, partial shading, uncertainty and sensitivity analysis are performed to investigate the dynamic performance of the controller under adverse conditions. The successful simulation demonstrates that the ANFIS-based FOPI controller performs better than the conventional INC controller and ANFIS PI controller.
This study examines how the rollback of electric vehicle (EV) incentive policies following the transition to a new U.S. administration in 2025 affected stock market efficiency, using the Semi-Strong Efficient Market Hypothesis (SEMH) within Kingdon’s Multiple Streams Framework (MSF). MSF conceptualizes the policy shock by identifying the misalignment of the problem, policy, and political streams, while SEMH assesses how this public information was incorporated into EV stock prices. Abnormal returns (AR) and cumulative abnormal returns (CAR) are evaluated using the Market Model (MM), the Capital Asset Pricing Model (CAPM), and the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model. Tesla, a pure EV manufacturer, showed statistically significant AR after the rollback announcement. The extended window indicates this inefficiency persisted, creating exploitable opportunities for investors and signaling a violation of the SEMH. Conversely, Ford and GM exhibited no abnormal returns, suggesting efficient price adjustment and greater resilience due to diversified production lines. These results demonstrate that political instability in energy policy weakens regulatory credibility, disrupts clean-energy transitions, and reduces investor confidence. Stable and transparent policy design is therefore essential to support private investment, sustain technological innovation, and advance progress toward the Sustainable Development Goals (SDGs). This study is among the first to link political science and green finance to apply Kingdon's MSF within an event-study, revealing how disrupted policy streams can cause financial market inefficiencies. Future research could extend the MSF-SEMH framework to international EV markets and green-technology sectors to test if similar policy reversals cause comparable market inefficiencies.
