Due to the growing emphasis on sustainability and digitalization in today's business context, there are newly grounds to consider the need to address developing Digital Innovation (DI) alongside green practices to improve firm sustainability. The current research seeks to investigate from DI, which consists of include Artificial Intelligence and Internet of Things, as well as green practices which consists of renewable energy solutions and waste reductions, the constructs of corporate sustainability and business performance. To aid in understanding this relationship, a survey of 200 individuals across the diverse industries was collected then analyzed using descriptive statistics, as well as using correlation and regression approaches. Results indicated the mean of DI (2.9800), Green Practices (2.9793), and Sustainability Outcomes (3.0742) all reflected low levels of implementation. Correlation and regression analyses indicated weak development relationships reflecting of statistically insignificant relationships (p > 0.05), indicating that leadership appear to be potentially more influential that other observable factors towards sustainability performance. The research provides insight on how little emerged from the interaction data between digital and green purposes, and puts forth additional implications for future research on how cultural and regulative strategies uniquely influence the change-based nature of sustainable corporate performance.
Coastal environments are increasingly threatened by emerging contaminants; however, integrated assessments of multiple pollutant classes remain scarce. This study provides a simultaneous investigation of microplastics (MPs) and total petroleum hydrocarbons (TPHs) in seawater and sediments along the southwestern coast of the Caspian Sea. Samples were collected from fourteen coastal stations during the dry season. MPs were characterized by type, size, shape, color, and polymer composition using microscopy and Raman spectroscopy, while TPHs were quantified using GC-MS. The results revealed MP abundances ranging from 0.05 to 2.35 particles L-1 in water and from 2.01 to 24.13 particles kg-1 in sediment. Fibers and fragments in the 100-500 & micro;m size class were dominant, with polyethylene (PE) and polyethylene terephthalate (PET) identified as the most prevalent polymers. TPH concentrations varied from not detected to 20.152 & micro;g L-1 in water and from 8.37 to 1835.29 & micro;g kg-1 in sediment. Spatial analysis showed no overlap between the contamination hotspots of MPs and TPHs, indicating distinct sources and transport pathways. This concurrent assessment underscores a complex contamination profile in the region and highlights potential ecological risks from both physical and chemical stressors. The distinct spatial patterns provide critical information for designing targeted pollution control measures.
This study investigates the seasonal variability of meteorological feature importance in forecasting Global Horizontal Irradiance (GHI) using machine learning (ML) and deep learning models. High-resolution solar and meteorological data from NREL's NSRDB (24.25 degrees N, 45.34 degrees E, 740 m) were seasonally partitioned into winter, spring, summer, and autumn. Feature selection was conducted using Pearson correlation (threshold 0.25), followed by dimensionality reduction through Principal Component Analysis (PCA). Six ML models XGBoost, LightGBM, Random Forest, SVR, MLP, and LSTM were trained on the processed datasets, and SHAP analysis was used to interpret feature contributions. The results revealed that clear-sky irradiance parameters (GHI, DNI, DHI) consistently dominated GHI prediction (correlation >0.95; SHAP >10(-)& sup1;), while features like temperature and relative humidity varied across seasons. Wind direction, though weakly correlated, showed increased influence in winter. PCA enhanced model stability in spring and winter but slightly reduced accuracy during periods of high irradiance variability. Overall, XGBoost and Random Forest models provided the most accurate and reliable forecasts across seasons.
As patterns of energy usage become more complicated, smart, data-driven strategies are needed for effective forecasting and management. In order to optimise decision support systems in the energy industry, this study suggests a hybrid data mining framework that combines Extreme Gradient Boosting (XGBoost) with K-Means clustering. The model is intended to increase the precision and interpretability of energy usage forecasts while detecting discrete consumption behaviour clusters by utilising the publicly accessible UCI Individual Household Electric Power Consumption dataset. The suggested XGBoost + K-Means model performs noticeably better than conventional models like Linear Regression, Decision Tree, and Random Forest, according to a comparative analysis. It achieves a high R2 score of 0.91, a mean absolute error (MAE) of 39.7 Wh, and aroot mean square error (RMSE) of 49.6 Wh. Furthermore, evaluation criteria including F1-score, precision, and recall attest to the model's resilience and appropriateness for real-time applications. These results demonstrate how hybrid machine learning techniques can convert energy data into useful insights, which will ultimately help develop more intelligent and sustainable energy management plans.
The rapid growth of the photovoltaic device market is imperative for meeting global sustainability goals, such as reducing chlorofluorocarbon emissions to improve air quality, fulfilling the increasing public energy demands, and ultimately lowering the cost of electricity production. The development and application of advanced energy materials are gaining significant attention within the scientific and industrial communities. In this context, recent research has focused on exploring various photophysical mechanisms that can be integrated into photovoltaic devices to achieve conversion efficiencies theoretically surpassing the Shockley-Queisser limit. This limit, which represents the maximum theoretical efficiency for single-junction solar cells, has long been considered a fundamental constraint. However, innovative strategies such as multi-junction architectures, down-conversion and up-conversion layers, hot carrier extraction, and plasmonic enhancements are being investigated to overcome these limitations. It is noteworthy that approximately 55% of incident photon energy is lost, predominantly due to sub-bandgap losses, where photons have insufficient energy to excite electrons across the bandgap, and thermalization losses, where excess photon energy above the bandgap is dissipated as heat. Addressing these intrinsic loss mechanisms is crucial for the development of next-generation photovoltaic technologies capable of delivering higher efficiencies and supporting a sustainable energy future.
This paper presents an innovative Edge AI framework to detect nonlinear energy usage patterns in real-time with the application to Smart Grid infrastructures. Designed for use on Edge devices where there is limited processing power but required to be highly analytic error-free, the proposed framework is lightweight. The authors developed GBOCLE-Energy, an efficient Anomaly Detector Model based on advanced Gradient Boosting methods specifically for low latency and real-time energy consumption data analysis. This model uses compressed forms of Light Gradient Boosting and One-Class SVM Algorithms to discover temporal, contextual, and relational anomalies across multiple Nodes on the Smart Grid network. Additionally, three techniques, Simplified Terrestrial Analysis, Adaptive Isolation-based Scoring, and Lightweight Graph-Based Neighbour Mechanisms, were used to further enable the detection of nonlinear relationships and interactions among the different components within the grid. The model uses Compact Nonlinear Indicators (e.g., Consumption Dev Index, Device Influence Vector and Reduced-Order Chaotic Metrics) as Analytical Features and Hidden Indicators for Anomalous Energy Usage. The experimental findings indicate that the suggested ensemble model has excellent detection accuracy (0.984), precision (0.974), sensitivity (0.975), specificity (0.965), F1-score (0.975), and AUC (0.984) while minimizing the computational and memory requirements for edge deployment. These findings support the conclusion that optimized edge-based detection is an efficient and feasible solution for detecting energy consumption anomalies and therefore offers the potential to enable predictive management and enhance operational performance in the Smart Grid environment.
This publication presents a Multimodal AI-Driven Edge Framework designed to provide energy-efficient monitoring of human activity in Smart Grids. Current approaches for monitoring energy networks utilize Centralized Cloud Infrastructures that place heavy demands on computers and create very high latencies in communication as well as limiting the ability to create real-time recommendations to optimize energy consumption. The proposed framework will utilize a combination of compressed and full deep learning pipelines combined with quantized inference models, and tracking methods based on TinyML technology; allowing for the utilization of multimodal data (occupancy, movement, and usage) directly on edge devices (NVIDIA Jetson Nano, Raspberry Pi 5, ARM-based Processors) therefore minimizing memory footprint, computational load, and power consumption while generating very accurate analysis across all )modality types tested. Test results demonstrate 42%-70% decreased latency, 30%-55% decreased memory footprint and that they operate in an energy-efficient manner making them ideal for continued field deployment. Our Multimodal AI-Driven Edge Framework enables the collection of activity data to develop predictive energy management systems, implement adaptive demand responses and support real-time energy optimization for Smart Grids without dependence on cloud connectivity. We have demonstrated lightweight, multimodal AI technology that will augment the energy efficiency, scalability, and intelligence of future Smart Grids.
This study proposes a Knowledge Graph-Enhanced NB-IoT Architecture to improve intelligent educational data management and support system innovation in learning environments. The purpose is to address limitations of traditional innovation and entrepreneurship education, which often lacks real-time contextual information and personalized guidance. The proposed approach integrates NB-IoT for distributed, real-time data acquisition with a domain-specific knowledge graph for semantic organization, correlation, and enrichment of learning resources. A multi-layer framework was designed to support data collection, knowledge representation, and intelligent recommendation processes. Experimental evaluation demonstrates that the architecture enhances the relevance of learning activities, improves resource organization, and delivers more accurate and personalized educational interventions. The results indicate that combining NB-IoT infrastructure with knowledge graph intelligence can significantly strengthen adaptive learning environments and better support the development of innovative competencies.
As a research hotspot in the field of energy, the development degree of new energy is related to the development of carbon finance market. In order to quickly promote the realization of the dual carbon goals and improve the sustainability of new energy development, this paper proposes a carbon finance analysis method, combined with ant colony algorithm to analyze new energy data such as wind, photovoltaic and hydropower, and judge the carbon finance market. Then, risk prediction is carried out based on the energy value created by wind and photovoltaic power, as well as the depth of energy development. Finally, the utilization effect of new energy sources such as wind power and photovoltaic power is output, the stability of energy output, and the development depth of wind power and photovoltaic energy. The results show that there is a fluctuating relationship between the energy output of wind and photovoltaic power generation and their risk level, and the increase of electricity will inhibit the financial risk, and the energy output value is greater than the threshold of 15%. The risk reduction rate was 36.28% and 34.50%, and the integration of new energy and carbon finance was-0.2896, indicating that new energy has an impact on carbon finance, and the score of carbon emission was relatively low, which was 0.0178. The index integrates energy to reduce the risk rate of carbon finance, and the reduction rate is about 25%. Therefore, the research on carbon finance risk prediction can provide support for the development of wind power and photovoltaic power generation, expand the application scope of new energy, and realize the application depth of wind energy and photovoltaic power.
CC The transition toward cost-effective photovoltaic technologies requires alternatives to high-purity polysilicon, where upgraded metallurgical-grade (UMG) silicon represents a promising candidate. Yet, the intrinsic limitations of UMG silicon namely elevated impurity concentrations, high defect densities, and shortened minority carrier lifetimes remain key barriers to its widespread adoption. In this study, we address these challenges by exploring boron (B) and phosphorus (P) co-doping in UMG silicon nanostructures as a route to performance enhancement. A multi-tiered methodology was established, integrating controlled doping experiments, advanced electrical characterization, and compact-device modeling informed by Shockley Read Hall recombination kinetics. To systematically capture process property performance relationships, a design of experiments (DOE) framework was implemented, wherein dopant concentrations, carrier lifetimes, and interfacial resistances were varied. The extracted photovoltaic parameters open-circuit voltage (Voc), short-circuit current density (Jsc), fill factor (FF), and conversion efficiency (eta) were quantitatively benchmarked against undoped references. Simulation results indicated that optimized B/P co-doping reduces recombination-active defect states and enhances carrier transport, yielding Voc improvements exceeding 60 mV and FF gains up to 8% under typical operating conditions. Monte Carlo-based statistical analysis further confirmed the robustness of the identified optima, with eta improvements of 15-20% compared to baseline UMG silicon. These findings substantiate the role of synergistic B/P co-doping as a technically viable and economically attractive strategy to elevate UMG silicon toward high-efficiency photovoltaics. The presented optimization framework provides not only a mechanistic understanding of dopant-defect interactions but also practical guidelines for scaling laboratory protocols to industrial solar cell manufacturing.
Energy consumption will cause environmental pollution to intensify, affect the development modern tourism, and a large number of tourism behaviors will also exacerbate carbon emissions, so new energy technologies such as wind power and photovoltaic can promote the sustainable development of tourism and balance the relationship between social development entertainment consumption. This paper examines the relationship between green energy and tourism industry, focusing on wind and photovoltaic power to optimize tourism energy structures and broaden renewable applications. Results indicate that photovoltaic and wind generation fluctuates but remains stable, while rising tourism energy demand is offset by renewables, reducing consumption without harming operations or economic benefits. Tourism energy peaks between 13:00-18:00, when photovoltaic supply is stable and maximal; demand grows from 8:00-13:00; and from 18:00-24:00 photovoltaic output declines while wind power provides storage. Savings increase with photovoltaic shares up to 50%. Energy stability reaches 70-75%, and satisfaction ranges from 65.32-85.36%. Therefor the sustainable development of new energy can enhance the sustainability of tourism, promote its development in the direction of energy conservation and environmental protection, and improve the integration level of electricity tourism.
This paper undertakes a real-world performance evaluation of the Purulia Pumped Storage Plant (PPSP) situated in West Bengal and its integration with solar photovoltaic (PV) power plant to assess the techno-commercial feasibility of the hybrid system towards mitigation of intermittency nature of solar energy which is a necessity for India's energy transition and ambitious renewable energy goals, using high-resolution real operational SCADA data instead of simulation-based approaches. Pumped Hydroelectric Plants (PHPs) are primarily utilised during peak demand periods and in instances of major power plant outages. India's very first pumped storage plant was commissioned in 1985. Presently, out of the 4.75 GW of pumped storage plants installed across the country, 3.3 GW are operational in pumping mode. However, these plants have fallen short of their projected energy generation figures due to insufficient availability of off-peak power for pumping operations, until the advent of various renewable energy sources, notably solar energy, in India. Against this backdrop, this analysis delves into the economic viability of the Purulia Pumped Storage Plant considering real solar generation profiles distributed across the West Bengal state and PPSP operational data of six representative days viz., 6th Feb., 26th April., 8th June., 24th Aug., 4th Oct., and 30th Dec. across different seasons of the year 2023 to assess critical performance indicators such as Daily Efficiency (DE), Cumulative Pumped Ratio (CPR) and Levelized Cost of Storage (LCOS) for both actual grid power operational scenario and hypothetical solar power generation driven pumping scenario matching with the actual PPSP operation. The study findings reveal that the hypothetical solar-PPSP hybrid system doesn't just slightly improve economics, it fundamentally enhances the value proposition by drastically reducing reliance on the grid electricity, achieving up to a 100% displacement on four days except 6th Feb. and 30th Dec. This substitution of high-cost grid power of up to 6.01 INR/kWh with low-cost solar energy with LCOE of 2.55 INR/kWh directly caused a dramatic surge in the CPR by 15% to 136% and halved the LCOS range to 0.31-1.05 INR/kWh, fundamentally transforming the plant's economic viability. CPR on the six representative days have increased from 159.50, 98.36, 102.20, 40.23, 137.37 and 152.18 in actual operational scenario to 182.92, 178.98, 225.64, 94.82, 294.13 and 230.83 respectively in the hypothetical scenario. On the other hand, LCOS has decreased from the range of 0.38-2.32 INR/kWh in case of actual operational scenario to the range of 0.31-1.05 INR in the hypothetical scenario. This work may therefore contribute as a data-driven evidence base on the techno-economic advantages of solar-powered pumping for PHP with varying seasonal demand and solar generation patterns, marking a significant departure from model-driven studies and would be useful for policymakers in reinforcing the role of hybridization of the existing and the upcoming PHPs in achieving India's ambitious renewable energy goal.
Electric vehicles (EVs) are transforming transportation, requiring planned charging networks to handle rising demand, manage power systems, reduce congestion, and integrate renewables. This work develops a stochastic optimization framework for EV charging stations with transactive control to cut carbon emissions promote EV adoption. Consequently, deploying EVCS will become a crucial strategy for addressing the gration of renewable energy. An innovative approach to supplying electric power stored in EV fleets involves using transportation networks as supplementary infrastructure .The article explores how transportation works affect EVCS and DR scheduling under uncertain PV output, highlighting the benefits of DR integration for improved efficiency and grid reliability. A mixed-integer linear programming (MILP) model is proposed to enhance the solution quality of the DC power flow method, thereby reducing computational complexity. A detailed evaluation of economic and environmental factors is included, showing a 1.288% overall system cost reduction with EV fleets and DR in scheduling costs, and a 2.431% decrease in EV fleet and solar power costs. This research aims to offer a sustainable, cost-effective, and scalable solution for EV charging infrastructure to support future electric mobility, with the dispatching problem verified on a modified IEEE 30-bus system
the CC Attribution distribution properly The increases in power costs and environmental pollution are major contributors to global warming worldwide. Modern power generating systems therefore require green technologies that focus on improving energy harvesting-based power generation. Green energy materials are essential for converting other types of energy into electrical energy. Among the major energy storage technologies, heat energy storage has maximum efficiency and can be converted into electrical energy by thermoelectric devices. Thermoelectric materials play a dual role: they convert thermal energy into electricity via the Seebeck effect and provide heating or cooling when electrical energy is applied through the Peltier effect. This review highlights the role of thermoelectric devices in converting heat to electricity and in the reverse process for effective thermal management. The review also encompasses the utilization of renewable energy sources, such as photovoltaics, for hydrogen generation to mitigate the intermittency of solar energy.
This paper presents a novel reliability-constrained optimization framework for the design and sizing of a Multi-Energy Hub (MEH) that integrates Combined Heat and Power (CHP), Electrical Energy Storage Systems (ESS), Thermal Storage Systems (TSS), renewable energy sources, and uniquely waste heat recovery from a Modular Multilevel Converter (MMC). The proposed energy hub model simultaneously meets the electricity and thermal demands of an industrial consumer under real tariff conditions and climate data from Phoenix, Arizona. The key innovation lies in modeling the thermal losses of the MMC as a valuable source of heat recovery, which reduces boiler fuel consumption and operational expenditures (OPEX). The optimization framework employs a Genetic Algorithm (GA) to minimize the total cost, encompassing capital expenditure (CAPEX) and OPEX, while enforcing constraints on energy balance, storage limitations, system capacity, and permissible energy shortage. Two scenarios one with MMC heat recovery and the other without are evaluated for both summer and winter conditions. The results show that incorporating MMC heat recovery reduces total cost by up to 2.3%, mainly due to reduced gas consumption in the boiler. Furthermore, a reliability-based constraint ensures that at least 95% of the energy demand is met, minimizing Energy Not Supplied (ENS) and enhancing system resilience. The proposed method provides a scalable and flexible design tool for next-generation industrial energy systems, particularly in hot climates with highly variable energy demand. Integrating waste heat from power electronics into hybrid energy systems introduces a novel dimension in thermal-electric synergy.
This study aims to explore the effectiveness of implementing low-energy technologies. Through experiments, it was found that the traditional geological route selection method consumes 156 kilowatt hours per kilometer of electricity, while the average electricity consumption of the new low energy consumption technology is 90 kilowatt hours per kilometer. Low energy consumption technology can reduce energy consumption by about 37%. In a six-month 200 student teaching project, students' understanding of energy conservation and emission reduction has greatly improved, and their understanding rate of energy-saving technology has increased from 40% before teaching to 87% after teaching. These findings are of great significance for promoting education in energy efficiency and sustainable development.
Electric Vehicles (EVs) rely on charging stations (CSs), but uncoordinated charging often leads to grid voltage deviations and long waiting times. This study proposes an integrated scheduling and routing framework that combines renewable-powered CSs with intelligent decision-making. A Singleton Membership Modified Fuzzy Logic Controller (SMM-FLC)-based MPPT ensures stable renewable power extraction, while a He-Uniform Initialized ModTanh Activated ANN (HUI-MTA-ANN) selects the optimal CS by considering queue lengths, travel deadlines, and cost. The Bayes Distributed Squirrel Search Optimization (BD-SSO) algorithm then identifies the most efficient route, and Quadratic Programming (QP) schedules charging slots. Results on an EV charging dataset show the framework achieves 97% accuracy with very low false selection rates, while significantly improving renewable energy utilization. The findings highlight a scalable and intelligent solution for reliable EV charging infrastructure.
Wind speed forecasting is essential for electrical energy management and planning. Wind turbine energy production varies according to fluctuations in wind speed. Unpredictable variations in wind patterns create vulnerabilities for wind power installations. To mitigate this unpredictability, an effective approach is to anticipate wind speed at specific heights, which is crucial for the operation of wind farms. Although previous work has examined different learning algorithms, researchers are still striving to find a stable model that minimizes prediction error. The objective of this study is to predict wind speed using machine learning algorithms. The results of the study highlight the remarkable predictive performance of eleven models, which are based on regression and classification methods with cross-validation. On the test data, the evaluation reveals impressive results, with coefficients of determination ranging from 0.986023 to 0.794080. The comparative study of the different algorithms shows that the proposed LightGBM (LGB) model outperforms most similar models in the literature, achieving statistically significant accuracy values. This result made it possible to evaluate the power of a wind turbine over the 12 months of the year. It shows that March generates the most energy, while August produces the least.
This study presents both a physical and monetary evaluation of the key social and environmental impacts linked to the development of a 20 MW wind farm in Zawia, Libya. The analysis considers a range of externalities, including carbon footprint, water use, noise emissions, visual impacts, shadow and shadow flicker, land occupation, and potential electromagnetic interference. The project is planned on a 2 km2 site and will incorporate 10 Gamesa G-114 wind turbines. Using the System Advisor Model (SAM) for energy yield estimation, the annual electricity generation reaches approximately 104,240.6 MWh. The project's initial capital cost is estimated at $39.36 million. The conventional levelized cost of energy (LCoE), calculated without factoring in social or environmental externalities, stands at $86.84/MWh. When externalities are monetized, they contribute an additional $7.92/MWh, representing nearly one-third of the operation and maintenance expenses ($25/MWh). If the evaluation internalizes only the carbon footprint, the LCoE decreases to $49.43/MWh. Conversely, when all social and environmental impacts are fully incorporated, the adjusted LCoE rises to $57.35/MWh. These findings highlight the critical importance of integrating external costs into wind energy assessments, providing a fairer comparison with conventional energy sources and strengthening the case for renewables competitive energy markets.
The current simulation study seeks to completely eliminate dependence on fossil fuels consumption in a diesel fuel/natural gas RCCI engine. Instead, the engine uses two final products resulting from the natural gas steam reforming process, hydrogen and DME. By changing the engine fuels to nonfossil fuels, the results reveal that along with a 100% reduction in fossil fuel consumption, the total fuel cost per hour of the engine operation will increase by 300%. Compared to diesel fuel/natural gas RCCI engine, the duration of combustion was increased beyond 50 degree crank angle along with the reduction in peak pressure by up to 57 bar. However, a maximum increase of 2.57 bar in indicated mean effective pressure with a gross indicated efficiency of over 55% can be achieved without any concerns about diesel knock. In the hydrogen/DME RCCI engine, carbon dioxide emission can be reduced by up to 157 tons per year in each cylinder. The 2007 EPA and EURO VI levels for formaldehyde and unburned methane are achievable, respectively. Lower than Euro II level for carbon monoxide is unattainable. Moreover, the level of soot and NOx emission lead to the relevant Euro II range due to the chemical properties of DME.
