Short-term PV power prediction based on XLSTM model

With the vigorous development of solar photovoltaic power generation, accurate prediction of short-term photovoltaic power generation has become an important issue. However, since the short-term power generation of photovoltaic power generation is affected by many environmental factors and has great uncertainty, the safe operation of the power grid is challenged. Therefore, it is necessary to simulate the power generation in advance through data models and predict the power generation amount. The power grid relies on the valuation and makes corresponding adjustments to maintain the stability of the power grid system.

Photovoltaic power generation prediction is crucial for the effective integration of renewable energy into the grid, real-time grid balancing, and the optimization of energy storage systems. However, PV power generation is highly dependent on environmental factors such as weather conditions. Photovoltaic power generation prediction is crucial for the effective integration of renewable energy into the grid, real-time grid balancing, and the optimization of energy storage systems. However, PV power generation is highly dependent on environmental factors such as weather conditions. Effectively integrating environmental information remains a major challenge for photovoltaic power forecasting. This study proposes a hybrid deep learning model that incorporates an adaptive neural network to capture the latent relationships between PV power generation and environmental variables, thereby enhancing forecasting accuracy. The adaptive graph neural network employs a data-driven directed graph structure, where TCN and variable interaction layers are alternately stacked to better model the spatiotemporal coupling among variables for long-term PV output forecasting. The proposed model was evaluated on three sites located in different regions, with a fixed input length of 96 and output horizons ranging from 96 to 768 steps. Compared with state-of-the-art baselines, the model achieved average improvements of 2.19% and 1.57% in MSE and MAE at a 384-step horizon, and 2.81% and 2.47% at a 768-step horizon, respectively, demonstrating superior performance in long-term PV output forecasting tasks.

Photovoltaic (PV) power generation is highly intermittent in nature and any accurate very short-term prediction can decrease the impact of its uncertainties and operation costs and boost the reliable and efficient integration of PV systems into micro/smart grids. This work develops a new generalized technique for very short-term prediction of PV power generation from the lagged power generation data using fuzzy techniques. A preprocessor extracts relevant statistical features from the PV data which are fed to the fuzzy predictor. A modified version of Wang-Mendel training algorithm is employed to directly extract the fuzzy rules from the training data pairs. This methodology exploits the limited training data more efficiently. In addition, an online additive learning routine is proposed, which enables the predictor to learn from new data while running the predictions. So, the prediction accuracy increases over time and the predictor updates to account for long-term changing conditions of weather and PV system performance and its surroundings. Numerical results of the comparison of the proposed approach with simple fuzzy and traditional artificial neural network methods on a live PV system in the United Kingdom demonstrate its improved prediction accuracy, outperforming the benchmark approaches with a normalized mean absolute error (NMAE) of 3.6%.

In order to fully exploit the relationship between temporal features in photovoltaic power generation data and improve the prediction accuracy of photovoltaic power generation, a photovoltaic power generation forecasting method is proposed based on a hybrid model of the convolutional neural network (CNN) and extreme gradient boost (XGBoost). Taking the historical data of China’s photovoltaic power plants as a sample, the high-dimensional mapping relationship of photovoltaic power generation variables is extracted based on the convolutional layer and pooling layer of the CNN network to construct a high-dimensional time-series feature vector, which is an input for the XGBoost. A photovoltaic power generation prediction model is established based on CNN-XGBoost by training CNN and XGBoost parameters. Since it is difficult for a single model to achieve optimal prediction accuracy under different weather conditions, the k-means clustering algorithm is used to group the power datasets and train independent models to improve prediction accuracy. Through the actual data verification of photovoltaic power plants, the proposed photovoltaic power generation prediction model can accurately predict the power, which shows high prediction accuracy and generalization ability compared with other methods.

With the continuous increase of solar penetration rate, it has brought challenges to the smooth operation of the power grid. Therefore, to make photovoltaic power generation not affect the smooth operation of the grid, accurate photovoltaic power prediction is required. And short-term forecasting is essential for the deployment of daily power generation plans. In this paper, A short-term photovoltaic power generation forecast method based on K-means++, grey relational analysis (GRA) and support vector regression (SVR) (Hybrid Kmeans-GRA-SVR, HKGSVR) was proposed. The historical power data was clustered through the multi-index K-means++ algorithm. And the similar days and the nearest neighbor similar day of the prediction day were selected by the GRA algorithm. Then, similar days and nearest neighbor similar days were used to train SVR to obtain an accurate photovoltaic power prediction model. Under ideal weather, the average values of MAE, RMSE, and R2 were 0.8101 kW, 0.9608 kW, and 99.66%, respectively. The average computation time was 1.7487 s, which was significantly better than the SVR model. Thus, the demonstrated numerical results verify the effectiveness of the proposed model for short-term PV power prediction.

Accurate prediction of the generation capacity of photovoltaic systems is fundamental to ensuring the stability of the grid and to performing scheduling arrangements correctly. In view of the temporal defect and the local minimum problem of back-propagation neural network, a forecasting method of power generation based on long short-term memory-back-propagation is proposed. On this basis, the traditional prediction data set is improved. According to the three traditional methods listed in this article, we propose a fourth method to improve the traditional photovoltaic power station short-term power generation prediction. Compared with the traditional method, the long short-term memory-back-propagation neural network based on the improved data set has a lower prediction error. At the same time, a horizontal comparison with the multiple linear regression and the support vector machine shows that the long short-term memory-back-propagation method has several advantages. Based on the long short-term memory-back-propagation neural network, the short-term forecasting method proposed in this article for generating capacity of photovoltaic power stations will provide a basis for dispatching plan and optimizing operation of power grid.

Photovoltaic power generation system is one of the main clean energy power generation systems at present, which plays an important role in daily production and life. However, the photovoltaic power generation system is easily affected by various factors, and the output power will be unstable in the practical application process, which will affect the power generation efficiency. In this paper, a prediction method of distributed energy grid-connected dense data based on federated learning is constructed. This method can not only realize the short-term prediction of distributed photovoltaic power generation data, but also ensure that the data can be encrypted and modeled, thus solving the “digital island” problem. The model evaluation shows that the method in this paper performs well in short-term photovoltaic power generation prediction, and it can predict the short-term power generation of different photovoltaic power stations with high prediction accuracy. This method is of great significance to improve the management and scheduling ability and energy utilization rate of distributed photovoltaic power generation systems.

Photovoltaic power generation depends on weather conditions, which has the characteristics of randomness, volatility and intermittence. In order to ensure the security and stability of the grid connected process and make full use of the advantages of photovoltaic power generation, this paper proposes Bayesian optimization algorithm based on grey correlation to predict the power of photovoltaic power generation. The gray correlation selects the similar correlation data from the original data set, and normalizes the data sample set to eliminate the influence of data size caused by different data dimensions. Bayesian optimization algorithm can effectively prevent over fitting by adding regularization term and super parameter α, β. Experiments show that the photovoltaic power prediction method based on Bayesian optimization algorithm under grey correlation has higher accuracy and robustness, and can provide more comprehensive information for power grid dispatching and control.

In recent years, the photovoltaic power generation has obvious intermittent, randomness and volatility, and high permeability photovoltaic will have a huge impact on the safety and stability of the grid. The prediction of photovoltaic power generation is to improve the quality of photovoltaic grid, optimize grid scheduling, and ensure the basic technology of power grid safety and stability. In order to improve the prediction accuracy of photovoltaic power generation, this article will comprehensively carding and compare from 3 dimensions: photovoltaic power generation and meteorological factor correlation analysis, similar day selection, prediction method based on machine learning, and summarize the advantages and disadvantages of various methods. Further research has been put forward accordingly.

Accurate short-term solar and wind power predictions play an important role in the planning and operation of power systems. However, the short-term power prediction of renewable energy has always been considered a complex regression problem, owing to the fluctuation and intermittence of output powers and the law of dynamic change with time due to local weather conditions, i.e. spatio-temporal correlation. To capture the spatio-temporal features simultaneously, this paper proposes a new graph neural network-based short-term power forecasting approach, which combines the graph convolutional network (GCN) and long short-term memory (LSTM). Specifically, the GCN is employed to learn complex spatial correlations between adjacent renewable energies, and the LSTM is used to learn dynamic changes of power generation curves. The simulation results show that the proposed hybrid approach can model the spatio-temporal correlation of renewable energies, and its performance outperforms popular baselines on real-world datasets.

Photovoltaic power generation is fluctuation and intermittent, and the grid-connected operation of large-scale photovoltaic power plants may have an impact on the safe and stable economic operation of the power system. An effective way to solve the problem is to make scientific forecasts of the output power of PV power plants. In this paper, Back Propagation (BP) and Radial Basis Function (RBF) neural network prediction models are established by using the historical values of actual power generation of Guhe Runneng Photovoltaic Power Station in Gaotang County of Liaocheng City in 2017. According to the characteristics of photovoltaic power fluctuation, the output power is treated as a set of digital signals for short-term PV power prediction, and a prediction model based on the Hilbert Huang Transform (HHT) power data decomposition is proposed. Through the analysis of an example, it can be concluded that after HHT, the prediction effect is significantly improved, and the accuracy of PV power prediction is improved. Moreover, compared with BP neural network, RBF neural network has smaller prediction error.

Photovoltaic power generation represents a pivotal form of renewable energy. Accurate power prediction is therefore of paramount importance to the advancement of the energy revolution. This paper examines the Yumara Solar System PV plant in the central region of Australia as a case study. The research begins with the use of the k nearest neighbor algorithm (KNN) and extreme gradient boosting algorithm (XGBoost) to pre-process the PV power generation data from the PV plant. This is followed by the creation of line graphs, which illustrate the changes in variables in the PV power generation data from the PV plant in 2022. Subsequently, this paper employs the dung beetle optimization (DBO) algorithm to optimize the long short-term memory (LSTM) network and construct the DBO-LSTM model. The DBO-LSTM model is further integrated with the extended belief rule base (EBRB) model under the integrated learning framework. The weight assignments are reasonable, in accordance with the model features, for the purpose of constructing the DBO-LSTM-EBRB model for predicting the capacity of photovoltaic power generation. This paper presents experimental results that demonstrate superior predictive performance of the DBO-LSTM-EBRB model in comparison to the traditional LSTM and EBRB models. The R2 value achieved for the DBO-LSTM-EBRB model ranges from 0.950 to 0.978.

Reliable prediction of photovoltaic power generation is key to the efficient management of energy systems in response to the inherent uncertainty of renewable energy sources. Despite advances in weather forecasting, photovoltaic power prediction accuracy remains a challenge. This study presents a novel approach that combines genetic algorithms and dynamic neural network structure refinement to optimize photovoltaic prediction. This methodology dynamically adjusts the neural network parameters during training, including the number of neurons, transfer functions, weights, and biases, to minimize the root mean square error. Evaluation was performed on twelve representative days using annual, monthly, and seasonal data, and a comparison was made with multiple linear regression and nonlinear autoregressive neural network models, demonstrating the approach’s effectiveness. Evaluation metrics such as mean square error, R-value, and mean percentage error reveal promising prediction accuracy. MATLAB is used for modeling, training, and testing, and a real 4.2 kW PV plant is used for validation. The results indicate significant improvements, with mean square errors as low as 20 W on cloudy days and 175 W on sunny days. The proposed methodology achieves prediction versus target regressions consistency, with R values ranging from 0.95824 to 0.99980, highlighting its efficiency in providing reliable predictions of PV power generation.

Photovoltaic power generation is affected by complex and changeable weather conditions, which has the characteristics of volatility and intermittence, resulting in difficulties in photovoltaic grid connection. In order to ensure the stable operation of power system after large-scale photovoltaic grid connection, it is necessary to accurately predict the photovoltaic power in advance. Different weather conditions will lead to great differences in the irradiance actually received by photovoltaic arrays, so a single prediction model is not enough to cope with the prediction of photovoltaic power generation under various complex weather changes. The precision of photovoltaic data clustering is not the key to improve the precision of photovoltaic data classification. To solve the above problems, this paper proposes a matrix algorithm based on solar photovoltaic power prediction.

Accurate prediction of photovoltaic power is of great significance to the safe operation of power grids. In order to improve the prediction accuracy, a similar day clustering convolutional neural network (CNN)–informer model was proposed to predict the photovoltaic power. Based on correlation analysis, it was determined that global horizontal radiation was the meteorological factor that had the greatest impact on photovoltaic power, and the dataset was divided into four categories according to the correlation between meteorological factors and photovoltaic power fluctuation characteristics; then, a CNN was used to extract the feature information and trends of different subsets, and the features output by CNN were fused and input into the informer model. The informer model was used to establish the temporal feature relationship between historical data, and the final photovoltaic power generation power prediction result was obtained. The experimental results show that the proposed CNN–informer prediction method has high accuracy and stability in photovoltaic power generation prediction and outperforms other deep learning methods.