K-means clustering based photo voltaic power forecasting using artificial neural network, particle swarm optimization and support vector regression

Short-term PV power prediction based on meteorological similarity days and SSA-BiLSTM

Deep attention ConvLSTM-based adaptive fusion of clear-sky physical prior knowledge and multivariable historical information for probabilistic prediction of photovoltaic power

Due to the large scale of grid-connected photovoltaic (PV) power generation installations, accurate PV power forecasting is critical to the safe and economic operation of the electric power system. In the paper, by analyzing the influence of external ambient factors and the changing characteristics of PV modules with time, it is found that PV power generation is a nonlinear and time-varying process. This suggests that a certain single forecasting model is inadequate for representing actual generation characteristics, and it is difficult to obtain an accurate forecasting result. An adaptive back propagation (BP) neural network model adopting scrolling time window is proposed to solve the problem. Via an update of the training data of BP neural network with the scrolling time window, the forecasting model adapts to time and a changing external environment with the required modeling precision. Meanwhile, through evaluation of the forecasting performance in different time windows, an optimized time window can be determined to guarantee accuracy. Finally, using the actual operation data of a PV plant in Beijing, the approach is validated as being applicable for PV power forecasting and is able to effectively respond to the dynamic change of the PV power generation process. This improves the forecasting accuracy and also reduces computation complexity as compared with the conventional BP neural network algorithm.

In recent years, photovoltaic (PV) power generation has been developing rapidly, but its power instability poses a challenge to the stable operation of power systems. In this study, three machine learning models, CatBoost, Random Forest, and Support Vector Regression (SVR), were used for PV power prediction, and it was found that the SVR model performed the best, with the highest fit between predicted and true values (R² ≈ 0. 95) and the smallest fluctuation in residual error (-50% to 50%). Through SHAP analysis, the study reveals that illumination intensity is the most critical variable affecting PV power generation with a contribution of more than 70%. Additional machine learning models and optimization algorithms can be further explored in the future to improve prediction performance. In summary, this study provides an effective combinatorial machine learning method for high-precision PV power prediction, revealing the dominant role of key factors such as illumination intensity on the prediction results. Future research directions include optimizing the model structure and integrating multi-source data to further improve the accuracy and reliability of PV power prediction.

Accurate photovoltaic (PV) power forecasting is essential for the stable and reliable operation of PV power generation systems. Recently, various deep learning- (DL-) based forecasting models have been proposed for accurate forecasting, but newly built systems cannot benefit from them due to the absence of PV power data. Although zero-shot methods based on single site can be used for PV power forecasting, they suffer from performance degradation problems when the characteristics of the source data and target data are different. To address this issue, we propose a novel zero-shot PV power forecasting scheme that leverages historical data from multiple PV generation systems at different sites. The proposed scheme constructs an individual forecasting model using historical data from each PV generation system. Then, two correlation coefficients are calculated for each forecasting model: one based on the correlation between the input variables of the source data and target data and the other on the correlation between the input variables and output variables of the source data. Lastly, the final forecasting value is calculated as a weighted sum of the predicted values of the constructed forecasting models for the input variables of the target data. In the extensive experiments for diverse DL models for forecasting, correlation coefficient types for weights, and data time intervals, the combination of recurrent neural network, Pearson’s correlation coefficient, and solar-noon time yielded the best prediction performance, with an improvement of up to 34.47% in mean absolute error and up to 15.94% in root mean square error compared to the best single-site zero-shot prediction. In addition, in experiments on PV power data from 9 cities in Korea using this combination, the proposed scheme achieved the best predictive performance in almost all cases and the second-best performance with a very narrow margin only in a few cases.

Photovoltaic (PV) power classification model has become a significant alternative to PV power point forecasting model in which classes of future PV powers are estimated. In this work, a novel PV power classification model which could be realized using feed forward neural network (FFNN) trained with a hybrid meta-heuristic approach is proposed to forecast the classes of future PV powers. The insisted classification model is implemented to classify three classes of PV power for the location Oregon city of United States of America (USA) with latitude 43.8041 0 N , longitude 120.5542 0 W , and altitude of 1124 m. The meta-heuristic approach comprising of Levy Flight (Levy)-Sine Cosine Algorithm (SCA)-Particle Swarm Optimization (PSO) is utilized for training the parameters of FFNN by minimizing the root mean square error RMSE . The results disclose that the proposed approach attained the most accurate prediction of PV power with % MAPE = 2.38 for the month of November as well as high classification accuracy with PCA = 94.96 % in the month of April. Based on the obtained results, the proposed LF-SCA-PSO trained FFNN outperformed the existing hybrid models in both prediction and classification and hence possess the potential to be a new alternative to assist engineers in predicting the PV power of solar systems at short- and long-time horizons.

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.

Time series forecasting for hourly photovoltaic power using conditional generative adversarial network and Bi-LSTM

Photovoltaic (PV) power forecasting urges in economic and secure operations of power systems. To avoid an inaccurate individual forecasting model, we propose an approach for a one-day to three-day ahead PV power hourly forecasting based on the stacking ensemble model with a recurrent neural network (RNN) as a meta-learner. The proposed approach is built by using real weather data and forecasted weather data in the training and testing stages, respectively. To accommodate uncertain weather, a daily clustering method based on statistical features, e.g., daily average, maximum, and standard deviation of PV power is applied in the data sets. Historical PV power output and weather data are used to train and test the model. The single learner considered in this research are artificial neural network, deep neural network, support vector regressions, long short-term memory, and convolutional neural network. Then, RNN is used to combine the forecasting results of each single learner. It is also important to observe the best combination of the single learners in this paper. Furthermore, to compare the performance of the proposed method, a random forest ensemble instead of RNN is used as a benchmark for comparison. Mean relative error (MRE) and mean absolute error (MAE) are used as criteria to validate the accuracy of different forecasting models. The MRE of the proposed RNN ensemble learner model is 4.29%, which has significant improvements by about 7–40%, 7–30%, and 8% compared to the single models, the combinations of fewer single learners, and the benchmark method, respectively. The results show that the proposed method is promising for use in real PV power forecasting systems.

The intermittence and fluctuation of photovoltaic power generation seriously affect output power reliability, efficiency, fault detection of photovoltaic power grid, etc. The precise forecasting of photovoltaic power generation is the critical method to solve the above limitations. Current photovoltaic power generation forecasting methods generally usually adopt meteorological data and historical continuous photovoltaic power generation as inputs, but they do not take into account historical periodic photovoltaic power generation as inputs, which makes the existing methods inadequate in learning time correlation. Therefore, to further study the time correlation for improving the prediction accuracy, an LSTM-FC deep learning algorithm composed of long-term short-term memory (LSTM) and fully connected (FC) layers is proposed. The double-branch input of the model enables it not only to consider the impact of meteorological data on power generation but also to consider time continuity and periodic dependence, thereby improving the prediction accuracy to a certain extent. In this paper, meteorological data, historical continuous data, and historical periodic data are used as experimental data, and these three types of data are combined into different input forms to evaluate and compare LSTM-FC with other baseline models, including support vector machines (SVM), gradient boosting decision tree (GBDT), generalized regression neural network (GRNN), feedforward neural network (FFNN), and LSTM. The simulation results show that the accuracy of the models with meteorological data, continuous data, and periodic data as input is higher than that of other input forms, and the accuracy of LSTM-FC is the highest among these models, and its root mean square error (RMSE) is 11.79% higher than that of SVM.

Traditional photovoltaic (PV) power prediction methods in microgrids are insensitive to weather characteristics, and fluctuations in weather data can lead to insufficient PV power prediction accuracy. Therefore, this paper proposes a short-term PV power prediction method for microgrids based on FCM–QPSO–LSTM, which is used to predict PV power under different seasons and weather types. First, the fuzzy [Formula: see text]-means clustering algorithm (FCM) is used to perform clustering analysis on multi-source weather data, and the initial dataset is clustered into sunny, cloudy, and rainy days. Second, to address the shortcomings of a single long-short-term memory (LSTM) model in terms of prediction accuracy and stability, the quantum particle swarm optimization (QPSO) algorithm is used to optimize the model of the LSTM network, and the QPSO–LSTM prediction model is constructed. By introducing quantum dynamic inertia weights and adaptive learning factors, the QPSO algorithm enhances the global search capability and local search capability, which greatly improves the prediction accuracy and stability of the prediction model. Finally, the average absolute percentage error and root mean square error (RMSE) are applied as evaluation indexes to assess the performance of the prediction model. The simulation results show that the QPSO–LSTM model is better than the single LSTM model and the traditional PSO–LSTM model in terms of the average absolute percentage error and RMSE indicators and is able to make accurate predictions of short-term PV power under different seasons and weather types.

Short-term photovoltaic power forecasting method based on irradiance correction and error forecasting

In order to reduce the impact of photovoltaic power output uncertainty on the economic operation of power system, this paper studies the problem of controllable output power of photovoltaic power generation system. In grid connected photovoltaic energy storage system, typical photovoltaic power generation capacity curves obtained by statistical analysis, using particle swarm optimization algorithm to optimize the economic model under different power system output control, when the lowest total cost is the optimal selection of the battery capacity. The battery capacity is determined, the photovoltaic power generation pre measurement, using particle swarm optimization algorithm to optimize the system economic operation model, each time the amount of output of photovoltaic power plant is expected to system debugging, the specific numerical output. Combined with the output of photovoltaic power generation, the output power of the battery is calculated by the analysis and calculation unit, and the economic operation of the grid connected photovoltaic energy storage power generation system is realized through the DC/DC control of the battery charging and discharging.

A photovoltaic (PV)-powered electric motor is used for hose-drawn traveler driving instead of a water turbine to achieve high transmission efficiency. PV power generation (PVPG) is affected by different meteorological conditions, resulting in different power generation of PV panels for a hose-drawn traveler. In the above situation, the hose-drawn traveler may experience deficit power generation. The reasonable determination of the PV panel capacity is crucial. Predicting the PVPG is a prerequisite for the reasonable determination of the PV panel capacity. Therefore, it is essential to develop a method for accurately predicting PVPG. Extreme gradient boosting (XGBoost) is currently an outstanding machine learning model for prediction performance, but its hyperparameters are difficult to set. Thus, the XGBoost model based on particle swarm optimization (PSO-XGBoost) is applied for PV power prediction in this study. The PSO algorithm is introduced to optimize hyperparameters in XGBoost model. The meteorological data are segmented into four seasons to develop tailored prediction models, ensuring accurate prediction of PVPG in four seasons for hose-drawn travelers. The input variables of the models include solar irradiance, time, and ambient temperature. The prediction accuracy and stability of the model is then assessed statistically. The predictive accuracy and stability of PV power prediction by the PSO-XGBoost model are higher compared to the XGBoost model. Finally, application of the PSO-XGBoost model is implemented based on meteorological data.

Deep multi-attribute spatial–temporal graph convolutional recurrent neural network-based multivariable spatial–temporal information fusion for short-term probabilistic forecast of multi-site photovoltaic power