Search Results (925)

This study introduces Itô-RMSProp, a novel extension of the RMSProp optimizer inspired by Itô stochastic calculus, which integrates adaptive Gaussian noise into the update rule to enhance exploration and mitigate overfitting during training. We embed this optimizer within Gated Recurrent Unit (GRU) networks for stock price forecasting, leveraging the GRU’s strength in modeling long-range temporal dependencies under nonstationary and noisy conditions. Extensive experiments on real-world financial datasets, including a detailed sensitivity analysis over a wide range of noise scaling parameters ($\epsilon$), reveal that Itô-RMSProp-GRU consistently achieves superior convergence stability and predictive accuracy compared to classical RMSProp. Notably, the optimizer demonstrates remarkable robustness across all tested configurations, maintaining stable performance even under volatile market dynamics. These findings suggest that the synergy between stochastic differential equation frameworks and gated architectures provides a powerful paradigm for financial time series modeling. The paper also presents theoretical justifications and implementation details to facilitate reproducibility and future extensions. Full article

Under the tightening carbon reduction policies, port microgrids face the challenge of optimizing the installed capacity of multiple power generation types to reduce operating costs and increase renewable energy penetration. We develop a bi-level cost-optimization framework in which the upper level decides long-term capacities (PV, wind, gas turbine, bio-fuel unit, and battery energy storage), and the lower level dispatches a multi-energy port microgrid (electricity–heat–cold) on an hourly basis with frequency regulation services. To ensure rigor and reproducibility, we (i) move the methodology upfront and formalize all constraints, (ii) provide a dedicated data–preprocessing pipeline for multi-region 50/60 Hz frequency time series, and (iii) map a policy intensity index to a carbon price and/or an annual cap used in the objective/constraints. The bi-level MILP is solved by a column-and-constraint generation algorithm with optimality gap control. We report quantitative metrics—annualized total cost, CO₂ emissions (t), renewable shares (%), and regulation cycles—across scenarios. Results show consistent cost–carbon trade-offs and robust capacity shifts toward storage and biofuel as policy tightens. All inputs and scripts are organized for exact replication. Full article

Due to the highly perishable nature of fresh products, consumers often worry about product quality, which significantly dampens their purchase intentions. To address this and boost consumers’ willingness to pay, many fresh foods enterprises resort to sales manipulation tactics as a way to attract customers and gain a competitive edge. To further explore whether such yields net benefits to competing firms and the fresh product market, this research develops a game model involving two competing members selling fresh products. The model analyzes how sales manipulation affects the product prices, market shares, and profits of both firms across four scenarios. Finally, numerical analysis supports the robustness of findings and provides insights for establishing management guidelines for fresh product enterprises and the market. Full article

The European Union air transport sector has been repeatedly exposed to major disruptions such as the 2008 financial crisis, the COVID-19 pandemic, the war in Ukraine, and volatile energy prices. Strengthening resilience has, therefore, become a strategic priority. This study examines how strategic energy investments—covering renewable energy, sustainable aviation fuels (SAFs), electrification, hydrogen technologies, and advanced infrastructure—contribute to the resilience of the EU air transport system. The methodology integrates both primary and secondary data from EU policy documents, ICAO and IATA databases, Eurostat, and national statistics. A multi-criteria evaluation was applied using four key performance indicators: emission reduction efficiency (ER), annual exposure index (AEI), investment performance index (IPI), and net present value (NPV). Projects were assessed through Simple Additive Weighting (SAW) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), complemented by sensitivity analysis. The results show that the Pioneer project delivers the strongest environmental and financial outcomes, ranking first in ER, AEI, and NPV. Hermes performs best in job creation and social impact, while BioOstrand achieves substantial absolute CO₂ reductions but lower cost efficiency. TULIPS shows limited effectiveness across all indicators. Sensitivity analysis confirmed that rankings remain robust under alternative weighting scenarios. The findings underscore that project design and alignment with resilience objectives matter more than investment size. Strategic energy investments should, therefore, be prioritized not only for decarbonization but also for their ability to reinforce both technological and socio-economic resilience, providing a reliable foundation for a sustainable and crisis-resistant EU air transport sector. Full article

This paper presents the first rigorous empirical investigation into a fundamental question of cryptocurrency valuation: Are cryptocurrency prices in line with the prices of fundamental assets? To answer this, we analyze the nine largest cryptocurrencies by market capitalization—Bitcoin (BTC), Ethereum (ETH), Solana (SOL), Binance Coin (BNB), Ripple (XRP), Cardano (ADA), Litecoin (LTC), Tron (TRX), and the stablecoin DAI—against a suite of traditional benchmarks, including major fiat currencies (EUR, CAD, JPY), gold, and the S&P500 index. Our dataset spans from 1 January 2014 to 30 June 2025, with start dates varying for newer cryptocurrencies to ensure robust time series analysis. Guided by the asset pricing theory, we formulate a martingale test: if a cryptocurrency is priced in line with a fundamental numeraire asset, its price ratio relative to that numeraire must follow a martingale process. Our extensive empirical analysis reveals that the prices of major cryptocurrencies (BTC, ETH, SOL, BNB) consistently reject the martingale hypothesis when traditional assets (currencies, gold, equities) serve as the numeraire, indicating a decoupling from fundamental valuation anchors. Conversely, when Bitcoin or Ethereum itself is used as the numeraire, most smaller cryptocurrencies are priced in line with these crypto benchmarks, suggesting an internal valuation ecosystem that operates independently of traditional finance. Full article

The effective management of energy interactions in multi-island microgrid systems presents a significant challenge due to the geographical dispersion of islands. To address this, this paper proposes a hierarchical distributed optimization strategy based on the alternating direction method of multipliers (ADMM). The strategy features a two-layer architecture: the upper layer employs the ADMM to solve the system-level optimal power flow problem and generates distributed node marginal electricity prices (DLMPs) as clear economic coordination signals. The lower layer consists of individual island microgrids, which independently and in parallel solve their internal security-constrained economic dispatch (SCED) problems upon receiving the converged DLMP signals. This layered decoupling design functionally separates system-level coordination from microgrid-level optimization and enhances privacy protection by preventing the exposure of internal cost functions and operational constraints during upper-layer iterations. Case studies demonstrate that the proposed strategy reduces total operating costs by 10.3% compared to a centralized approach, while also significantly decreasing communication data volume by 83% and ensuring robust privacy protection. The algorithm exhibits good scalability with sublinear growth in iteration counts as the system scales, validating its effectiveness and practical potential for enhancing energy management in multi-island microgrid systems. Full article

This study introduces a Transformer-based forecasting tool termed EXPERT (EXchange rate Prediction using Encoder Representation from Transformers) and applies it to exchange rate forecasting. We developed and trained a Transformer-based forecasting model, then evaluated its performance on nine currency pairs with various characteristics. Finally, we benchmarked its effectiveness against six established forecasting models: Linear Regression, Random Forest, Stochastic Gradient Descent, XGBoost, Bagging Regression, and Long Short-Term Memory. Our dataset covers the period from 1999 to 2022. The models were evaluated for their ability to predict the next day’s closing price using three performance metrics. In addition, the EXPERT system was evaluated on its ability to extend forecast horizons and as the core of a trading strategy. The model’s robustness was further evaluated using the Multiple Comparisons with the Best (MCB) metric on five dataset samples. Full article

The global energy transition, characterized by the proliferation of intermittent renewables and the evolution of electricity markets, has positioned virtual power plants (VPPs) as crucial aggregators of distributed energy resources. However, their participation in competitive markets is fraught with multifaceted uncertainties stemming from price volatility, renewable generation intermittency, and unpredictable prosumer behavior, which necessitate sophisticated, risk-averse bidding strategies to ensure financial viability. This review provides a comprehensive analysis of the state-of-the-art in risk-averse bidding for VPPs. It first establishes a resource-centric taxonomy, categorizing VPPs into four primary archetypes: DER-driven, demand response-oriented, electric vehicle-integrated, and multi-energy systems. The paper then delivers a comparative assessment of different optimization techniques—from stochastic programming with conditional value-at-risk and robust optimization to emerging paradigms such as distributionally robust optimization, game theory, and artificial intelligence. It critically evaluates their application contexts and effectiveness in mitigating specific risks across diverse market types. Finally, the review synthesizes these insights to identify persistent challenges—including computational bottlenecks, data privacy, and a lack of standardization—and outlines a forward-looking research agenda. This agenda emphasizes the development of hybrid AI–physical models, interoperability standards, multi-domain risk modeling, and collaborative VPP ecosystems to advance the field towards a resilient and decarbonized energy future. Full article

The transition of Türkiye’s energy market toward net-zero emissions by 2053 requires modeling approaches capable of capturing complex interactions and long-term uncertainties. In this study, a long-term agent-based modeling (ABM) framework was developed, integrating econometric demand forecasting with a seasonal autoregressive integrated moving average (SARIMA) model and machine learning (ML)-based day-ahead market (DAM) price prediction. Of the ML models tested, CatBoost achieved the highest accuracy, outperforming XGBoost and Random Forest, and supported investment analysis through net present value (NPV) calculations. The framework represents major market actors—including generation units, investors, and the market operator—while also incorporating the impact of Türkiye’s first nuclear power plant (NPP) under construction and the potential introduction of a carbon emissions trading scheme (ETS). All model components were validated against historical data, confirming robust forecasting and market replication performance. Hourly simulations were conducted until 2053 under alternative policy and demand scenarios. The results show that renewable generation expands steadily, led by onshore wind and solar photovoltaic (PV), while nuclear capacity, ETS implementation, and demand assumptions significantly reshape prices, generation mix, and carbon emissions. The nuclear plant lowers market prices, whereas an ETS substantially raises them, with both policies contributing to emission reductions. These scenario results were connected to actionable policy recommendations, outlining how renewable expansion, ETS design, nuclear development, and energy efficiency measures can jointly support Türkiye’s 2053 net-zero target. The proposed framework provides an ex-ante decision-support framework for policymakers, investors, and market participants, with future extensions that can include other energy markets, storage integration, and enriched scenario design. Full article

With the increasing penetration of distributed energy resources (DERs) in distribution networks, traditional passive distribution systems are evolving into active and flexible systems capable of participating in the transmission-level energy market. Integrating distribution networks into a transmission-centric market-clearing model introduces challenges, such as capturing internal operational constraints and reflecting the economic features of distribution systems. To this end, this paper proposes a market integration method for distribution networks based on a feasible region and an accompanying bidding strategic bidding method to enable their efficient participation in the transmission-level electricity market. With a two-stage adaptive robust optimization framework, the feasible region that preserves operational characteristics of the distribution system and ensures the satisfaction of operational constraints within the distribution system is first depicted. The feasible region appears as time-coupled box-shaped regions. On this basis, a strategic bidding method is proposed based on the nested segmentation of the feasible region, jointly considering power and reserve. With it, the bidding prices of energy and reserve can be prepared, and then, together with the feasible region, can be smoothly integrated into the transmission-level market model. Numerical case studies demonstrate the effectiveness of the proposed method. Full article

This article introduces a cost-effective, IoT-enabled flexible energy management system (EMS) for residential photovoltaic (PV) microgrids with battery storage, implemented on an ESP32 microcontroller. The proposed system achieves indirect control over commercial household inverters by altering wattmeter readings and utilizing Modbus communication, thereby avoiding expensive hardware modifications. A significant contribution of this work is enabling the injection of energy from the Battery Energy Storage System (BESS) into the grid, a capability often restricted by commercial inverters. Real-world experimentation validated robust performance of the proposed system, demonstrating its ability to dynamically manage energy flows, achieve minimal tracking errors, and optimize energy usage in response to both flexibility market signals and electricity prices. This approach provides a practical and accessible solution for prosumers to actively participate in energy trading and flexibility markets using widely available technology. Full article

Accurate inflation forecasting is of central importance for monetary authorities, governments, and businesses, as it shapes economic decisions and policy responses. While most studies focus on headline inflation, this paper analyses the Harmonised Index of Consumer Prices (HICP) and its 12 subcomponents in the euro area over the period 2000–2023, covering episodes of financial crisis, economic stability, and recent inflationary shocks. We apply a broad set of machine learning and deep learning models, systematically optimized through grid search, and evaluate their performance using the Normalized Mean Absolute Error (NMAE). To complement traditional accuracy measures, we introduce the Forecastability Index (FI) and the Interquartile Range (IQR), which jointly capture both the difficulty and robustness of forecasts. Our results show that RNN and LSTM architectures consistently outperform traditional approaches such as SVR and RFR, particularly in volatile environments. Subcomponents such as Health and Education proved easier to forecast, while Recreation and culture and Restaurants and hotels were among the most challenging. The findings demonstrate that macroeconomic stability enhances forecasting accuracy, whereas crises amplify errors and inter-model dispersion. By highlighting the heterogeneous predictability of inflation subcomponents, this study provides novel insights with strong policy relevance, showing which categories can be forecast with greater confidence and where uncertainty requires more cautious intervention. Full article

The Kennedy model provides a flexible and mathematically consistent framework for modeling the term structure of interest rates, leveraging Gaussian random fields to capture the dynamics of forward rates. Building upon our earlier work, where we developed both theoretical results—including novel proofs of the martingale property, connections between the Kennedy and HJM frameworks, and parameter estimation theory—and practical calibration methods, using maximum likelihood, Radon–Nikodym derivatives, and numerical optimization (stochastic gradient descent) on simulated and real par swap rate data, this study extends the analysis in several directions. We derive detailed formulas for the volatilities implied by the Kennedy model and investigate their asymptotic properties. A comprehensive sensitivity analysis is conducted to evaluate the impact of key parameters on derivative prices. We implement an industry-standard Monte Carlo method, tailored to the conditional distribution of the Kennedy field, to efficiently generate scenarios consistent with observed initial forward curves. Furthermore, we present closed-form pricing formulas for various interest rate derivatives, including zero-coupon bonds, caplets, floorlets, swaplets, and the par swap rate. A key advantage of these results is that the formulas are expressed explicitly in terms of the initial forward curve and the original parameters of the Kennedy model, which ensures both analytical tractability and consistency with market-observed data. These closed-form expressions can be directly utilized in calibration procedures, substantially accelerating multidimensional nonlinear optimization algorithms. Moreover, given an observed initial forward curve, the model provides significantly more accurate pricing formulas, enhancing both theoretical precision and practical applicability. Finally, we calibrate the Kennedy model to market-observed caplet prices. The findings provide valuable insights into the practical applicability and robustness of the Kennedy model in real-world financial markets. Full article

Informal settlements, urban areas with substandard housing conditions and inadequate infrastructure, are increasing in Africa’s sub-Saharan cities, fueled by rapid urbanization, economic challenges, and high housing prices. However, developers often ignore the green building (GB) concept when upgrading housing conditions for these communities. This study aims to investigate GB design strategies specifically for residential structures in Akabahizi to identify and propose practical strategies suitable for informal settlements such as Akabahizi and to develop sustainable housing solutions that enhance environmental quality and meet the needs of residents. Simulation software and combined qualitative and quantitative data collection techniques, including field surveys, interviews, and assessments of existing building conditions, constitute the methodology used in this study. The focus was on the influence of climatic factors, including temperature, precipitation, and wind, on design choices, particularly GB design and current residential buildings in Akabahizi. Based on the survey, 82.5% of residents support the GB concept, 87.4% recognize the importance of GB for community well-being, and 97.1% recognize the benefits of integrating energy-efficient technology for residents’ well-being. Questionnaire findings were considered in decision-making for the design of the new proposed structure to address challenges in the area. Optimized energy efficiency, daylight access, and thermal comfort resulting from courtyard design support GB design incorporating a courtyard as a robust and culturally relevant sustainable design framework tailored for Akabahizi. The courtyard provides green space that promotes social interaction, improves air quality, and delivers natural cooling elements that are essential for residential housing. The proposed new design, with green roof and renewable energy devices, improved material usage, and natural ventilation elements, outperformed the existing one in terms of lower levels of carbon emission for environmental protection. In conclusion, a collaborative effort is needed among various stakeholders, including architects, urban planners, and educational institutions, to promote and implement sustainable building practices. The study suggests that enhancing awareness, offering training opportunities, and empowering local professionals and residents alike can pave the way for improved living conditions and sustainable urban development in Akabahizi and similar informal settlements. Full article

In response to the persistent failures of traditional election polling, this study introduces the Decentralized Prediction Market Voter Framework (DPMVF), a novel tool to empirically test and quantify the predictive capabilities of Decentralized Prediction Markets (DPMs). We apply the DPMVF to Polymarket, analysing over 11 million on-chain transactions from 1 September to 5 November 2024 against aggregated polling in the 2024 U.S. Presidential Election across seven key swing states. By employing Cross-Correlation Function (CCF) for linear analysis and Dynamic Time Warping (DTW) for non-linear pattern similarity, the framework provides a robust, multi-faceted measure of the lead-lag relationship between market sentiment and public opinion. Results reveal a striking divergence in predictive clarity across different electoral contexts. In highly contested states like Arizona, Nevada, and Pennsylvania, the DPMVF identified statistically significant early signals. Using a non-parametric Permutation Test to validate the observed alignments, we found that Polymarket’s price trends preceded polling shifts by up to 14 days, a finding confirmed as non-spurious with a high confidence (p < 0.01) and with an exceptionally high correlation (up to 0.988) and shape similarity. At the same time, in states with low polling volatility like North Carolina, the framework correctly diagnosed a weak signal, identifying a “low-signal environment” where the market had no significant polling trend to predict. This study’s primary contribution is a validated, descriptive tool for contextualizing DPM signals. The DPMVF moves beyond a simple “pass/fail” verdict on prediction markets, offering a systematic approach to differentiate between genuine early signals and market noise. It provides a foundational tool for researchers, journalists, and campaigns to understand not only if DPMs are predictive but when and why, thereby offering a more nuanced and reliable path forward in the future of election analysis. Full article