Search Results (1,068)

Accurate short-term forecasting of residential electricity demand is increasingly important for smart distribution systems, particularly in the context of demand-side management and flexibility-oriented grid operation. In this study, a high-resolution forecasting framework is proposed in which household electricity demand is classified into fixed, shiftable, and adjustable load categories and forecasted together with total load. A one-minute-resolution synthetic residential load dataset is generated using the Centre for Renewable Energy Systems Technology (CREST) demand model for households with two to five occupants over a 31-day winter period in January. The appliance-level demand data are grouped according to operational characteristics and integrated into a representative four-bus distribution feeder. Minute-level power flow analysis is then performed to calculate technical losses, which are incorporated into the forecasting dataset together with meteorological variables (temperature, wind speed, and solar irradiance) and temporal descriptors. Using this multi-input structure, random forest (RF), support vector machine (SVM), feed-forward neural network (FFNN), and long short-term memory (LSTM) models are comparatively evaluated for the prediction of fixed, shiftable, adjustable, and total residential loads. Model performance is assessed using root mean square error (RMSE) and Pearson correlation coefficient (R), while mean absolute error (MAE) is additionally reported for the final test set. The results show that the LSTM model provided the most consistent overall forecasting performance, particularly for shiftable, adjustable, and total load estimation, while RF yielded competitive results for fixed-load correlation and short-window forecasting in Buses 1 and 2. In contrast, SVM and FFNN exhibited weaker generalization performance across several load categories. The proposed framework provides a practical foundation for the development of dynamic pricing mechanisms that consider load-type-based controllability levels. Overall, the findings demonstrate that integrating load categorization with meteorological, temporal, and technical loss information provides a robust and reproducible framework for smart grid applications such as demand-side management, peak load mitigation, and flexibility-aware residential load analysis. Full article

Short-term wind power forecasting is a key enabling technology for wind farm operation optimization, power grid dispatch, and electricity market decision-making. However, existing studies often lack unified standards in data partitioning, input feature construction, and hyperparameter tuning, making fair and reproducible comparisons across models difficult to achieve. To address this issue, this study focuses on day-ahead wind power forecasting for a single wind farm and establishes a benchmarking framework with strict chronological splitting, a shared feature information set, and a consistent hyperparameter tuning budget. Within this framework, six representative models, namely Ridge, XGBoost, LightGBM, DLinear, Transformer, and PatchTST, are systematically evaluated. A two-level evaluation protocol combining a fixed hold-out split and expanding-window rolling validation is adopted to compare model performance from multiple perspectives, including overall accuracy, sensitivity to hyperparameter tuning, robustness across rolling windows, and performance under typical operating scenarios. The results show that model rankings are not fully consistent between the hold-out evaluation and the rolling-validation setting. Under the fixed hold-out split, LightGBM achieved the lowest NRMSE of 10.2326%, while Transformer obtained the lowest NMAE of 6.9944%. In contrast, under the 8-fold expanding-window rolling validation, Transformer achieved the lowest average NRMSE of 8.1684%, followed by LightGBM with 8.7344%. These results indicate that the best performance on a single test split does not necessarily imply the strongest robustness across multiple time windows. In addition, strong tree-based models remain highly competitive in this single-wind-farm forecasting task, whereas more complex deep temporal models do not always deliver stable advantages. Meanwhile, the performance gains brought by hyperparameter optimization vary substantially across models, suggesting that conclusions drawn from default-parameter comparisons are of limited reliability. These findings demonstrate that systematic benchmarking under strict temporal constraints and fair tuning conditions is essential for clarifying the comparative performance, robustness, and engineering applicability of different model families. The study can therefore provide practical guidance for model selection and deployment in short-term wind power forecasting for single wind farms. Full article

Drought is a slowly evolving, multi-driver hydro-meteorological hazard whose accurate early prediction is a cornerstone of climate-smart agriculture and water-resource planning. Existing data-driven drought forecasting frameworks suffer from three persistent limitations: (i) most models concatenate heterogeneous climate variables into a single flat feature vector, implicitly assuming a single dominant driver such as precipitation, even though atmospheric moisture demand, radiation and wind-mediated evapotranspiration co-determine drought onset; (ii) wavelet preprocessing is typically applied to the full series, introducing future-information leakage that violates the operational causality requirement of forecasting; and (iii) most architectures predict a single horizon and provide no causal attribution explaining when, where and which climatic variables initiated the event. This study proposes WaveDroughtNet, a multi-modal, multi-horizon deep-learning framework that addresses these limitations through five integrated components: (a) a strictly causal Daubechies-4 wavelet decomposition computed in a rolling fashion; (b) six modality-specific encoders with stochastic modality dropout (p = 0.15); (c) cross-modal multi-head attention with four heads; (d) a four-layer temporal convolutional network (TCN) backbone with dilation factors yielding a 240-step receptive field; and (e) a post hoc DroughtOriginTracer that combines temporal attention, modal-attribution and inter-district propagation scans. The Standardised Precipitation Evapotranspiration Index (SPEI), used as the supervisory target, is computed following the canonical Vicente-Serrano formulation. water balance $D=P-\mathrm{P}\mathrm{E}\mathrm{T}$ (Hargreaves PET) at a 4-week (≈1-month) timescale, fitted with a three-parameter log-logistic distribution via L-moments, validated by Kolmogorov–Smirnov goodness-of-fit testing ($\alpha =0.05$) per district, and standardised through the inverse-normal cumulative distribution function. Trained on 18,304 weekly district records from NASA POWER reanalysis (2014–2025) covering all 32 districts of Tamil Nadu, India, WaveDroughtNet uses only 256,869 parameters and produces, in a single forward pass, four forecasts (1 week, 1 month, 3 months, 1 year). On the held-out 2024 test partition ($N=1728$), the model attains weighted $F1=0.9221$ and $R^2=0.8512$ at the 1-week horizon, and weighted $F1=0.8498$ and $R^2=0.6812$ at the 1-year horizon. Diebold–Mariano tests confirm that WaveDroughtNet significantly outperforms naive persistence, seasonal naive, LSTM, ConvLSTM and a vanilla Transformer at the 3-month and 1-year horizons (p < 0.001). The DroughtOriginTracer successfully back-projects 15 Coimbatore events to causal origins 29–41 weeks prior to onset. We explicitly acknowledge three limitations that constrain operational deployment in its current form—zero severe events in the 2024 test partition ($F{1}_{severe}$ = 0.000), static inter-district modelling, and absence of vegetation-index supervision—and propose concrete mitigation pathways in the Discussion. Full article

This study presents a forecast-driven Advanced Forecasting Model (AFM) and Virtual Power Plant (VPP) framework for a hybrid renewable energy system comprising utility-scale solar PV, wind generation, and a Battery Energy Storage System. Long Short-Term Memory neural networks provide real-time short-term forecasts to dynamically schedule power flows based on battery state-of-charge, grid import limits, and system constraints. Solar irradiance forecasting achieved MAE = 10.674 W/m², RMSE = 16.348 W/m², and MAPE = 14.18%, while wind speed forecasting achieved MAE = 0.880 m/s, RMSE = 1.115 m/s, and MAPE = 22.01%. Two dispatch scenarios were evaluated over a 72 h window: a reactive baseline and the proposed AFM/VPP strategy. The AFM reduced total grid imports by 57.48% (1466.34 MWh to 623.47 MWh), increased renewable utilization, and minimized curtailment. Financial analysis indicates an accelerated break-even (Year 6 vs. Year 9), a higher net present value, and cumulative 20-year profits exceeding R26.01 billion despite marginally higher capital expenditure. Emissions analysis shows annual CO₂ reductions from 123,680 t to 61,841 t, yielding 1.236 million tons of avoided emissions over 20 years. These results confirm that forecast-driven dispatch enhances operational efficiency, economic performance, and environmental sustainability, establishing a scalable approach for VPP operation in renewable-rich energy systems. Full article

The intermittent nature of renewable energy sources such as solar and wind power poses significant challenges for grid stability and energy management. Accurate forecasting is crucial for mitigating these challenges, as traditional models such as Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA) often fail to capture the non-linear relationships between weather patterns and energy generation. To address this limitation, this research proposes a machine learning framework leveraging Convolutional Neural Networks (CNNs) for spatial pattern recognition and Recurrent Neural Networks (RNNs) for time-series forecasting. By integrating system design parameters with meteorological data, the framework aims to enhance prediction accuracy. The potential outcomes of this framework are not just improved grid stability, optimized energy storage utilization, and reduced operational costs, but also a significant step towards the efficient integration of renewable energy into the power system, fostering a sense of optimism for the future of renewable energy forecasting. Full article

Accurate short-term wind power forecasting is crucial for maintaining the stability of modern power grid dispatching systems; however, the high nonstationarity and volatility of wind power data pose challenges for traditional forecasting models. To address these issues, a hybrid forecasting framework, variational mode decomposition (VMD)–improved sparrow search algorithm (ISSA)–support vector regression (SVR), is proposed herein. First, VMD is employed to decompose raw wind power data into multiple stable mode components. Then, the ISSA is introduced to optimize the hyperparameters of SVR, thereby alleviating the tendency of conventional SVR hyperparameter tuning methods to become trapped in local optima. Independent SVR forecasting models are then established for each decomposed mode component, and the final forecasting output is obtained via signal reconstruction. Experiments conducted on real-world datasets collected from five wind turbines demonstrate that the proposed framework consistently outperforms several baseline models. Compared with the VMD–SSA–SVR model, the proposed VMD–ISSA–SVR framework reduces the average root mean square error by 8.03% (from 124.28 to 114.19 kW) and improves the average coefficient of determination to 0.9024, with a maximum value of 0.9115. These results verify the effectiveness of the proposed framework in modeling complex nonlinear wind power data and highlight the superior optimization capability of the proposed ISSA. Full article

Short-term wind speed forecasting plays a critical role in enabling the reliable integration of renewable energy and supporting the sustainable operation of power systems. However, traditional dual-frequency decomposition methods oversimplify wind speed dynamics by separating them into only high-frequency disturbances and low-frequency trends, making it difficult to capture intermediate-frequency transitional dynamics. Additionally, single models struggle to adapt to multi-scale temporal features, limiting forecasting performance. To address these issues, this paper proposes a tri-band frequency-aware heterogeneous expert collaboration framework. First, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is employed for signal denoising, followed by Particle Swarm Optimization-Time Varying Filtering-based Empirical Mode Decomposition (PSO-TVF-EMD) for multi-scale signal disentanglement. Then, Permutation Entropy (PE) is used to construct a tri-band structure consisting of high-, intermediate-, and low-frequency components. A frequency-aware expert routing mechanism assigns Bayesian Optimization Long Short-Term Memory (BO-LSTM), an improved Markov model, and Auto-Regressive Integrated Moving Average (ARIMA) to the corresponding frequency bands. Finally, a reliability-aware cooperative aggregation strategy integrates predictions from multiple experts. Experimental results show that representative baseline models, including BO-LSTM, Markov, ARIMA, Gated Recurrent Unit (GRU) and Convolutional Neural Network Long Short-Term Memory (CNN-LSTM), achieve MAE values ranging from 0.308 to 0.429, while the proposed framework reduces the Mean Absolute Error (MAE) to 0.193 and Root Mean Square Error (RMSE) to 0.274, with a Mean Absolute Percentage Error (MAPE) of 7.35% and R² of 0.927. Compared with the dual-frequency decomposition scheme (MAE = 0.266), the proposed tri-band framework achieves an average improvement of approximately 28.1%. The results suggest that explicitly modeling intermediate-frequency dynamics and aligning model inductive biases with multi-scale signal characteristics can effectively enhance short-term wind speed forecasting performance. Full article

In this study, we developed a leakage-free time-series machine learning framework to improve the accuracy of short-term (10 min ahead) wind speed forecasting. The measurements were obtained from real operational data collected at the Bandırma/Balıkesir wind power plant in Türkiye. The framework incorporates chronological train validation test splitting, causal missing data imputation, leakage-free feature engineering, and supervised lag-based modeling. Such a leak-proof design is crucial to avoid future information influencing the training and testing process of models, thus making the forecasting process more realistic and reliable in practice. We tested several models, including persistence, Support Vector Regression (SVR), Least-Squares Gradient Boosting (LSBoost), Random Forest (RF), Elastic Net (ELASTIC), and a stacking ensemble, and evaluated their performance using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), R-Squared (R²), bias measures, and skill scores, complemented by diagnostic analyses including residual distribution, autocorrelation, regime-based evaluation, Bland–Altman plots, and Quantile Quantile (Q-Q) plots. Our analyses showed that the Elastic Net model achieved balanced and statistically consistent performance, with a test RMSE of 0.6325 m/s, R² = 0.977, and negligible bias. Residual analysis indicated that errors were centered around zero, exhibited weak temporal dependence, and followed an approximately normal distribution in the central quantiles. Regime-based evaluation revealed that the model performed strongly in medium- and high-wind-speed conditions, while accuracy decreased under low wind speeds due to measurement uncertainty and low signal-to-noise ratios. Feature importance analysis indicated that previous wind speed was the dominant predictor, with solar irradiation and air temperature also contributing significantly. Forecast error decomposition showed that most prediction errors arose from natural atmospheric variability, with minimal systematic bias. The Diebold–Mariano test confirmed that ELASTIC statistically outperformed conventional machine learning models such as SVR and Random Forest. The proposed framework demonstrates statistically consistent short-term forecasting behavior that may support operational wind energy management and grid balancing applications. Full article

Wind power forecasting is essential for improving renewable energy exploitation and maintaining power system stability. However, influenced by factors such as the velocity and orientation of the wind and atmospheric pressure, wind power exhibits strong variability and uncertainty. Moreover, raw data often contains missing values, shutdown periods, and anomalies, which can degrade forecasting performance. Aiming at solving these challenges, this study develops a wind power forecasting approach integrating data cleaning with a hybrid prediction model. In the preprocessing stage, correlation analysis is employed to select meteorological variables strongly associated with power output as input features, thereby reducing redundancy and improving model effectiveness. Subsequently, missing values and shutdown records are removed, and an improved DBSCAN method is applied to detect anomalous samples. These outliers are then corrected using least squares regression, enhancing data quality while preserving continuity. In the forecasting stage, a hybrid model integrating TCN, BiLSTM, and the Plant Root Growth Optimization (PRGO) algorithm is developed. Specifically, TCN serves to capture local temporal features, while BiLSTM extracts bidirectional temporal dependencies. The PRGO serves to globally optimize model architecture parameters and key hyperparameters, improving convergence efficiency and generalization performance. Experiments on real wind farm data demonstrate that the proposed TCN-BiLSTM-PRGO model consistently outperforms all baselines (TCN, LSTM, TCN-BiLSTM, TCN-Transformer, and TCN-BiLSTM-WOA) across 12 h, 24 h, and 48 h horizons. At 12 h, it achieves a mean R² of 0.942, NMAE of 6.014%, and NRMSE of 7.539% over five runs, improving R² by 0.008–0.123 and reducing NMAE by 0.37–4.57 percentage points compared to other models. It also attains the highest R² at 24 h (0.791) and 48 h (0.833). Statistical significance (p < 0.05) and chronological split tests (R² = 0.940) further confirm their robustness and generalization. The proposed method offers a reliable solution for high-precision wind power forecasting. Full article

In high-penetration wind power systems, outage loss uncertainty analysis is fundamental to maintenance scheduling, and its accuracy critically depends on real-time wind power generation, which is dominated by ultra-short-term wind speed fluctuations. Accurate wind speed prediction is therefore essential for reliable outage loss evaluation and subsequent maintenance decision-making. Dense turbine layouts in wind farms lead to strong wake effects, resulting in complex physical attenuation and spatiotemporal correlations in wind speed between upstream and downstream turbines. Leveraging upstream turbine information can therefore enhance the accuracy of downstream wind speed forecasting. However, existing approaches that incorporate neighboring information, such as graph neural networks, rely primarily on data-driven learning and do not explicitly account for the physical mechanisms of wake attenuation, which limits their predictive performance. To address these challenges, a physics-informed ultra-short-term wind speed forecasting method is proposed which integrates an LSTM network for temporal feature extraction with the Jensen wake model through a weighted loss function within a PINN framework. Wake relationships are first identified based on wind direction and turbine layout, and the Jensen wake model is employed to characterize downstream wind speed attenuation. The weighted loss jointly optimizes data-driven and physics-based objectives, enabling the model to coordinate temporal pattern learning with wake-related physical interactions while adhering to wake decay physics. Moreover, the proposed approach accounts for topology-sensitive power flow variations under high-penetration renewable systems, where outage losses are strongly influenced by real-time wind power and wake-effect uncertainties. Case studies demonstrate that, compared with a conventional LSTM model, the proposed method reduces the normalized mean absolute error and the normalized root mean square error by 14.3% and 13.5%, respectively, indicating improved forecasting accuracy and potential for more reliable system outage analysis. Full article

Reliable short-term wind power forecasting is crucial for smart grid stability. However, high-dimensional noise and stochastic fluctuations in wind sequences often degrade the accuracy of traditional forecasting models. Moreover, wind power time series typically exhibit asymmetric rising and decaying patterns, which further complicate accurate modeling. To address these challenges, this study proposes a hybrid intelligent system that integrates three components: data preprocessing, heterogeneous ensemble learning, and probabilistic interval forecasting. First, we build a multi-stage preprocessing workflow. Adaptive DBSCAN and Local Outlier Factor (LOF) remove spatial and density anomalies. Then multivariate variational mode decomposition (MVMD) synchronously separates multi-scale oscillatory patterns while preserving cross-channel correlations and frequency-domain symmetry across input variables. SHAP analysis quantifies feature importance, ensuring interpretability. The selected features are fed into a heterogeneous ensemble model consisting of Transformer, BPNN, ELM, XGBoost, and QRLSTM, which collectively capture multi-scale temporal dependencies and diverse data patterns. The ensemble weights are dynamically optimized by a modified multi-objective dragonfly algorithm (MMODA) that balances forecast accuracy and stability. Based on this ensemble, we apply MMODA to tune kernel density estimation for generating high-quality forecast intervals, maximizing coverage while minimizing interval width. Experiments on two wind farms in Shandong show that our MMODA-optimized ensemble reduces mean absolute percentage error by about 44.7% compared to single models, and ablations confirm that MVMD preprocessing adds a further 10.7% reduction. The proposed system provides an interpretable and reliable decision-support tool for sustainable grid operations. Full article

The high-penetration integration of offshore renewable energy introduces significant challenges, including high volatility, randomness, and insufficient energy accommodation, which place higher demands on the planning and operation of offshore integrated energy systems. To address these issues, this paper proposes an offshore multi-energy coupled DC microgrid system integrating wind, photovoltaic, tidal current, and wave energy, together with flexible loads such as seawater desalination and power-to-hydrogen. A hybrid forecasting model based on EMD-PCA-LSTM is developed to improve prediction accuracy under uncertain conditions. On this basis, a two-stage optimization framework considering both economic efficiency and operational risk is established. At the planning level, a joint operation–planning model incorporating Conditional Value-at-Risk (CVaR) is formulated to determine the optimal capacity configuration by minimizing the total annualized cost and risk cost. At the operational level, a multi-time-scale rolling optimization model is constructed to enhance system adaptability under renewable fluctuations. Case study results demonstrate that the proposed method significantly improves renewable energy accommodation, reduces the curtailment rate to 0.7%, and effectively balances economic performance and operational stability. The proposed framework provides a practical and efficient approach for capacity allocation and optimal operation of offshore multi-energy coupled systems. Full article

Weather forecasting, given the ever-increasing occurrence of climate change-induced events, has been widely introduced as a method to offer accurate and timely forecasts for proactive measures and risk mitigation. Artificial intelligence of things (AIoT) offers promising solutions for short-term weather forecasting, contributing to the advancement of sustainable and efficient weather monitoring technologies. This work presents everWeather_2.0, a significantly enhanced low-cost and self-powered AIoT-based weather forecasting station, which addresses key challenges in power consumption, user engagement and forecasting accuracy. The proposed end-to-end Cloud-Edge-IoT (CEI) proof-of-concept solution improves upon its predecessor by combining a more robust renewable energy subsystem for complete power autonomy with a series of lightweight, adaptive statistical models for on-device forecasting and an integrated display for on-site user engagement. Deployed in a real-world scenario, the station demonstrated seamless operation and high short-term forecasting accuracy for the thermodynamic variables during the pilot deployment period, with model errors observed as low as 2% for 30 min forecasts to 4.3% for 120 min intervals, validating its applicability in real-time and continuous physical weather monitoring. While wind speed and rainfall were monitored, they were excluded from the current accuracy metrics due to their high volatility and the insufficient number of events recorded during the pilot period to ensure reliable modeling. Full article

Integrated energy systems (IES) are widely recognized as a key pathway toward carbon neutrality, enabling the coupling and coordinated optimization of electricity, heat, gas, and cooling. This review provides a structured, technology-oriented overview of IES based on a unified five-subsystem framework (production, conversion, transmission, storage, and consumption). It systematically covers: (1) renewable energy utilization—solar, wind, and geothermal—supported by a global spatial distribution map and representative top-performing commercial products; (2) energy cascade utilization, where combined heat and power/combined cooling, heating and power (CHP/CCHP) raises overall efficiency from approximately 35–40% to 70–90%; (3) multi-form energy storage—electrical, electrochemical, chemical, thermal, and mechanical—distinguishing short-term balancing (e.g., lithium-ion (Li-ion), flywheels, supercapacitors, with 85–95% round-trip efficiency) from long-duration and seasonal applications (e.g., pumped hydro, hydrogen/power-to-gas (P2G), redox flow batteries); and (4) forecasting, collaborative optimization, and the bidirectional integration of IES with smart grids and grid modernization. A strategic strengths, weaknesses, opportunities, and threats–Political, Economic, Sociological, Technological, Legal, and Environmental (SWOT–PESTLE) analysis is further presented to position IES within the global energy transition. The review highlights that IES and grid innovation are mutually enabling, and that realizing the full carbon-neutrality potential of IES requires coordinated progress in standardization, digitalization, long-duration storage, and cross-sector policy alignment. Full article

High penetration of wind and photovoltaic (PV) generation increases renewable uncertainty and real-time balancing pressure in active distribution networks. To address this problem, this paper proposes a two-stage robust optimization method for day-ahead and real-time scheduling of a flexibly interconnected multi-microgrid (MMG) system enabled by a flexible interconnection device (FID). The proposed framework jointly optimizes power purchase from the upper-level distribution network, micro-gas turbine output, energy storage system (ESS) operation, and FID-based bidirectional power exchange, thereby coordinating local temporal flexibility and inter-microgrid spatial flexibility. A polyhedral uncertainty set is used to model wind and PV forecast errors, and the problem is solved by the column-and-constraint generation (C&CG) algorithm. Case studies on a two-microgrid system show that, compared with independent operation under traditional robust optimization, the proposed method reduces real-time balancing cost, wind and PV curtailment, and total operating cost by 98.96%, 95.84%, and 0.59%, respectively. Sensitivity analysis further verifies the economy–robustness trade-off under different uncertainty budgets and forecast deviation levels. Full article