Prediction Of Photovoltaic Power Generation Research Articles

Optimization of Microgrid Dispatching by Integrating Photovoltaic Power Generation Forecast

In order to address the impact of the uncertainty and intermittency of a photovoltaic power generation system on the smooth operation of the power system, a microgrid scheduling model incorporating photovoltaic power generation forecast is proposed in this paper. Firstly, the factors affecting the accuracy of photovoltaic power generation prediction are analyzed by classifying the photovoltaic power generation data using cluster analysis, analyzing its important features using Pearson correlation coefficients, and downscaling the high-dimensional data using PCA. And based on the theories of the sparrow search algorithm, convolutional neural network, and bidirectional long- and short-term memory network, a combined SSA-CNN-BiLSTM prediction model is established, and the attention mechanism is used to improve the prediction accuracy. Secondly, a multi-temporal dispatch optimization model of the microgrid power system, which aims at the economic optimization of the system operation cost and the minimization of the environmental cost, is constructed based on the prediction results. Further, differential evolution is introduced into the QPSO algorithm and the model is solved using this improved quantum particle swarm optimization algorithm. Finally, the feasibility of the photovoltaic power generation forecasting model and the microgrid power system dispatch optimization model, as well as the validity of the solution algorithms, are verified through real case simulation experiments. The results show that the model in this paper has high prediction accuracy. In terms of scheduling strategy, the generation method with the lowest cost is selected to obtain an effective way to interact with the main grid and realize the stable and economically optimized scheduling of the microgrid system.

Ultra-Short-Term Photovoltaic Power Prediction Based on BiLSTM with Wavelet Decomposition and Dual Attention Mechanism

Photovoltaic power generation relies on sunlight conditions, and traditional prediction models find it difficult to capture the deep features of power data, resulting in low prediction accuracy. In addition, there are problems such as outliers and missing values in the data collected on site. This article proposes an ultra-short-term photovoltaic power generation prediction model based on wavelet decomposition, a dual attention mechanism, and a bidirectional long short-term memory network (W-DA-BiLSTM), aiming to address the limitations of existing deep learning models in processing nonlinear data and automatic feature extraction and optimize for the common problems of outliers and missing values in on-site data collection. This model uses the quartile range method for outlier detection and multiple interpolation methods for missing value completion. In the prediction section, wavelet decomposition is used to effectively handle the volatility and nonlinear characteristics of photovoltaic power generation data, while the bidirectional long short-term memory network (LSTM) structure and dual attention mechanism enhance the model’s comprehensive learning ability for time series data. The experimental results show that compared with the SOTA method, the model proposed in this paper has higher accuracy and efficiency in predicting photovoltaic power generation and can effectively address common random fluctuations and nonlinear problems in photovoltaic power generation.

Nature-Inspired Optimization with Deep Learning-Enabled Sustainable Predictive Model for Intelligent Systems

Solar energy can be considered as an alternate solution to conventional energy sources. Short-term Photovoltaic (PV) Power Generation (PVPG) prediction methods are essential stabilize power integration among PV and smart grids. The PVPG generation process is highly dependent on climatic conditions and therefore high intermittent. Highly accurate PVPG prediction of PVPG acts based on the generation, transmission and dispersion of electricity, confirming the stability and dependability of power system. The current progress of Machine Learning (ML) and Deep Learning (DL) approaches enables for designing of accurate PVPG prediction models. In this view, this paper develops a new Badger Optimization with Deep Learning Enabled PV Power Generation Predictive (HBODL-PVPGP) model. The presented HBODL-PVPGP model enables to forecast of the PVPG process. To accomplish this, the HBODL-PVPGP model initially investigates the features depending upon intrinsic characteristics earlier in the learning process. In addition, the Bidirectional Gated Recurrent Unit (BiGRU) model is implemented for forecasting process. The performance of the BiGRU model can be improvised by the design of HBO-based hyperparameter tuning procedure. For ensuring the enhanced performance of the HBODL-PVPGP model, an extensive range of experimental study was effectuated and the results were investigated under various factors. The result highlighted the precipitated performance of the HBODL-PVPGP procedure on the current algorithms.

Spatio-Temporal Photovoltaic Power Prediction with Fourier Graph Neural Network

The strong development of distributed energy sources has become one of the most important measures for low-carbon development worldwide. With a significant quantity of photovoltaic (PV) power generation being integrated to the grid, accurate and efficient prediction of PV power generation is an essential guarantee for the security and stability of the electricity grid. Due to the shortage of data from PV stations and the influence of weather, it is difficult to obtain satisfactory performance for accurate PV power prediction. In this regard, we present a PV power forecasting model based on a Fourier graph neural network (FourierGNN). Firstly, the hypervariable graph is constructed by considering the electricity and weather data of neighbouring PV plants as nodes, respectively. The hypervariance graph is then transformed in Fourier space to capture the spatio-temporal dependence among the nodes via the discrete Fourier transform. The multilayer Fourier graph operator (FGO) can be further exploited for spatio-temporal dependence information. Experiments carried out at six photovoltaic plants show that the presented approach enables the optimal performance to be obtained by adequately exploiting the spatio-temporal information.

Short-Term Photovoltaic Power Forecasting Using PV Data and Sky Images in an Auto Cross Modal Correlation Attention Multimodal Framework

The accurate prediction of photovoltaic (PV) power generation is crucial for improving virtual power plant (VPP) efficiency and power system stability. However, short-term PV power forecasting remains highly challenging due to the significant impact of weather changes, especially the complexity of cloud motion. To this end, this paper proposes an end-to-end innovative deep learning framework for data fusion based on multimodal learning, which utilizes a new auto cross modal correlation attention (ACMCA) mechanism designed in this paper for feature extraction and fusion by combining historical PV power generation time-series data and sky image data, thereby enhancing the model’s prediction performance under complex weather conditions. In this paper, the effectiveness of the proposed model was verified through a large number of experiments, and the experimental results showed that the model’s forecast skill (FS) reached 24.2% under all weather conditions 15 min in advance, and 24.32% under cloudy conditions with the largest fluctuations. This paper also compared the model with a variety of existing unimodal and multimodal models, respectively. The experimental results showed that the model in this paper outperformed other benchmark methods in all indices under different weather conditions, demonstrating stronger adaptability and robustness.

Bagging ensemble of artificial neural networks with weighted averaging and grey wolf optimization for solar photovoltaic power generation prediction

ABSTRACT Solar power generation is highly uncertain and intermittent due to its strong dependence on climate and meteorological factors. In this study, we have proposed a bagging ensemble of artificial neural network (BagANN) model with weighted averaging optimized using grey wolf optimization (GWO) and investigate the impact of climatic and meteorological factors on the output power prediction of the solar power plant. Initially, we have preprocessed the data by applying feature engineering, normalization techniques, and appropriate features are selected for power prediction. The hyperparameters of the BagANN model are tuned and optimized using the GWO technique to improve the models predictive accuracy. Experiments are conducted to evaluate the generalization performance of the developed model using five-fold and 10-fold cross-validation techniques. The performance results are assessed using RMSE, coefficient of determination ( R 2 ), adjusted ( R 2 ), and the MAPE. Results show that the BagANN with weighted averaging has a higher level of precision with R 2 of 98.5% predictive accuracy, has less prediction errors with 0.1263 MSE and 0.0267 MAPE than the other developed models. From the result, it is evident that the BagANN model has significant improvements compared to other models available in the literature in terms of robustness of the prediction and predictive accuracy.

Spectral correction of photovoltaic module electrical properties

Except for irradiance and temperature, the distribution of solar spectrum also affects the electrical performance of photovoltaic (PV) modules. To explore the effect, this study conducted long-term experimental measurements on the wide range of full solar radiation spectrum with monocrystalline silicon (m-Si) and cadmium telluride (CdTe), and established new spectral correction function (SCF) under horizontal conditions based on the average photon energy (APE). It is verified with a good agreement R2 of 0.95, and the maximum RMSE is only 0.017 %. Moreover, the application of the SCFs to building vertical façade has also been well verified, and the accuracy of electrical performance prediction can be improved by 14.51 % (m-Si) and 3.57 % (CdTe). In addition, this study combines the annual horizontal total solar radiation spectrum in Beijing and gives the annual spectral gain and loss (SGL) ratio of two PV panels. The power generation performance of the two PV modules under the actual spectrum will be underestimated for about 53.5 % and 99.7 % of the time in the whole year. This study broadens the dimension of evaluating the electrical performance parameters of PV panels and provides a basis and guidance for the accurate prediction and calculation of photovoltaic power generation.

Optimisation of photovoltaic storage and charging system operation based on photovoltaic power generation prediction analysis

Abstract The power generation of photovoltaic power generation systems has more influencing factors. Accurate PV power prediction data is needed in order to maintain the stable operation of the power system. In this paper, a photovoltaic power generation prediction scheme combining the integrated weather-similar day clustering method is designed with reference to the weather characteristics in the northeast region. The training samples are classified into three generalized weather small sample datasets. The radial basis (RBF) neural network prediction optimized with the Fruit Fly Algorithm (FOA) is chosen as the main body of the prediction system model. The prediction model is established. The power prediction is compared with the power prediction using the BP neural network model. The simulated prediction results are analyzed. The results indicate that the FOA-RBF neural network model exhibits superior performance in approximating nonlinear functions compared to the BP neural network. Ultimately, the forecasted data is utilized for operational enhancement, aiming for peak economic efficiency as the goal of optimization. This process considers limitations like energy equilibrium to enhance the financial viability of the optical storage charging facility, which improves the economic efficiency of the photovoltaic storage charging station, enhances the reliability of the system, and verifies the usability of the small-sample PV power prediction strategy based on FOA-RBF neural network designed in this paper for the northeastern region.

Research on photovoltaic power generation prediction based on EPSO-ELM model

Abstract To increase the precision of photovoltaic power prediction (PPG), we propose a PPG algorithm based on variational mode decomposition (VMD) and enhanced particle swarm optimization (EPSO) to optimize the extreme learning machine (ELM). Firstly, VMD is employed to decompose photovoltaic power data into multiple intrinsic modal components of different frequencies, to decrease the non-stationarity of the original sequence. Secondly, an improved EPSO algorithm is constructed using Latin hypercube sampling and quadratic interpolation strategy. Then, the EPSO is used to globally optimize the ELM parameters and establish EPSO-ELM combination models for different modal sequence components. Finally, the trained combination model is used to perform multidimensional prediction on the modal feature components of each decomposed subsequence, and the modal prediction sequences of each layer are superimposed and combined to form the final output result. The simulation results prove that the constructed VMD-EPSO-ELM model has stronger robustness and higher prediction accuracy compared to conventional short-term PPG prediction models.

Based on Deep Learning Model and Flink Streaming Computing Short Term Photovoltaic Power Generation Prediction for Suburban Distribution Network

With the advancement of global energy internet construction, accurate prediction of new energy generation power such as photovoltaic is an important foundation for ensuring the safety and economic working of new power systems. A short-term photovoltaic power generation prediction method for suburban distribution networks based on deep learning model fusion and Flink flow calculation is proposed to address the challenges of complex power grids, diversified disturbance factors, and isolated monitoring points. This method uses Bi directional Long Short Term Memory(BiLSTM) to extract cross sequential nonlinear characteristic of photovoltaic power generation time series data. Compared with standard LSTM, BiLSTM can consider both historical and future information simultaneously, thus extracting richer extracted features from power generation time series data. This method also integrates attention mechanism to capture the importance distribution of historical temporal features for power generation prediction, effectively solving the problem of long-term temporal dependence in standard LSTM models. The Flink streaming computing framework embeds a trained BiLSTM-Attention photovoltaic power generation prediction model, enabling real-time prediction and monitoring analysis of photovoltaic power generation at various monitoring points in the suburban distribution network. This article uses a dataset of a suburban photovoltaic power station for validation, and trains the model with historical power generation data, meteorological factors, weather types, seasons, and other information as inputs. The BiLSTM-Attention fusion model studys the temporal characteristics of power generation, and has high accuracy in predicting short-term photovoltaic power generation in different scenarios. The Flink streaming computing platform can not only process high throughput predicted power data, but also has low time delay.

Sky images based photovoltaic power forecasting: A novel approach with optimized VMD and Vision Mamba

As the global demand for sustainable energy sources continues to grow, accurate prediction of photovoltaic power generation is crucial for optimizing the utilization of solar resources and enhancing the efficiency of photovoltaic systems. To improve the accuracy of photovoltaic power forecasting, this paper proposes a novel hybrid predictive model that integrates Optimized Variational Mode Decomposition (VMD), Vision Mamba (Vim) for extracting features from sky images, and advanced mechanisms like Patch Embedding and Variate-wise Cross-Attention. Initially, the proposed model employs SAO-optimized VMD to decompose the photovoltaic power series into high, medium, and low-frequency components. Subsequently, these components are patched to serve as input for the subsequent layers. In the third step, exogenous variables, including meteorological and image data, are introduced and processed through Variate Embedding combined with cross-attention mechanisms to capture the intricate interactions between these variables. Finally, by integrating the outputs from all processing steps through normalization and feed-forward layers, the final predictive results are produced. Experimental evaluations across different seasons demonstrate significant enhancements in forecasting accuracy, with the model achieving Root Mean Square Error (RMSE) values of 0.3587 in spring, 0.4376 in summer, 0.3544 in autumn, and 0.3493 in winter. Similarly, Mean Absolute Error (MAE) and Mean Squared Error (MSE) across these seasons underscore the model's effectiveness. This model offers new technical means for photovoltaic power forecasting and provides valuable decision support for the optimization and management of photovoltaic power systems.

CloudSwinNet: A hybrid CNN-transformer framework for ground-based cloud images fine-grained segmentation

Solar irradiance is the main factor affecting the output of a photovoltaic (PV) power station. The dominant role of ground-based clouds on the variation of direct solar radiation in the whole sky. Ground-based cloud segmentation plays a crucial role in photovoltaic power generation prediction. Despite advancements, current cloud segmentation methods fail to meet the requirements of this application. While convolutional neural networks demonstrate impressive segmentation capabilities in ground-based cloud segmentation tasks, their inherent limitations hinder further performance enhancement. Hence, this paper introduces CloudSwinNet, a hybrid CNN-Transformer framework for ground-based cloud images fine-grained segmentation. CloudSwinNet leverages concepts from convolutional neural networks, including stepwise downsampling, local convolution, and skip connections. To further explore the fine-grained features in the ground-based cloud images, we incorporates the Fine-grained Feature Fusion Module (Fg-FFM) in encoder structure, while the jump connection structure integrates the GloRe spatial graph inference module. These additions enable comprehensive learning of multi-scale and long-range dependencies. We conducted extensive experiments on the fine-grained ground-based cloud dataset, demonstrating that CloudSwinNet outperforms six semantic segmentation networks in both qualitative and quantitative evaluations. The results of ablation experiments prove the effectiveness of the module introduced in this paper.

Prediction of long-term photovoltaic power generation in the context of climate change

Accurate long-term prediction of power generation in photovoltaic (PV) power stations is crucial for preparing generation plans and future planning. Quantitative prediction of future power generation from PV stations not only contributes to the stable operation of the local power system but also assists managers in formulating regional energy policies to promote renewable energy consumption. We utilized the NEX-GDDP-CMIP6 high-resolution climate dataset and employed the Vine Copula method for post-downscaling. This approach enabled high-resolution forecasts of key meteorological factors under different shared socioeconomic pathways (SSPs) scenarios (SSP245 and SSP585) for a PV power station in Yunnan, China. Additionally, we developed the KM-PSO-SVR power generation prediction model, which enables future accurate long-term PV power generation prediction. The results show that the Vine Copula multi-model ensemble downscaling model can effectively simulate the changes in key meteorological factors in the PV power station area. The KM-PSO-SVR model exhibited good simulation performance, with a mean absolute error of 0.843, root mean square error of 1.136, and correlation coefficient of 0.874 during the validation period. The results indicate that during the decade spanning from January 1, 2025, to December 31, 2034, radiation and wind speed will be decrease, while the temperature is expected to increase. In the SSP245 scenario, there is a 1.585 % increase in the average annual power generation during the future carbon peaking period (2025–2034). However, the SSP585 scenario, representing higher future emissions, shows a lower increase of 1.479 %.

Forecasting Solar Photovoltaic Power Production: A Comprehensive Review and Innovative Data-Driven Modeling Framework

The intermittent and stochastic nature of Renewable Energy Sources (RESs) necessitates accurate power production prediction for effective scheduling and grid management. This paper presents a comprehensive review conducted with reference to a pioneering, comprehensive, and data-driven framework proposed for solar Photovoltaic (PV) power generation prediction. The systematic and integrating framework comprises three main phases carried out by seven main comprehensive modules for addressing numerous practical difficulties of the prediction task: phase I handles the aspects related to data acquisition (module 1) and manipulation (module 2) in preparation for the development of the prediction scheme; phase II tackles the aspects associated with the development of the prediction model (module 3) and the assessment of its accuracy (module 4), including the quantification of the uncertainty (module 5); and phase III evolves towards enhancing the prediction accuracy by incorporating aspects of context change detection (module 6) and incremental learning when new data become available (module 7). This framework adeptly addresses all facets of solar PV power production prediction, bridging existing gaps and offering a comprehensive solution to inherent challenges. By seamlessly integrating these elements, our approach stands as a robust and versatile tool for enhancing the precision of solar PV power prediction in real-world applications.

Uncertainty analysis of photovoltaic power generation system and intelligent coupling prediction

Accurate prediction of photovoltaic power generation is essential to promoting the active consumption and low-carbon protection. The complex uncertainty of the photovoltaic system itself leads to the deviation in the photovoltaic power prediction. Therefore, we propose a new prediction model for coupled intelligence optimization. First, the photovoltaic power is decomposed into effective mode components using VMD optimized by GWO. Statistical techniques were used to analyze multidimensional uncertainty and extract features, then, optimize the performance of the coupled model. Second, the Zebra optimization (ZOA) establishes an appropriate balance between exploration and utilization to achieve the optimization of the model parameters. In addition, the CNN is used to extract complex features and enhance the correlation between input values and output values. Finally, the power was predicted using the BiLSTM. The results show that applying the statistical technique to the coupled prediction model not only reveals the uncertainty of photovoltaic systems but reduces the prediction error. Among them, the R2 increased by 0.42 %, the values of MAPE, MSE, RMSE, and MAE were reduced to different degrees. It can better optimize the allocation and reasonable consumption of renewable energy, which provides the decision basis for the adjustment of renewable energy structure.

Machine Learning-Based Prediction and Benefit Analysis of Photovoltaic Power Generation

Machine Learning-Based Prediction and Benefit Analysis of Photovoltaic Power Generation

Prediction of rainy-day photovoltaic power generation based on Generative Adversarial Networks and enhanced Sparrow Search Algorithm

Accurate and timely photovoltaic (PV) power forecasting is crucial for the stable operation of power systems. To address the issue of sparse PV power data on rainy days, this paper proposes the use of Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP) to augment rainy-day data. A progressive gradient penalty strategy is introduced to avoid gradient vanishing. A Bidirectional Long Short-Term Memory network is employed for power forecasting, with adaptive hyperparameter optimization achieved through an improved Sparrow Search Algorithm (LSSA). The optimization capability is enhanced using a chaotic mapping strategy. Validation on data from a 1 MW PV plant demonstrates that the LSSA-optimized model achieves the best forecasting results. After augmenting the rainy-day dataset, the Mean Absolute Error and Root Mean Square Error decreased by 15.3 % and 6.15 %, respectively, indicating that WGAN-GP significantly improves forecasting accuracy on rainy days.

Research on prediction method of photovoltaic power generation based on transformer model

Accurate prediction of photovoltaic power generation is of great significance to stable operation of power system. To improve the prediction accuracy of photovoltaic power, a photovoltaic power generation prediction machine learning model based on Transformer model is proposed in this paper. In this paper, the basic principle of Transformer model is introduced. Correlation analysis tools such as Pearson correlation coefficient and Spearman correlation coefficient are introduced to analyze the correlation between various factors and power generation in the photovoltaic power generation process. Then, the prediction results of traditional machine learning models and the Transformer model proposed in this paper were compared and analyzed for errors. The results show that: for long-term prediction tasks such as photovoltaic power generation prediction, Transformer model has higher prediction accuracy than traditional machine learning models. Moreover, compared with BP, LSTM and Bi-LSTM models, the Mean Square Error (MSE) of Transformer model decreases by 70.16%, 69.32% and 62.88% respectively in short-term prediction, and the Mean Square Error (MSE) of Transformer model decreases by 63.58%, 51.02% and 38.3% respectively in long-term prediction, which has good prediction effect. In addition, compared with the long-term prediction effect of Informer model, Transformer model has higher prediction accuracy.

Photovoltaic Power Generation Related Physical Quantity Mining Method Based On Historical Time Series Data Analysis

Abstract The impact of distributed photovoltaic grid-connected on distribution network security, power quality and system stability cannot be ignored. In order to better cope with the uncertainty and output of new energy output and understand the characteristics of distributed new energy power output, it is necessary to predict photovoltaic power. The historical time series data of photovoltaic power is large in dimension and quantity. If the data output is not carried out, a large amount of redundant data will affect the accuracy of photovoltaic prediction. Therefore, this paper proposes a feature selection method based on MIC most mutual information coefficient, which filters out the most relevant data of photovoltaic power generation from the original feature variables, and then performs the feature dimension reduction method of linear discriminant analysis (LDA) to map the high-dimensional data to a lower dimension space. Finally, using the prediction simulation example, using the long and short time neural network (LSTM) prediction comparison, the feature dimension reduction method of MIC feature selection and LDA effectively improves the accuracy of photovoltaic power generation prediction.

Short-Term Prediction of Rural Photovoltaic Power Generation Based on Improved Dung Beetle Optimization Algorithm

Addressing the challenges of randomness, volatility, and low prediction accuracy in rural low-carbon photovoltaic (PV) power generation, along with its unique characteristics, is crucial for the sustainable development of rural energy. This paper presents a forecasting model that combines variational mode decomposition (VMD) and an improved dung beetle optimization algorithm (IDBO) with the kernel extreme learning machine (KELM). Initially, a Gaussian mixture model (GMM) is used to categorize PV power data, separating analogous samples during different weather conditions. Afterwards, VMD is applied to stabilize the initial power sequence and extract numerous consistent subsequences. These subsequences are then employed to develop individual KELM prediction models, with their nuclear and regularization parameters optimized by IDBO. Finally, the predictions from the various subsequences are aggregated to produce the overall forecast. Empirical evidence via a case study indicates that the proposed VMD-IDBO-KELM model achieves commendable prediction accuracy across diverse weather conditions, surpassing existing models and affirming its efficacy and superiority. Compared with traditional VMD-DBO-KELM algorithms, the mean absolute percentage error of the VMD-IDBO-KELM model forecasting on sunny days, cloudy days and rainy days is reduced by 2.66%, 1.98% and 6.46%, respectively.