Search Results (118)

This study investigates the predictive performance of financial indicators in forecasting stock prices within the automotive sector using an adaptive neuro-fuzzy inference system (ANFIS). In light of the growing complexity of global financial markets and the increasing demand for automated, data-driven forecasting models, this research aims to identify those financial ratios that most accurately reflect price dynamics in this specific industry. The model incorporates four widely used financial indicators, return on assets (ROA), return on equity (ROE), earnings per share (EPS), and profit margin (PM), as inputs. The analysis is based on real financial and market data from automotive companies, and model performance was assessed using RMSE, nRMSE, and confidence intervals. The results indicate that the full model, including all four indicators, achieved the highest accuracy and prediction stability, while the exclusion of ROA or ROE significantly deteriorated model performance. These findings challenge the weak-form efficiency hypothesis and underscore the relevance of firm-level fundamentals in stock price formation. This study’s sector-specific approach highlights the importance of tailoring predictive models to industry characteristics, offering implications for both financial modeling and investment strategies. Future research directions include expanding the indicator set, increasing the sample size, and testing the model across additional industry domains. Full article

Electricity distribution systems face increasing challenges due to demand growth, regulatory requirements, and the integration of distributed generation. In this context, distribution companies must make strategic and well-supported investment decisions, particularly in asset reinforcement actions such as reconductoring. This paper presents a multi-stage methodology to optimize reconductoring investments under load forecasting uncertainties. The approach combines a decomposition strategy with Monte Carlo simulation to capture demand variability. By discretizing a lognormal probability density function and selecting the largest loads in the network, the methodology balances computational feasibility with modeling accuracy. The optimization model employs exhaustive search techniques independently for each network branch, ensuring precise and consistent investment decisions. Tests conducted on the IEEE 123-bus feeder consider both operational and regulatory constraints from the Brazilian context. Results show that uncertainty-aware planning leads to a narrow investment range—between USD 55,108 and USD 66,504—highlighting the necessity of reconductoring regardless of demand scenarios. A comparative analysis of representative cases reveals consistent interventions, changes in conductor selection, and schedule adjustments based on load conditions. The proposed methodology enables flexible, cost-effective, and regulation-compliant investment planning, offering valuable insights for utilities seeking to enhance network reliability and performance while managing demand uncertainties. Full article

In this study, we consider monthly load forecasting, which is an essential decision for energy infrastructure planning and investment. This study focuses on the Texas power grid, where electricity consumption has surged due to rising industrial activity and the increased construction of data centers driven by growing demand for AI. Based on an extensive exploratory data analysis, we identify key characteristics of monthly electricity demand in Texas, including an accelerating upward trend, strong seasonality, and temperature sensitivity. In response, we propose a regression-based forecasting model that incorporates a carefully designed set of input features, including a nonlinear trend, lagged demand variables, a seasonality-adjusted month variable, average temperature of a representative area, and calendar-based proxies for industrial activity. We adopt a rolling forecasting approach, generating 12-month-ahead forecasts for both 2023 and 2024 using monthly data from 2013 onward. Comparative experiments against benchmarks including Holt–Winters, SARIMA, Prophet, RNN, LSTM, Transformer, Random Forest, LightGBM, and XGBoost show that the proposed model achieves superior performance with a mean absolute percentage error of approximately 2%. The results indicate that a well-designed regression approach can effectively outperform even the latest machine learning methods in monthly load forecasting. Full article

This paper presents a comprehensive analysis comparing the implementation of electric and diesel buses for public transport services in the low-density area of the City of Jastrebarsko in Croatia. It utilizes a multidimensional approach and incorporates direct and indirect costs, such as vehicle acquisition, operation, charging, maintenance, and environmental impact costs during the lifecycle of the buses. The results show that, despite the higher initial investment in electric buses, these vehicles offer savings, especially when coupled with significantly reduced emissions of pollutants, which decreases indirect costs. However, local contexts differ, leading to a need to revise whether or not a municipality can finance the procurement and operations of such a fleet. The paper utilizes a robust methodological framework, integrating a proposal based on real-world data and demand and combining it with predictive analytics to forecast long-term benefits. The findings of the paper support the introduction of buses as a sustainable solution for Jastrebarsko, which provides insights for public transport planners, urban planners, and policymakers, with a discussion about the specific issues regarding the introduction, procurement, and operations of buses of different propulsion in a low-density area. Full article

As the global energy transition accelerates, the integration of solar power and artificial intelligence (AI) technologies offers new pathways for sustainable local development. This study examines four Serbian municipalities—Šabac, Sombor, Pirot, and Čačak—to assess how AI-enabled solar power systems can enhance energy resilience, reduce emissions, and support community-level sustainability goals. Using a mixed-method approach combining spatial analysis, predictive modeling, and stakeholder interviews, this research study evaluates the performance and institutional readiness of local governments in terms of implementing intelligent solar infrastructure. Key AI applications included solar potential mapping, demand-side management, and predictive maintenance of photovoltaic (PV) systems. Quantitative results show an improvement >60% in forecasting accuracy, a 64% reduction in system downtime, and a 9.7% increase in energy cost savings. These technical gains were accompanied by positive trends in SDG-aligned indicators, such as improved electricity access and local job creation in the green economy. Despite challenges related to data infrastructure, regulatory gaps, and limited AI literacy, this study finds that institutional coordination and leadership commitment are decisive for successful implementation. The proposed AI–Solar Integration for Local Sustainability (AISILS) framework offers a replicable model for emerging economies. Policy recommendations include investing in foundational digital infrastructure, promoting low-code AI platforms, and aligning AI–solar projects with SDG targets to attract EU and national funding. This study contributes new empirical evidence on the digital–renewable energy nexus in Southeast Europe and underscores the strategic role of AI in accelerating inclusive, data-driven energy transitions at the municipal level. Full article

Demand for mental health support has exerted unprecedented pressure on statutory services. Innovative solutions such as Green or Nature-Based Social Prescribing (NBSP) programmes may help address unmet need, improve access to personalised treatment, and support the sustainable delivery of primary services within a prevention model of population health. We piloted an innovative health economics-informed Social Return on Investment (SROI) analysis and forecast of a ‘Making Well’ therapeutic craft and horticulture programme for mental health between October 2021 and March 2022. Quantitative and qualitative outcome data were collected from participants with mild-to-moderate mental health conditions at baseline and nine-weeks follow-up using a range of validated measures, including the Short Warwick–Edinburgh Mental Well-being Scale, ICEpop CAPability measure for Adults (ICECAP-A), General Self-Efficacy Scale (GSES), and a bespoke Client Service Receipt Inventory (CSRI). The acceptability and feasibility of these measures were explored. Results indicate that the Making Well programme generated well-being-related social value in the range of British Pound Sterling (GBP) GBP 3.30 to GBP 4.70 for every GBP 1 invested. Our initial pilot forecast suggests that the programme has the potential to generate GBP 5.40 to GBP 7.70 for every GBP 1 invested as the programme is developed and delivered over a 12-month period. Despite the small sample size and lack of a control group, our results contribute to the evidence-base for the effectiveness and social return on investment of NBSP as a therapeutic intervention for improving health and well-being and provides an example of the use of health economic well-being outcome measures such as ICECAP-A and CSRIs in social value analysis. Combining SROI evaluation and forecast methodologies with validated quantitative outcome measures used in the field of health economics can provide valuable social cost–benefit evidence to decision-makers. Full article

Accurate short-term passenger forecasts help regional airports align capacity with demand and plan investments effectively. Drawing on quarterly traffic data for 2010–2024 supplied by the Polish Civil Aviation Authority, this study employs Holt–Winters exponential smoothing to predict passenger volumes at Szczecin–Goleniów Airport for 2025. Additive and multiplicative formulations were parameterized with Excel Solver, using the mean absolute percentage error to identify the better-fitting model. The additive version captured both the steady post-pandemic recovery and pronounced seasonal peaks, indicating that passenger throughput is likely to rise modestly year on year, with the highest loads expected in the summer quarter and the lowest in early spring. These findings suggest the airport should anticipate continued growth and consider adjustments to terminal capacity, apron allocation, and staffing schedules to maintain service quality. Because the Holt–Winters method extrapolates historical patterns and does not incorporate external shocks—such as economic downturns, policy changes, or public health crises—its projections are most reliable over the short horizon examined and should be complemented by scenario-based analyses in future work. This study contributes to sustainable airport management by providing a reproducible, data-driven forecasting framework that can optimize resource allocation with minimal environmental impact. Full article

The increasing integration of renewable energy sources (RES) in power systems presents challenges related to variability, stability, and efficiency, particularly in smart microgrids. This systematic review, following the PRISMA 2020 methodology, analyzed 66 studies focused on advanced energy storage systems, intelligent control strategies, and optimization techniques. Hybrid storage solutions combining battery systems, hydrogen technologies, and pumped hydro storage were identified as effective approaches to mitigate RES intermittency and balance short- and long-term energy demands. The transition from centralized to distributed control architectures, supported by predictive analytics, digital twins, and AI-based forecasting, has improved operational planning and system monitoring. However, challenges remain regarding interoperability, data privacy, cybersecurity, and the limited availability of high-quality data for AI model training. Economic analyses show that while initial investments are high, long-term operational savings and improved resilience justify the adoption of advanced microgrid solutions when supported by appropriate policies and financial mechanisms. Future research should address the standardization of communication protocols, development of explainable AI models, and creation of sustainable business models to enhance resilience, efficiency, and scalability. These efforts are necessary to accelerate the deployment of decentralized, low-carbon energy systems capable of meeting future energy demands under increasingly complex operational conditions. Full article

As Turkey’s energy demand surges due to industrialization, population growth, and economic development, precise forecasting of electricity demand has become crucial for ensuring energy security and facilitating sustainable planning. This study undertakes an analysis of Turkey’s current energy landscape and develops long-term electricity demand forecasts utilizing a diverse array of statistical and machine learning models, including linear regression, polynomial regression, and artificial neural networks (ANNs). By incorporating economic indicators, demographic trends, and historical consumption data, this research projects Turkey’s electricity demand up to 2045. Among the various influencing factors, industrial production stands out as the most significant driver. The findings offer strategic insights into infrastructure investments, the integration of renewable energy, and policies aimed at enhancing efficiency. This research presents a data-driven, policy-oriented framework to assist decision-makers in reducing import dependence while steering Turkey towards a sustainable energy transition. Full article

This paper proposes hybrid LSTM-DES models that combine traditional forecasting methods with recurrent neural network techniques. We experimented with these proposed models using four passenger datasets from different regions of Thailand. Additionally, we compared their performance with several individual forecasting models, including the Double Moving Average (DMA), Double Exponential Smoothing (DES), and Holt–Winters methods (both additive and multiplicative trends), as well as long short-term memory (LSTM) recurrent neural networks. Our proposed hybrid model builds upon previous work with improvements in hyperparameter tuning using the GRG nonlinear optimization method. The results demonstrate that the hybrid LSTM-DES models outperformed all individual models in terms of both accuracy and demand variation. The reason behind the success of the hybrid model is that it works well with both linear and nonlinear trends, as well as the seasonality of certain periods. Furthermore, the forecast results for train passengers will serve as input variables to estimate the future revenue of train travel programs in various regions, including rail tourism. This information will help identify which regions should receive increased focus and investment by the train tourism program. For example, if the forecasted number of passengers in the northern region is high, the State Railway of Thailand will promote and improve infrastructure at the train station and nearby tourist attractions. Full article

This study evaluates four hybrid infrastructure scenarios for supporting battery electric buses (BEBs) and fuel cell electric buses (FCEBs), analyzing different combinations of grid power, solar energy, battery storage, and fuel cell systems. A multi-stage framework—comprising energy demand forecasting, infrastructure capacity planning, and multi-criteria decision-making (MCDM) evaluation incorporating total cost of ownership (TCO), carbon emissions, and energy resilience—was developed and applied to a real-world transit depot. The results highlight critical trade-offs between financial, environmental, and operational objectives. The limited rooftop solar configuration, integrating solar energy through a Solar Power Purchase Agreement (SPPA), emerges as the most cost-effective near-term solution. Offsite solar with onsite large-scale battery storage and offsite solar with fuel cell integration achieve greater sustainability and resilience, but they face substantial cost barriers. The analysis underscores the importance of balancing investment, emissions reduction, and resilience in planning zero-emission bus fleets. Full article

Climate change requires efficient water resource management, especially in regions where viticulture is developing. This study evaluates the water requirements, precipitation deficits, and irrigation needs of vineyards in two locations in southern Poland. The analysis covers both a reference period (1931–2020) and a forecast period (2030–2100), based on two climate change scenarios: RCP 4.5 and RCP 8.5. Grapevine water requirements were estimated using a crop coefficient tailored to Poland’s agroclimatic conditions, combined with meteorological data on air temperature and precipitation. Monthly crop coefficient values were calculated as the ratio of grapevine potential evapotranspiration, estimated using the Penman–Monteith method, to reference evapotranspiration, calculated using the Treder approach for the period 1981–2010. Precipitation deficits were assessed for normal, medium dry, and very dry years using the Ostromęcki method. Irrigation water demand was estimated for light, medium, and heavy soils using the Pittenger method. The results indicate a significant increase in both water demand and precipitation deficits in the forecast period, regardless of the scenario. In very dry years, irrigation will be necessary for all soil types. In medium dry years, water deficits will primarily affect vineyards on light soils. These findings underscore the urgent need for improvements in irrigation planning, especially in areas with low soil water. They offer practical insights for estimating future water storage needs and implementing precision irrigation adapted to changing climate conditions. Adopting such adaptive strategies is essential for sustaining vineyard productivity and improving water use efficiency. This study also supports the integration of climate projections into regional planning and calls for investment in innovative, water-saving technologies to strengthen the long-term resilience of Poland’s wine industry. Full article

Demographic growth in Kazakhstan over the past decades has had a significant impact on the entire education system, particularly at the preschool and primary levels. High birth rates have led to an increasing number of children requiring enrollment in kindergartens and first-grade classes. This often results in a shortage of available places, increased workload for teaching staff, and a decline in the quality of educational services. This paper examines the application of Business Intelligence (BI) tools and Geographic Information Systems (GIS) for forecasting potential shortages of educational places and identifying regional priorities in infrastructure development. A predictive model is presented, based on birth rate indicators and age cohorts, which enables the estimation of future demand for preschool and primary school capacity across the regions of Kazakhstan. The study highlights the urgent need for proactive planning and targeted investment to prevent critical shortages and to ensure equitable access to quality education. The findings can serve as a foundation for the development of effective public education policies and support the formulation of regional strategies that reflect current demographic trends. Full article

This paper investigates a supply chain setting in which a retailer exhibiting overconfidence invests in demand forecasting. Specifically, the retailer overestimates both the precision of the forecasting signal and the productivity of the investment. We analytically characterize the retailer’s investment behavior and show that overconfidence can lead to overinvestment in forecast accuracy. Beyond the investment decision itself, we examine how overconfidence influences the performance of both supply chain members and the system as a whole. When the retailer withholds forecast information from the supplier, overconfidence tends to harm both the retailer and the overall supply chain. However, under information-sharing arrangements, overconfidence can become beneficial—improving outcomes for the supplier and the system. Notably, when the retailer shares forecast with a sophisticated (strategically responsive) supplier, overconfidence may lead to a win–win outcome, where both parties gain from the retailer’s elevated investment in demand forecasting. These findings offer valuable insights into the conditions under which overconfidence shifts from being a liability to a strategic advantage, enriching our understanding of behavioral factors in supply chain decision-making. Full article

Geothermal resources are abundant in China and are distributed mainly in the eastern region where energy demand is high, especially in Tianjin. However, a significant disparity remains between the actual heating area and the potential heatable area of geothermal resources in Tianjin, which indicates the vast untapped potential for development and utilization in the region. In this study, we reviewed the history and current status of geothermal development in Tianjin. We further analyzed the factors affecting the development and utilization of geothermal heat in Tianjin. Subsequently, we constructed a system dynamics (SD) model of geothermal development and utilization in Tianjin. We developed four scenarios, including baseline, policy incentives, technological progress, and economic inputs. The results of the multiscenario forecasts and sensitivity analyses of the SD model showed the following: Tianjin will go through four stages of geothermal development and utilization in the future. Policy support and economic investment were the two main factors influencing the development of geothermal energy, and the influence of technological progress was comparatively smaller. Based on the above results, we proposed recommendations to promote sustainable development of geothermal energy in Tianjin according to three aspects: policy mechanism, economic investment, and technological progress. Full article