Explore the vast range of titles available.

The accurate prediction of photovoltaic (PV) power is crucial for planning, constructing, and scheduling high‐penetration distributed PV power systems. Traditional point prediction methods suffer from instability and lack reliability, which can be effectively addressed through interval prediction. This study proposes a short‐term PV power interval prediction method based on the framework of sparrow search algorithm (SSA)‐variational mode decomposition (VMD)‐convolutional neural network (CNN)‐gate recurrent unit (GRU). First, PV data undergo similar day clustering based on permutation entropy and VMD is applied to solar radiation signals with high correlation. Then, the hyperparameters of GRU are optimized by SSA according to the comprehensive evaluation indicator of interval prediction proposed in this study. Subsequently, quantile prediction results are obtained based on CNN‐GRU using the optimal parameters from SSA optimization. Finally, the prediction interval is composed of multiple quantile prediction results. A MATLAB R2022b program is developed to compare different prediction methods. The results demonstrate that compared to single neural network methods, the proposed method effectively improves the coverage width‐based criterion. In the interval prediction of sunny and rainy similar days, the comprehensive evaluation indicators of the proposed method are only 54.3% and 37.4% of the single GRU, respectively, indicating significantly improved interval prediction accuracy. Gate recurrent unit (GRU) network structure.

In view of the significant volatility and randomness of photovoltaic power, traditional forecasting methods are unable to meet the requirements for high prediction accuracy. It is urgent to develop a prediction model with high accuracy and strong stability. Therefore, this study proposes a novel multi-step short-term photovoltaic power prediction model based on Variational Mode Decomposition (VMD), Improved Whale Migration Algorithm (IWMA), and Convolutional Neural Network-Kernel Extreme Learning Machine structure (CNN-KELM). Initially, VMD is employed to decompose the original power sequence to reduce its nonlinearity and complexity. Furthermore, we construct a CNN-KELM hybrid model, and the IWMA algorithm, which integrates chaotic mapping, dynamic inertia weight and dynamic factor adjustment with Lévy flight strategy, is introduced to optimize the model parameters, thereby enhancing the prediction performance. Moreover, for each component, a VMD-CNN-IWMA-KELM forecasting model is established, and the predicted results are reconstructed and superimposed to obtain the final prediction. Finally, the performance of the proposed model is validated using two datasets. The experimental results show that the proposed model in this paper shows significant advantages in accuracy and stability. Its goodness-of-fit values reach 96.71% and 92.33%, respectively, effectively improving the accuracy of photovoltaic power prediction.

Ultra-short-term PV power prediction (USTPVPP) results provide a basis for the development of intra-day rolling power generation plans. However, due to the feature information and the unpredictability of meteorology, the current ultra-short-term PV power prediction accuracy improvement still faces technical challenges. In this paper, we propose a combined prediction framework that takes into account the reconfiguration of the predictable components of PV stations and the spatiotemporal heterogeneous maps. A circuit singular spectral decomposition (CISSD) intrinsic predictable component extraction method is adopted to obtain specific frequency components in sensitive meteorological variables, a mechanism based on radiation characteristics and PV power trend predictable component extraction and reconstruction is proposed to enhance power predictability, and a spatiotemporal heterogeneous graph neural network (STHGNN) combined with a Non-stationary Transformer (Ns-Transformer) combination architecture to achieve joint prediction for different PV components. The proposed method is applied to a PV power plant in Gansu, China, and the results show that the prediction method based on the proposed combined spatio-temporal heterogeneous graph neural network model combined with the proposed predictable component extraction achieves an average reduction of 6.50% in the RMSE, an average reduction of 2.50% in the MAE, and an average improvement of 11.93% in the R2 over the direct prediction method, respectively.

With the rapid development of renewable energy, photovoltaic power generation has become a key part of the global energy transition. Short-term photovoltaic prediction is critical for intra-day real-time power grid dispatching, and enhancing its accuracy is a key research focus. However, existing methods still have limitations in handling complex nonlinear relationships in photovoltaic temporal data. To tackle this, this paper proposes a new model combining Long Short-Term Memory (LSTM), Differential Transformer (DiffTransformer), and Multi-Objective Escape Algorithm (MOESC) for short-term photovoltaic power prediction optimization: Preprocessed data is input into the LSTM-Differential Transformer model, with the Differential Transformer encoder capturing fine-grained temporal changes via optimized multi-head attention and rotary positional encoding, and the LSTM decoder integrating local temporal information for power prediction. Subsequently, Pareto-improved MOESC performs multi-objective optimization on the model’s key parameters (balancing RMSE , MAE , and R² ), with the optimal parameters selected from the Pareto frontier. Experiments based on the Guoneng Rixin photovoltaic dataset show that, with user-defined weights ( RMSE : 30%, MAE : 30%, R² : 40%), this method outperforms XGBoost, LightGBM, SVR, LSTM, GRU and the unoptimized LSTM-Differential Transformer model in photovoltaic power prediction. It not only can effectively improve prediction accuracy but also exhibits better stability compared with the unoptimized LSTM-Differential Transformer model.

Photovoltaic (PV) power generation is highly stochastic and volatile, a trait that presents a notable challenge to the prediction accuracy of distributed PV systems. To address this challenge, this study proposes a short-term photovoltaic power prediction strategy that integrates variational modal decomposition (VMD) for feature extraction with an improved RIME (IRIME) optimization algorithm for parameter optimization. Firstly, the raw PV power data are split into several intrinsic mode functions (IMFs) using VMD. The decomposed IMFs are reconstructed by using the sample entropy (SE) method, and a new subsequence with enhanced features is obtained. Secondly, a bidirectional gated recurrent unit (BIGRU) prediction model is established, and its structural parameters are optimized by the IRIME algorithm. Finally, the prediction results of each subsequence are summarized to obtain the final prediction value. Information from a centralized PV power station located in southern China is employed to verify the suggested prediction model. Experimental findings indicate that in comparison with other models, the proposed model achieves the smallest PV power prediction error; the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) of the proposed model are reduced at least by 21.95%, 3.03%, and 12.33%, respectively. The coefficient of determination (R2) is increased at least by 10.56‰. The method presented in this research is capable of improving prediction accuracy efficiently and holds specific engineering practicality.

The output power of photovoltaic (PV) systems is influenced by various environmental factors, exhibiting strong nonlinearity and non-stationarity, which poses significant challenges for accurate forecasting. To address these issues, this paper proposes a short-term PV power forecasting method based on wavelet convolutional neural networks and an improved Transformer. First, the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is employed to decompose the original PV power sequence into several intrinsic mode functions (IMFs). Fuzzy entropy is then utilized to evaluate the complexity of each component, and subsequences with similar entropy values are reconstructed to reduce the non-stationarity of the original series. Subsequently, Pearson correlation coefficients and the maximal information coefficient (MIC) are applied to capture both linear and nonlinear relationships between each reconstructed component and meteorological features, enabling the selection of strongly correlated variables. On this basis, a wavelet convolutional network (WTConv) is introduced to perform multi-scale decomposition and frequency-band feature extraction on the reconstructed components by integrating wavelet transform with convolution operations, effectively expanding the receptive field and extracting deep-seated features of the sequences. Finally, an improved iTransformer model is adopted for time-series modeling, leveraging its inverted encoding structure and self-attention mechanism to fully capture long-term dependencies among multivariate variables. The proposed model is validated using actual power data from a PV plant in Ningxia, China, across four seasons. Comprehensive experiments, including ablation studies, comparative analyses, loss function convergence evaluation, and Diebold–Mariano significance tests, are conducted to thoroughly assess the model’s effectiveness and superiority. Experimental results demonstrate that the proposed model achieves excellent prediction accuracy and stability in spring, summer, autumn, and winter, showing strong potential for engineering applications.

Due to the uncertainty of weather conditions and the nonlinearity of high-dimensional data, as well as the need for a continuous and stable power supply to the power system, traditional regression analysis and time series forecasting methods are no longer able to meet the high accuracy requirements of today's PV power forecasting. To significantly improve the prediction accuracy of short-term PV output power, this paper proposes a short-term PV power forecasting method based on a hybrid model of temporal convolutional networks and gated recurrent units with an efficient channel attention network (TCN-ECANet-GRU) using the generated data of an Australian PV power station as the research object. First, temporal convolutional networks (TCNs) are used as spatial feature extraction layers, and an efficient channel attention network (ECANet) is embedded to enhance the feature capture capability of the convolutional network. Then, the GRU is used to extract the timing information for the final prediction. Finally, based on the experimental validation, the TCN-ECANet-GRU method generally outperformed the other baseline models in all four seasons of the year according to three performance assessment metrics: the normalized root mean square error (RMSE), normalized mean absolute error (MAE) and coefficient of determination (R 2 ). The best RMSE, MAE and R 2 reached 0.0195, 0.0128 and 99.72%, respectively, with maximum improvements of 11.32%, 8.57% and 0.38%, respectively, over those of the suboptimal model. Therefore, the model proposed in this paper is effective at improving prediction accuracy. Using the proposed method, this paper concludes with multistep predictions of 3, 6, and 9 steps, which also indicates that the proposed method significantly outperforms the other models.

Global horizontal irradiance prediction is essential for balancing the supply–demand and minimizing the energy costs for effective integration of solar photovoltaic system in electric power grid. However, its stochastic nature makes it difficult to get accurate prediction results. This study aims to develop a hybrid deep learning model that integrates a Convolutional Neural Network and Stacked Long Short-Term Memory (CNN-SLSTM) to predict the daily average global solar irradiance using real time meteorological parameters and daily solar irradiance data recorded in the study site. First, we have selected 14 significant relevant features from the dataset using recursive feature elimination techniques. The hyperparameters of the developed models are optimized using metaheuristic algorithm, a Slime Mould Optimization method. The efficacy of the model performance is evaluated using tenfold cross validation techniques. By using statistical performances metrics, the predictive performance of the developed model is compared with Gated Recurrent Unit, LSTM, CNN-LSTM, SLSTM and machine learning regressor models like Support Vector Machine, Decision Tree, and Random Forest. From the experimental results, the developed CNN-SLSTM model outperformed other models with a MSE, R 2 and Adj_R 2 of 0.0359, 0.9790 and 0.9789, respectively.

Due to the development of photovoltaic (PV) technology and the support from governments across the world, the conversion efficiency of solar energy has been improved. However, the PV power output is influenced by environment factors, resulting in features of randomness and intermittency. These features may have a negative influence on power systems. As a result, accurate and timely power prediction data is necessary for power grids to absorb solar energy. In this paper, we propose a new PV power prediction model based on the Gradient Boost Decision Tree (GBDT), which ensembles several binary trees by the gradient boosting ensemble method. The Gradient Boost method builds a strong learner by combining weak learners through iterative methods and the Decision Tree is a basic classification and regression method. As an ensemble machine learning algorithm, the Gradient Boost Decision Tree algorithm can offer higher forecast accuracy than one single learning algorithm. So GBDT is of value in both theoretical research and actual practice in the field of photovoltaic power prediction. The prediction model based on GBDT uses historical weather data and PV power output data to iteratively train the model, which is used to predict the future PV power output based on weather forecast data. Simulation results show that the proposed model based on GBDT has advantages of strong model interpretation, high accuracy, and stable error performance, and thus is of great significance for supporting the secure, stable and economic operation of power systems.

Recently, with the development of renewable energy technologies, photovoltaic (PV) power generation is widely used in the grid. However, as PV power generation is influenced by external factors, such as solar radiation fluctuation, PV output power is intermittent and volatile, and thus the accurate PV output power prediction is imperative for the grid stability. To address this issue, based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), improved artificial rabbits optimization (IARO) and convolutional bidirectional long short-term memory (CBiLSTM), a new hybrid model denoted by CEEMDAN-IARO-CBiLSTM is proposed. In addition, inputs of the proposed model are optimized by analyzing influential factors of PV output power with Pearson correlation coefficient method. In order to verify the prediction accuracy, CEEMDAN-IARO-CBiLSTM is compared with other well-known methods under different weather conditions and different seasons. Specifically, for different weather conditions, MAE and RMSE of the proposed model decrease by at least 0.329 and 0.411, 0.086 and 0.021, and 0.140 and 0.220, respectively. With respect to different seasons, MAE and RMSE of the proposed model decrease by at least 0.270 and 0.378, 0.158 and 0.209, 0.210 and 0.292, and 1.096 and 1.148, respectively. Moreover, two statistical tests are conducted, and the corresponding results show that the prediction performance of CEEMDAN-IARO-CBiLSTM is superior to other well-known methods.