Search Results (484)

The growing penetration of distributed photovoltaic (PV) systems presents significant operational challenges for power grids, driven by the scarcity of historical data and the high spatiotemporal variability of PV generation. To address these challenges, we propose Generative Reconstruction and Adaptive Identification via Latents (GRAIL), a unified, end-to-end framework that integrates generative modeling with adaptive clustering to discover latent structures and representative scenarios in PV datasets. GRAIL operates through a closed-loop mechanism where clustering feedback guides a cluster-aware data generation process, and the resulting generative augmentation strengthens partitioning in the latent space. Evaluated on a real-world, multi-site PV dataset with a high missing data rate of $45.4\%$, GRAIL consistently outperforms both classical clustering algorithms and deep embedding-based methods. Specifically, GRAIL achieves a Silhouette Score of 0.969, a Calinski–Harabasz index exceeding $4.132\times {10}^6$, and a Davies–Bouldin index of $0.042$, demonstrating superior intra-cluster compactness and inter-cluster separation. The framework also yields a normalized entropy of $0.994$, which indicates highly balanced partitioning. These results underscore that coupling data generation with clustering is a powerful strategy for expressive and robust structure learning in data-sparse environments. Notably, GRAIL achieves significant performance gains over the strongest deep learning baseline that lacks a generative component, securing the highest composite score among all evaluated methods. The framework is also computationally efficient. Its alternating optimization converges rapidly, and clustering and reconstruction metrics stabilize within approximately six iterations. Beyond quantitative performance, GRAIL produces physically interpretable clusters that correspond to distinct weather-driven regimes and capture cross-site dependencies. These clusters serve as compact and robust state descriptors, valuable for downstream applications such as PV forecasting, dispatch optimization, and intelligent energy management in modern power systems. Full article

This study aimed to implement an effective power prediction method to support the optimal management of the 30 MW Nagréongo solar photovoltaic (PV) plant in Burkina Faso. Initially, the performance of the PV plant was assessed by an external consultant based on data recorded in 2023 and 2024, revealing efficiency with a performance ratio (PR) of 73.73% in 2023, which improved to 77.43% in 2024. To forecast the plant’s power output, several deep learning models—namely LSTM, a GRU, LSTM-GRU, and an RNN—were applied using historical power data recorded at five-minute intervals during the 2024 periods of January–February; March–April; and July–August. All the deep learning models achieved accurate short-term forecasting for the 30 MW Nagréongo PV plant, with the seasonal performance shaped by the Sahelian weather regimes. The GRU performed best during the dry season (nRMSE ≈ 4%) and LSTM excelled in the hot months (nRMSE ≈ 2%), while the hybrid LSTM-GRU model proved most robust under rainy-season variability. Overall, the forecasting errors remained within 2–5% of plant capacity, demonstrating the suitability of these architectures for grid integration and operational planning in Sahel PV systems. Full article

In recent years, the amount of electricity produced by photovoltaic systems in Poland has increased significantly. This paper presents an evaluation of commercial software (PVGIS 5.3, ENERAD, and PVGIS 24) used for simulating energy produced by four photovoltaic installations. The results of the simulation were compared with the real energy production. The installations differ in terms of panel orientation (S, SE, SE-NW), tilt angle (12°, 25°, 37°) and location (roof- or ground-mounted). The average annual electricity production per 1 kW of module power for each installation was as follows: PV1—1104 kWh·kW⁻¹, PV2—1169 kWh·kW⁻¹, PV3—927 kWh·kW⁻¹, and PV4—831 kWh·kW⁻¹. The highest values were recorded for ground-mounted installations facing south. Simulations carried out using computer programs show differences between simulated and real electricity production values of 35–41% for the ENERAD software, 3–13% for the PVGIS 5.3 software, and 3–32% for the PVGIS 24 software. The most accurate forecasts were obtained for the PV2 system in the PVGIS 24 software (MPE 3%, RMSE 12%), and the most unfavorable for the same installation in the ENERAD software (MPE 41%, RMSE 48%). Full article

High-precision photovoltaic (PV) power generation prediction models are essential for ensuring secure and stable grid operation and optimized dispatch. Existing models often ignore the significant variations in PV grid-connected inverter loss distributions and exhibit inadequate data decomposition processing, which influences the accuracy of the prediction models. This paper proposes a PSO-VMD-LSTM prediction model that includes PV converter loss characteristics. Firstly, the Particle Swarm Optimization (PSO) algorithm is employed to optimize the parameters of Variational Mode Decomposition (VMD), enabling effective decomposition of data under different weather conditions. Secondly, the decomposed sub-modes are individually fed into Long Short-Term Memory (LSTM) networks for prediction, and the results are subsequently reconstructed to obtain preliminary predictions. Finally, a neural network-based equivalent model for inverter losses is constructed; the preliminary predictions are fed into this model to obtain the final prediction results. Simulation case studies demonstrate that the proposed PSO-VMD-LSTM-based model can comprehensively consider the impact of uneven converter loss distribution and effectively improve the accuracy of PV power prediction models. Full article

Recently, there has been a surge in the popularity of renewable energy systems due to their lucrative and sustainable attributes. Among these, photovoltaic (PV) systems stand out as prominent examples. Nevertheless, it is imperative to ascertain the management of waste produced by these systems in order to mitigate environmental pollution and harness their economic potential. This study aims to assess the present status and forecast the accumulation of waste generated by PV power plants in Chile. Utilizing openly available public data, a database is constructed to track the accumulation of waste. Two scenarios, namely, early-loss and regular-loss scenarios are employed to estimate the projected accumulation of PV waste. The findings indicate that by the years 2035 and 2043, the accumulation of waste is estimated to reach 100,000 tons under the early-loss scenario and regular-loss scenario. The total anticipated waste from solar PV modules is projected to be 284,906 tons, with c-Si PV modules contributing 175,595 tons to this total in Chile. Remarkably, it is determined that more than 235,000 tons of materials from this waste is recoverable, amounting to nearly USD 781 million in economic value. Silver is projected to bring the most economic value, with nearly USD 379 million, while lead, tin, cadmium, and zinc are each valued at less than USD 1 million. This study highlights the importance of promoting the sustainable development of PV systems, particularly in alignment with Sustainable Development Goals 7 (Affordable and Clean Energy) and 13 (Climate Action). Future research is expected to place greater emphasis on eco-design approaches in PV module production. Full article

With the increasing penetration of wind and photovoltaic (PV) power in modern power systems, accurate power forecasting has become crucial for ensuring grid stability and optimizing dispatch strategies. This study focuses on multiple wind farms and PV plants, where three deep learning models—Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and a hybrid Transformer–BiLSTM model—are constructed and systematically compared to enhance forecasting accuracy and dynamic responsiveness. First, the predictive performance of each model across different power stations is analyzed. The results reveal that the LSTM model suffers from systematic bias and lag effects in extreme value ranges, while Bi-LSTM demonstrates advantages in mitigating time-lag issues and improving dynamic fitting, achieving on average a 24% improvement in accuracy for wind farms and a 20% improvement for PV plants compared with LSTM. Moreover, the Transformer–BiLSTM model significantly strengthens the ability to capture complex temporal dependencies and extreme power fluctuations. Experimental results indicate that the Transformer–BiLSTM consistently delivers higher forecasting accuracy and stability across all test sites, effectively reducing extreme-value errors and prediction delays. Compared with Bi-LSTM, its average accuracy improves by 19% in wind farms and 35% in PV plants. Finally, this paper discusses the limitations of the current models in terms of multi-source data fusion, outlier handling, and computational efficiency, and outlines directions for future research. The findings provide strong technical support for renewable energy power forecasting, thereby facilitating efficient scheduling and risk management in smart grids. Full article

This study proposes a hierarchical two-layer control framework aimed at advancing the sustainability of renewable-integrated microgrids. The framework combines droop-based primary control, PI-based voltage and current regulation, and a supervisory Model Predictive Control (MPC) layer to enhance dynamic power sharing and system stability in renewable-integrated microgrids. The proposed method addresses the limitations of conventional control techniques by coordinating real and reactive power flow through an adaptive droop formulation and refining voltage/current regulation with inner-loop PI controllers. A discrete-time MPC algorithm is introduced to optimize power setpoints under future disturbance forecasts, accounting for state-of-charge limits, DC-link voltage constraints, and renewable generation variability. The effectiveness of the proposed strategy is demonstrated on a small hybrid microgrid system that serve a small community of buildings with a solar PV, wind generation, and a battery storage system under variable load and environmental profiles. Initial uncontrolled scenarios reveal significant imbalances in resource coordination and voltage deviation. Upon applying the proposed control, active and reactive power are equitably shared among DG units, while voltage and frequency remain tightly regulated, even during abrupt load transitions. The proposed control approach enhances renewable energy integration, leading to reduced reliance on fossil-fuel-based resources. This contributes to environmental sustainability by lowering greenhouse gas emissions and supporting the transition to a cleaner energy future. Simulation results confirm the superiority of the proposed control strategy in maintaining grid stability, minimizing overcharging/overdischarging of batteries, and ensuring waveform quality. Full article

To address the challenge of effective tracking of weather-induced power fluctuation trends in daytime PV power forecasting, this paper proposes a joint forecasting framework oriented to weather classification. For the weather classification module, a spectral clustering method incorporating multivariate feature fusion-based evaluation is introduced to address the limitation that conventional clustering models fail to effectively identify power fluctuations caused by dynamic weather variations. Simultaneously, to tackle non-stationary fluctuations and local abrupt changes in PV power forecasting, a non-stationary Transformer-BiLSTM model optimised using the Differentiated Creative Search (DCS) algorithm (DCS-NsT-BiLSTM)is proposed. This model enables the co-optimisation of global and local features under diverse weather patterns. The proposed method takes into consideration the climatic typology of PV power plants, thereby overcoming the insensitivity of traditional clustering models to high-dimensional non-stationary data. Furthermore, the approach utilises the novel intelligent optimisation algorithm DCS to update the key hyperparameters of the forecasting model, which in turn enhances the accuracy of day-ahead PV power generation forecasting. Applied to a photovoltaic power station in Jilin Province, China, this method reduced the mean root mean square error by 4.63% across various weather conditions, effectively validating the proposed methodology. Full article

The increasing reliance on renewable energy sources, such as photovoltaic (PV) systems, is pivotal for achieving sustainable development and addressing global energy challenges. However, short-term power forecasting for distributed PV systems often faces accuracy limitations, hindering their efficient grid integration. To address this, a novel hybrid prediction model is proposed, combining multivariate variational mode decomposition (MVMD) with a gated recurrent unit (GRU) network, an attention mechanism (ATT), and an enhanced vector weighted average algorithm (cINFO). The MVMD first decomposes historical data to reduce volatility. The INFO algorithm is then improved by integrating the crested porcupine optimizer (CPO), forming the cINFO algorithm to optimize GRU-ATT hyperparameters. An attention mechanism is incorporated to accentuate key influencing factors. The model was evaluated using the DKASC Alice Springs dataset. Results demonstrate high predictive accuracy, with mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R²) values of 0.0249, 0.0693, and 99.79%, respectively, under sunny conditions, significantly outperforming benchmark models. This confirms the model’s feasibility and superiority for short-term PV power forecasting. Full article

In this paper, an efficient hybrid photovoltaic (PV) power forecasting model is proposed to enhance the stability and accuracy of PV power prediction under typical weather conditions. First, the Improved Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) is employed to decompose both meteorological features affecting PV power and the power output itself into intrinsic mode functions. This process enhances the stationarity and noise robustness of input data while reducing the computational complexity of subsequent model processing. To enhance the detail-capturing capability of the Bidirectional Long Short-Term Memory (BiLSTM) model and improve its dynamic response speed and prediction accuracy under abrupt irradiance fluctuations, we integrate a Temporal Convolutional Network (TCN) into the BiLSTM architecture. Finally, a Multi-head Self-Attention (MHA) mechanism is employed to dynamically weight multivariate meteorological features, enhancing the model’s adaptive focus on key meteorological factors while suppressing noise interference. The results show that the ICEEMDAN-TCN-BiLSTM-MHA combined model reduces the Mean Absolute Percentage Error (MAPE) by 78.46% and 78.59% compared to the BiLSTM model in sunny and cloudy scenarios, respectively, and by 58.44% in rainy scenarios. This validates the accuracy and stability of the ICEEMDAN-TCN-BiLSTM-MHA combined model, demonstrating its application potential and promotional value in the field of PV power forecasting. Full article

Accurate very short-term load forecasting (VSTLF) is critical to ensure a secure operation of power systems under increasing uncertainty due to renewables. This study proposes an eXtreme Gradient Boosting (XGBoost)-based VSTLF model that incorporates day-ahead load forecasts (DALF) results and load variation features. DALF results provide trend information for the target time, while load variation, the difference in historical electric load, captures residual patterns. The load reconstitution method is also adapted to mitigate the forecasting uncertainty caused by behind-the-meter (BTM) photovoltaic (PV) generation. Input features for the proposed VSTLF model are selected using Kendall’s tau correlation coefficient and a feature importance score to remove irrelevant variables. A case study with real data from the Korean power system confirms the proposed model’s high forecasting accuracy and robustness. Full article

It is essential to balance energy supply and demand in residential buildings through accurate forecasting of energy use due to varying daily and seasonal residential building loads. This study demonstrates a data-driven Virtual Energy Replication Framework (VERF) to predict the behavior of residential buildings using weather forecast data. The framework integrates supervised machine learning models and time-ahead weather parameters to estimate photovoltaic (PV) power production, heat pump energy consumption, and indoor thermal comfort. The accuracy of prediction models is validated using TRNSYS simulations of a typical household in Saarbrucken, Germany, a temperate oceanic climate region. The XGBoost model exhibits the highest reliability, achieving a root mean square error (RMSE) of 0.003 kW for PV power generation and 0.025 kW for heat pump energy use, with R² scores of 0.94 and 0.87, respectively. XGBoost and random forest regression models perform well in predicting PV generation and HP electricity load, with mean prediction errors of 5.27–6% and 0–7.7%, respectively. In addition, the thermal comfort index (PPD) is predicted with an RMSE of 1.84 kW and an R² score of 0.80 using the XGBoost model. The mean prediction error remains between 2.4% (XGBoost regression) and −11.5% (lasso regression) throughout the forecasted data. Because the framework requires no real-time instrumentation or detailed energy modelling, it is scalable and adaptable for smart building energy systems, and has particular value for Building-Integrated Photovoltaics (BIPV) demonstration projects on account of its predictive load-matching capabilities. The research findings justify the applicability of VERF for efficient and sustainable energy management using weather-informed prediction models in residential buildings. Full article

Optimization dispatch with flexible load participation in new power systems significantly enhances renewable energy accommodation, though the potential of flexible loads remains underexploited. To improve renewable utilization efficiency, promote wind/PV consumption and reduce carbon emissions, this paper establishes a low-carbon economic optimization dispatch model for active distribution networks incorporating flexible loads and tiered carbon trading. First, a hybrid SSA (Sparrow Search Algorithm)–CNN-LSTM model is adopted for accurate renewable generation forecasting. Meanwhile, multi-type flexible loads are categorized into shiftable, transferable and reducible loads based on response characteristics, with tiered carbon trading mechanism introduced to achieve low-carbon operation through price incentives that guide load-side participation while avoiding privacy leakage from direct control. Considering the non-convex nonlinear characteristics of the dispatch model, an improved Beluga Whale Optimization (BWO) algorithm is developed. To address the diminished solution diversity and precision in conventional BWO evolution, Tent chaotic mapping is introduced to resolve initial parameter sensitivity. Finally, modified IEEE-33 bus system simulations demonstrate the method’s validity and feasibility. Full article

The increasing integration of renewable energy resources, driven by carbon neutrality goals, has intensified load variability, thereby making very short-term load forecasting (VSTLF) more challenging. Accurate VSTLF is essential for the reliable and economical real-time operation of power systems. This study proposes a Long Short-Term Memory (LSTM)-based VSTLF model designed to predict nationwide power system load, including renewable generation over a six-hour horizon with 15 min intervals. The model employs a reconstituted load approach that incorporates photovoltaic (PV) generation effects and computes representative weather variables across the country. Furthermore, the most informative input features are selected through a combination of correlation analyses. To further enhance input sequences, pseudo-trend components are generated using a Kalman filter-based predictor and integrated into the model input. The Kalman filter-based pseudo-trend produced an MAPE of 1.724%, and its inclusion in the proposed model reduced the forecasting error (MAPE) by 0.834 percentage points. Consequently, the final model achieved an MAPE of 0.890%, which is under 1% of the 94,929 MW nationwide peak load. Full article

Ultra-short-term photovoltaic (PV) cluster power prediction (PCPP) is crucial for intra-day energy dispatch. However, it faces significant challenges due to the chaotic nature of atmospheric systems and errors in meteorological forecasting. To address this, we propose a novel ultra-short-term PCPP strategy that introduces a dynamic smoothing mechanism for PV clusters. This strategy introduces a smoothing convergence function to quantify sequence fluctuations and employs dynamic clustering based on this function to identify PV stations with complementary smoothing effects. We model the similarities in fluctuation amplitude, trend correlation, and degree correlation among sub-cluster nodes using a spatiotemporal heterogeneous dynamic graph convolutional neural network (STHDGCN). Three dynamic heterogeneous graphs are constructed to represent these spatiotemporal evolutionary relationships. Furthermore, a bidirectional temporal convolutional neural network (BITCN) is integrated to capture the temporal dependencies within each sub-cluster, ultimately predicting the output of each node. Experimental results using real-world data demonstrate that the proposed method reduces the normalized root mean square error (NRMSE) and normalized mean absolute error (NMAE) by an average of 6.90% and 4.15%, respectively, while improving the coefficient of determination (R²) by 34.36%, compared to conventional cluster prediction approaches. Full article