Search Results (489)

This study introduces a novel Fuzzy Piecewise Fractional Derivative (FPFD) framework to enhance epidemiological modeling, specifically for the multi-phasic co-infection dynamics of Omicron and malaria. We address the limitations of traditional models by incorporating two key realities. First, we use fuzzy set theory to manage the inherent uncertainty in biological parameters. Second, we employ piecewise fractional operators to capture the dynamic, phase-dependent nature of epidemics. The framework utilizes a fuzzy classical derivative for initial memoryless spread and transitions to a fuzzy Atangana–Baleanu–Caputo (ABC) fractional derivative to capture post-intervention memory effects. We establish the mathematical rigor of the FPFD model through proofs of positivity, boundedness, and stability of equilibrium points, including the basic reproductive number (R0). A hybrid numerical scheme, combining Fuzzy Runge–Kutta and Fuzzy Fractional Adams–Bashforth–Moulton algorithms, is developed for solving the system. Simulations show that the framework successfully models dynamic shifts while propagating uncertainty. This provides forecasts that are more robust and practical, directly informing public health interventions. Full article

Accurate forecasting of the electric vehicle (EV) charging load is a prerequisite for developing coordinated charging and discharging strategies. This study proposes a method for predicting the EV charging load by incorporating user travel characteristics and charging behavior. First, a transportation network–distribution network coupling framework is established based on a road network model with multi-source information fusion. Second, considering the multiple-intersection features of urban road networks, a time-flow model is developed. A time-optimal path selection method is designed based on the topological structure of the road network. Then, an EV driving energy consumption model is developed, accounting for both the mileage energy consumption and air conditioning energy consumption. Next, the user travel characteristics are finely modeled under two scenarios: working days and rest days. A user charging decision model is established using a fuzzy logic inference system, taking into account the state of charge (SOC), average electricity price, and parking duration. Finally, the Monte Carlo method is applied to simulate user travel and charging behavior. A simulation of the spatiotemporal distribution of the EV charging load was conducted in a specific area of Jiangning District, Nanjing. The simulation results show that there is a significant difference in the time distribution of EV charging loads between working days and rest days, with peak-to-valley differences of 3100.8 kW and 3233.5 kW, respectively. Full article

This paper presents an in-depth review of cybersecurity challenges and advanced solutions in modern power-generation systems, with particular emphasis on smart grids. It examines vulnerabilities in devices such as smart meters (SMs), Phasor Measurement Units (PMUs), and Remote Terminal Units (RTUs) to cyberattacks, including False Data Injection Attacks (FDIAs), Denial of Service (DoS), and Replay Attacks (RAs). The study evaluates cutting-edge detection and mitigation techniques, such as Cluster Partition, Fuzzy Broad Learning System (CP-BLS), multimodal deep learning, and autoencoder models, achieving detection accuracies of (up to 99.99%) for FDIA identification. It explores critical aspects of power generation, including resource assessment, environmental and climatic factors, policy and regulatory frameworks, grid and storage integration, and geopolitical and social dimensions. The paper also addresses the transmission and distribution (T&D) system, emphasizing the role of smart-grid technologies and advanced energy-routing strategies that leverage Artificial Neural Networks (ANNs), Generative Adversarial Networks (GANs), and game-theoretic approaches to optimize energy flows and enhance grid stability. Future research directions include high-resolution forecasting, adaptive optimization, and the integration of quantum–AI methods to improve scalability, reliability, and resilience. Full article

Reservoir evaporation is a vital component of the hydrological cycle and presents considerable challenges for sustainable water management, especially in arid and semi-arid regions. This study assesses the effectiveness of two Artificial Intelligence (AI) methods: Multilayer Perceptron (MLP) and Adaptive Neuro-Fuzzy Inference System (ANFIS), a combination ANN with fuzzy logic, in estimating daily evaporation from a large reservoir in a semi-arid region. Using eight years of hydrometeorological data from a nearby station, the study employed the ReliefF algorithm as a feature selection method for relevant input variables. The dataset was divided into training, validation, and testing subsets with 5% and 10% validation ratios, using four train–test splits of 70:30, 75:25, 80:20, and 85:15. Various training algorithms (e.g., Levenberg–Marquardt) and membership functions (e.g., generalized bell-shaped functions) were tested for both models. MLP consistently outperformed ANFIS on the test sets, showing higher R² and lower RMSE values. In the best-performing 70:30 split, MLP achieved an R² of 0.8069 and RMSE of 0.0923, compared to ANFIS with an R² of 0.3192 and RMSE of 0.2254. The findings highlight the AI-based approaches’ potential to support improved evaporation forecasting and integration into decision support tools for water resource planning amid changing climatic conditions. Full article

Recent concerns regarding artificial intelligent (AI) technologies have spurred studies into improving wastewater treatment efficiency and identifying low-carbon processes. Treating one cubic meter of wastewater necessarily consumes a certain amount of chemicals and energy. Approximately 20% of the total chemical consumption is attributed to phosphorus and nitrogen removal, with the exact proportion varying based on treatment quality and facility size. To promote sustainability in wastewater treatment plants (WWTPs), there has been a shift from traditional control systems to AI-based strategies. Research in this area has demonstrated notable improvements in wastewater treatment efficiency. This review provides an extensive overview of the literature published over the past decades, aiming to advance the ongoing discourse on enhancing both the efficiency and sustainability of chemical dosing systems in WWTPs. It focuses on AI-based approaches utilizing algorithms such as neural networks and fuzzy logic. The review encompasses AI-based wastewater treatment processes: parameter analysis/forecasting, model development, and process optimization. Moreover, it summarizes six promising areas of AI-based chemical dosing, including acid–base regents, coagulants/flocculants, disinfectants/disinfection by-products (DBPs) management, external carbon sources, phosphorus removal regents, and adsorbents. Finally, the study concludes that significant challenges remain in deploying AI models beyond simulated environments to real-world applications. Full article

This research introduces a novel hybrid forecasting framework for solar energy prediction in high-latitude regions with extreme seasonal variations. This approach uniquely employs General Type-2 Fuzzy Logic (GT2-FL) for data preprocessing and uncertainty handling, followed by two advanced neural architectures, including BiLSTM and SCINet with Time2Vec encoding and Variational Mode Decomposition (VMD) signal processing. Four configurations are systematically evaluated: BiLSTM-Time2Vec, BiLSTM-VMD, SCINet-Time2Vec, and SCINet-VMD, each tested with GT2-FL preprocessed data and raw input data. Using meteorological data from Lithuania (2023–2024) with extreme seasonal variations where daylight hours range from 17 h in summer to 7 h in winter, F-BiLSTM-Time2Vec achieved exceptional performance, with nRMSE = 1.188%, NMAE = 0.813%, and WMAE = 3.013%, significantly outperforming both VMD-based variants and SCINet architectures. Comparative analysis revealed that Time2Vec encoding proved more beneficial than VMD preprocessing, especially when enhanced with fuzzification. The results confirm that fuzzification, BiLSTM architecture, and Time2Vec encoding provide the most robust forecasting capability under various seasonal conditions. Full article

Water quality is crucial to the economy and ecology because a healthy aquatic eco-system supports human survival and biodiversity. We have developed the Neuro-Adapt Fuzzy Strategist (NAFS) to improve water quality index (WQI) forecasting accuracy. The objective of the developed model is to achieve a balance by improving prediction accuracy while preserving high interpretability and computational efficiency. Neural networks and fuzzy logic improve the NAFS model’s flexibility and prediction accuracy, while its optimized backward pass improves training convergence speed and parameter update effectiveness, contributing to better learning performance. The normalized and partial derivative computations are refined to improve the model. NAFS is compared with ANN, Adaptive Neuro-Fuzzy Inference System (ANFIS), and current machine learning (ML) models such as LSTM, GRU, and Transformer based on performance evaluation metrics. NAFS outperforms ANFIS and ANN, with MSE of 1.678. NAFS predicts water quality better than ANFIS and ANN, with RMSE of 1.295. NAFS captures complicated water quality parameter interdependencies better than ANN and ANFIS using principal component analysis (PCA) and Pearson correlation. The performance comparison shows that NAFS outperforms all baseline models with the lowest MAE, MSE, RMSE and MAPE, and the highest R², confirming its superior accuracy. PCA is employed to reduce data dimensionality and identify the most influential water quality parameters. It reveals that two principal components account for 72% of the total variance, highlighting key contributors to WQI and supporting feature prioritization in the NAFS model. The Breusch–Pagan test reveals heteroscedasticity in residuals, justifying the use of non-linear models over linear methods. The Shapiro–Wilk test indicates non-normality in residuals. This shows that the NAFS model can handle complex, non-linear environmental variables better than previous water quality prediction research. NAFS not only can predict water quality index values but also enhance WQI estimation. Full article

Accurate photovoltaic (PV) power output forecasting is critical for ensuring stable operation of modern power systems, yet it is constrained by high-dimensional redundancy in input weather data and the inherent heterogeneity of output scenarios. To address these challenges, this paper proposes a novel cascaded data-driven forecasting approach that enhances forecasting accuracy through systematically improving and optimizing the feature extraction, scenario clustering, and temporal modeling. Firstly, guided by weather data–PV power output correlations, the Deep Autoencoder (DAE) is enhanced by integrating Pearson Correlation Coefficient loss, reconstruction loss, and Kullback–Leibler divergence sparsity penalty into a multi-objective loss function to extract key weather factors. Secondly, the Fuzzy C-Means (FCM) algorithm is comprehensively refined through Mahalanobis distance-based sample similarity measurement, max–min dissimilarity principle for initial center selection, and Partition Entropy Index-driven optimal cluster determination to effectively cluster complex PV power output scenarios. Thirdly, a Long Short-Term Memory–Temporal Pattern Attention (LSTM–TPA) model is constructed. It utilizes the gating mechanism and TPA to capture time-dependent relationships between key weather factors and PV power output within each scenario, thereby heightening the sensitivity to key weather dynamics. Validation using actual data from distributed PV power plants demonstrates that: (1) The enhanced DAE eliminates redundant data while strengthening feature representation, thereby enabling extraction of key weather factors. (2) The enhanced FCM achieves marked improvements in both the Silhouette Coefficient and Calinski–Harabasz Index, consequently generating distinct typical output scenarios. (3) The constructed LSTM–TPA model adaptively adjusts the forecasting weights and obtains superior capability in capturing fine-grained temporal features. The proposed approach significantly outperforms conventional approaches (CNN–LSTM, ARIMA–LSTM), exhibiting the highest forecasting accuracy (97.986%), optimal evaluation metrics (such as Mean Absolute Error, etc.), and exceptional generalization capability. This novel cascaded data-driven model has achieved a comprehensive improvement in the accuracy and robustness of PV power output forecasting through step-by-step collaborative optimization. Full article

Multi-UAV cooperative missions demand millisecond-level coordination across three key resource dimensions—battery energy, wireless bandwidth, and onboard computing power—where traditional Min or linearly weighted schedulers struggle to balance safety with efficiency. We propose a prediction-enhanced two-stage T-norm–Choquet–OWA resource aggregator. First, an LSTM-EMA model forecasts resource trajectories 3 s ahead; next, a first-stage T-norm (min) pinpoints the bottleneck resource, and a second-stage Choquet–OWA, driven by an adaptive interaction measure $\varphi$, elastically compensates according to instantaneous power usage, achieving a “bottleneck-first, efficiency-recovery” coordination strategy. Theoretical analysis establishes monotonicity, tight bounds, bottleneck prioritization, and Lyapunov stability, with node-level complexity of only $O\left(1\right)$. In joint simulations involving 360 UAVs, the method holds the average round-trip time (RTT) at 55 ms, cutting latency by 5%, 10%, 15%, and 20% relative to Min, DRL-PPO, single-layer OWA, and WSM, respectively. Jitter remains within 11 ms, the packet-loss rate stays below 0.03%, and residual battery increases by about 12% over the best heuristic baseline. These results confirm the low-latency, high-stability benefits of the prediction-based peak-shaving plus two-stage fuzzy aggregation approach for large-scale UAV swarms. Full article

Reliable electricity supply in educational facilities demands predictive frameworks that reflect usage patterns and consumption variability. This study investigates electricity-consumption forecasting in lower-to-middle-income pre-tertiary institutions in Western Cape, South Africa, using adaptive neuro-fuzzy inference systems (ANFISs) optimized by evolutionary algorithms. Using genetic algorithm (GA) and particle swarm optimization (PSO) algorithms, the impact of two clustering techniques, Subtractive Clustering (SC) and Fuzzy C-Means (FCM), along with their cogent hyperparameters, were investigated, yielding several sub-models. The efficacy of the proposed models was evaluated using five standard statistical metrics, while the optimal model was also compared with other variants and hybrid models. Results obtained showed that the GA-ANFIS-FCM with four clusters achieved the best performance, recording the lowest Root Mean Square Error (RMSE) of 3.83, Mean Absolute Error (MAE) of 2.40, Theil’s U of 0.87, and Standard Deviation (SD) of 3.82. The developed model contributes valuable insights towards informed energy decisions. Full article

Accurately assessing the spatiotemporal dynamics and influencing factors of ecosystem stability in arid desert regions (ADR) is crucial for ecological conservation and the achievement of high-quality regional development. However, existing assessment frameworks generally fail to adapt to the extremely fragile ecological conditions of ADR. Therefore, the Alxa Desert, a typical region, was selected as the research region, and an ecosystem stability assessment framework tailored to regional characteristics (perturbation–resilience–function) was constructed. Perturbation represents external pressure, resilience reflects the capacity for recovery and adaptation, and function serves as the supporting foundation. The three dimensions are dynamically coupled and jointly determine the stability status of the ecosystem in the Alxa Desert. Methodologically, this study innovatively introduces the Cloud Model–Analytic Hierarchy Process (CM-AHP) to calculate indicator weights, which more effectively addressed the widespread fuzziness and uncertainty inherent in ecosystem assessments compared to traditional methods. In addition, spatial autocorrelation methods was applied to reveal the spatial and temporal evolution characteristics of ecosystem stability from 2000 to 2020. Furthermore, the optimal parameters geographical detector model (OPGDM) was applied to analyze the effects of natural and human factors on the spatial differentiation of ecosystem stability in Alxa Desert. In addition, the Markov–FLUS model was employed to simulate the future trends of ecosystem stability over the next two decades. The results indicate that ecosystem stability in Alxa Desert from 2000 to 2020 was primarily characterized by vulnerable and moderate levels, with the area classified as extremely vulnerable decreasing significantly by 10% relative to its extent in 2000. Spatially, higher stability was observed in oasis regions and southeastern mountainous regions, while lower stability was concentrated in the desert hinterlands. Overall, ecosystem stability shifted from vulnerable toward moderate levels, reflecting a trend of gradual improvement. From 2000 to 2020, the Moran’s I varied between 0.78 and 0.81, showing strong spatial clustering. Surfce Soil moisture content (SSMC), Soil organic carbon (SOC), and enhanced vegetation index (EVI) were the primary factors influencing the spatial differentiation of ecosystem stability in Alxa Desert. The interaction between these factors further enhanced their explanatory power. Future forecasting results indicate that ecosystem stability will further improve by 2030 and 2040, particularly in the northern and southern areas of Alxa Left Banner and Alxa Right Banner. The findings can offer a theoretical foundation for future ecological conservation and environmental management in ADR. Full article

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

Accurate cloud segmentation is critical for astronomical observations and solar forecasting. However, traditional threshold- and texture-based methods suffer from limited accuracy (65–80%) under complex conditions such as thin cirrus or twilight transitions. Although the deep-learning segmentation method based on U-Net effectively captures low-level and high-level features and achieves significant progress in accuracy, current methods still lack interpretability and multi-scale feature integration and usually produce fuzzy boundaries or fragmented predictions. In this paper, we propose multi-scale CAM, an explainable AI (XAI) framework that integrates class activation mapping (CAM) with hierarchical feature fusion to quantify pixel-level attention across hierarchical features, thereby enhancing the model’s discriminative capability. To achieve precise segmentation, we integrate CAM into an improved U-Net architecture, incorporating multi-scale CAM attention for adaptive feature fusion and dilated residual modules for large-scale context extraction. Experimental results on the SWINSEG dataset demonstrate that our method outperforms existing state-of-the-art methods, improving recall by 3.06%, F1 score by 1.49%, and MIoU by 2.21% over the best baseline. The proposed framework balances accuracy, interpretability, and computational efficiency, offering a trustworthy solution for cloud detection systems in operational settings. Full article

There is an obvious correlation between the photovoltaic (PV) output of different physical levels; that is, the overall power change trend of large-scale regional (high-level) stations can provide a reference for the prediction of the output of sub-regional (low-level) stations. The current PV prediction methods have not deeply explored the multi-level PV power generation elements and have not considered the correlation between different levels, resulting in the inability to obtain potential information on PV power generation. Moreover, traditional probabilistic prediction models lack adaptability, which can lead to a decrease in prediction performance under different PV prediction scenarios. Therefore, a probabilistic prediction method for short-term PV power based on multi-level adaptive dynamic integration is proposed in this paper. Firstly, an analysis is conducted on the multi-level PV power stations together with the influence of the trend of high-level PV power generation on the forecast of low-level power generation. Then, the PV data are decomposed into multiple layers using the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and analyzed by combining fuzzy entropy (FE) and mutual information (MI). After that, a new multi-level model prediction method, namely, the improved dual dynamic adaptive stacked generalization (I-Stacking) ensemble learning model, is proposed to construct short-term PV power generation prediction models. Finally, an improved dynamic adaptive kernel density estimation (KDE) method for prediction errors is proposed, which optimizes the performance of the prediction intervals (PIs) through variable bandwidth. Through comparative experiments and analysis using traditional methods, the effectiveness of the proposed method is verified. Full article

This article presents a comprehensive review of methods used for forecasting electricity consumption. The studies analyzed by the authors encompass both classical statistical models and modern approaches based on artificial intelligence, including machine-learning and deep-learning techniques. Electricity load forecasting is categorized into four time horizons: very short term, short term, medium term, and long term. The authors conducted a comparative analysis of various models, such as autoregressive models, neural networks, fuzzy logic systems, hybrid models, and evolutionary algorithms. Particular attention was paid to the effectiveness of these methods in the context of variable input data, such as weather conditions, seasonal fluctuations, and changes in energy consumption patterns. The article emphasizes the growing importance of accurate forecasts in the context of the energy transition, integration of renewable energy sources, and the management of the evolving electricity system, shaped by decentralization, renewable integration, and data-intensive forecasting demands. In conclusion, the authors highlight the lack of a universal forecasting approach and the need for further research on hybrid models that combine interpretability with high predictive accuracy. This review can serve as a valuable resource for decision-makers, grid operators, and researchers involved in energy system planning. Full article