Prediction Of Photovoltaic Power Generation Research Articles

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.

Real-time energy management strategy of the hydrogen-coupled microgrid based on Informer model prediction results and deep reinforcement learning

Abstract Microgrids (MG) powered by emerging distributed energy resources (DERs) are increasingly pivotal in driving the decarbonization of the power system. Due to the uncertainty of renewable energy sources, electricity generated from wind and solar is typically stored, either in batteries or in the form of high-pressure hydrogen gas within hydrogen storage tanks to meet the refuelling requirements of fuel cell vehicles (FCVs). For FCVs, the substantial operational expenses associated with hydrogen refuelling stations pose a significant barrier to their widespread adoption, with a notable portion of these costs attributed to hydrogen transportation. To address this challenge, this paper employs the Informer model based on a transformer for day-ahead prediction of photovoltaic power generation, as well as load demand forecasting. Subsequently, the Twin-delayed deep deterministic (TD3) policy gradient algorithm is used for real-time energy management of the microgrid. While ensuring that the State of Charge (SOC) remains above 45% daily to meet future hydrogen demands, the MG pursues an optimal strategy to minimize the total daily cost, including electricity procurement, carbon emissions and equipment degradation. Compared to outcomes derived from rule-based approaches, the algorithm introduced in this paper minimizes solar waste and equipment degradation, facilitating hydrogen production during low-cost electricity periods and enhancing economic feasibility.

A novel structure adaptive grey seasonal model with data reorganization and its application in solar photovoltaic power generation prediction

The increasing expansion of photovoltaic power generation leads to unpredictable fluctuations in electricity supply, which can potentially jeopardize the stability of the power grid and escalate the costs associated with grid imbalances. As a result, precise forecasts of photovoltaic power generation play a vital role in optimizing capacity deployment, enhancing consumption levels, improving planning strategies, and maintaining grid balance within systems characterized by significant penetration of solar energy. This paper proposes a structural adaptive grey seasonal model based on data reorganization. Solar photovoltaic power generation data typically exhibit seasonal fluctuations, which pose a challenge to existing prediction techniques. Therefore, this paper adopts the idea of data reorganization to eliminate the seasonal fluctuations of observations, and the adaptive accumulation operator can accurately simulate the change trend of the original data in different periods, overcoming the defect of insufficient adaptability of the traditional accumulation operator. Subsequently, the time trend items are incorporated into the model structure to identify the trend characteristics of system development, which can effectively explain the power generation trend of photovoltaic systems at different time periods and improve the prediction accuracy of the model. In addition, the compatibility and unbiased nature of the proposed model have been demonstrated to help us better perceive the model. The Grey Wolf Optimizer (GWO) is used to optimize the adaptive parameters of the model, endowing it with higher flexibility and stronger adaptability. In order to verify the effectiveness of the model, three practical cases (namely quarterly solar power generation in the United States, Japan, and Germany) were compared with existing econometric techniques, artificial neural networks, and grey prediction methods. The experimental results show that the new model outperforms other benchmark models in both simulation and prediction performance, and enjoys high robustness.

Physical model and long short-term memory-based combined prediction of photovoltaic power generation

Physical model and long short-term memory-based combined prediction of photovoltaic power generation

A hybrid graph attention network based method for interval prediction of shipboard solar irradiation

Solar energy ships came into being to reduce carbon emissions of global shipping and fossil fuels consumption. Accurate prediction of photovoltaic power generation can improve the economical operation of solar energy ships and reduce the power fluctuation of ship power systems. This study proposes a novel hybrid method to forecast ultra-short-term solar irradiation in a solar energy ship along the east coast of China. An improved graph attention framework is trained to describe the dynamic spatial-temporal topology among five weather stations and a solar energy ship. Initial interval prediction results are then determined by an improved Divided Period Optimization (DPO) method. According to distribution characteristics of the prediction error of shipboard solar irradiation, a Dynamic Prediction Interval Modification (DPIM) method is proposed to further optimize the prediction accuracy and interval width of ultra-short-term solar irradiation. Comparison experiments show that the dynamic connection matrix effectively improves the prediction accuracy, and the DPIM method reduces the PI width of 28.84%–37.79 % in shipboard solar irradiation. The proposed prediction method can accurately predict the shipboard solar irradiation at the period of significant disturbance, and provide effective technical support for the economic scheduling of solar energy ships.

Neural network fusion optimization for photovoltaic power forecasting

This paper aims to establish a comprehensive photovoltaic power generation prediction model. By collecting photovoltaic power generation data and weather data for a year, we analyzed the photovoltaic output characteristics in different seasons and found that the output characteristics in different seasons are also different. This article uses three neural network models, Long Short Term Memory Network, Recurrent Neural Network, and Dense Neural Network, to analyze the output characteristics of different seasons. Training, prediction, and prediction error analysis found that different models have different prediction accuracy in different seasons. Therefore, this paper proposes a weighted ensemble model add weights model based on the Nelder-Mead method to train and predict different seasons respectively. By analyzing the prediction error, the prediction accuracy needs to be better than a single model. We add noise to the data set to simulate unstable lighting conditions such as rainy days, and train and predict the data set after adding noise. The prediction results show that the comprehensive model has higher prediction accuracy than a single model in extreme weather. In order to verify the reliability of the model, this article uses a sliding window to extract the confidence interval of the prediction results, and uses the Bootstrap method to calculate the confidence interval. By analyzing and comparing each model’s Average Coverage, Root Mean Squared Length, and Mean Width, the prediction accuracy and reliability of add weights model are better than those of a single model.

GCN–Informer: A Novel Framework for Mid-Term Photovoltaic Power Forecasting

Predicting photovoltaic (PV) power generation is a crucial task in the field of clean energy. Achieving high-accuracy PV power prediction requires addressing two challenges in current deep learning methods: (1) In photovoltaic power generation prediction, traditional deep learning methods often generate predictions for long sequences one by one, significantly impacting the efficiency of model predictions. As the scale of photovoltaic power stations expands and the demand for predictions increases, this sequential prediction approach may lead to slow prediction speeds, making it difficult to meet real-time prediction requirements. (2) Feature extraction is a crucial step in photovoltaic power generation prediction. However, traditional feature extraction methods often focus solely on surface features, and fail to capture the inherent relationships between various influencing factors in photovoltaic power generation data, such as light intensity, temperature, and more. To overcome these limitations, this paper proposes a mid-term PV power prediction model that combines Graph Convolutional Network (GCN) and Informer models. This fusion model leverages the multi-output capability of the Informer model to ensure the timely generation of predictions for long sequences. Additionally, it harnesses the feature extraction ability of the GCN model from nodes, utilizing graph convolutional modules to extract feature information from the ‘query’ and ‘key’ components within the attention mechanism. This approach provides more reliable feature information for mid-term PV power prediction, thereby ensuring the accuracy of long sequence predictions. Results demonstrate that the GCN–Informer model significantly reduces prediction errors while improving the precision of power generation forecasting compared to the original Informer model. Overall, this research enhances the prediction accuracy of PV power generation and contributes to advancing the field of clean energy.

Ultra-Short-Term Photovoltaic Power Generation Prediction Based on Hunter–Prey Optimized K-Nearest Neighbors and Simple Recurrent Unit

In view of the current problems of complex models and insufficient data processing in ultra-short-term prediction of photovoltaic power generation, this paper proposes a photovoltaic power ultra-short-term prediction model named HPO-KNN-SRU, based on a Simple Recurrent Unit (SRU), K-Nearest Neighbors (KNN), and Hunter–Prey Optimization (HPO). Firstly, the sliding time window is determined by using the autocorrelation function (ACF), partial correlation function (PACF), and model training. The Pearson correlation coefficient method is used to filter the principal meteorological factors that affect photovoltaic power. Then, the K-Nearest Neighbors (KNN) algorithm is utilized for effective outlier detection and processing to ensure the quality of input data for the prediction model, and the Hunter–Prey Optimization (HPO) algorithm is applied to optimize the parameters of the KNN algorithm. Finally, the efficient Simple Recurrent Unit (SRU) model is used for training and prediction, with the Hunter–Prey Optimization (HPO) algorithm applied to optimize the parameters of the SRU model. Simulation experiments and extensive ablation studies using photovoltaic data from the Desert Knowledge Australia Solar Centre (DKASC) in Alice Springs, Australia, validate the effectiveness of the integrated model, the KNN outlier handling, and the HPO algorithm. Compared to the Support Vector Regression (SVR), Long Short-Term Memory (LSTM), Temporal Convolutional Network (TCN), and Simple Recurrent Unit (SRU) models, this model exhibits an average reduction of 19.63% in Mean Square Error (RMSE), 27.54% in Mean Absolute Error (MAE), and an average increase of 1.96% in coefficient of determination (R2) values.

Application of artificial intelligence technology in photovoltaic power generation prediction

As a new type of clean energy, photovoltaic power generation has been widely used in production and life. Compared with traditional energy, because the energy of photovoltaic power generation comes from the sun, photovoltaic power generation is mainly affected by the weather, which leads to the intermittency and volatility of this energy and makes the supply and demand relationship of this kind of energy more complex and changeable. With the continuous development of artificial intelligence technology, the field of photovoltaic power generation forecasting is also gradually realizing intelligence. At present, the photovoltaic power generation prediction model based on machine learning and deep learning has gradually become mainstream, which can effectively improve prediction accuracy and efficiency. In addition, the use of artificial intelligence technology can also achieve real-time monitoring and diagnosis of the operating status of photovoltaic power generation systems, further improving the stability and reliability of the system. In the future, with the continuous development and application of artificial intelligence technology, the field of photovoltaic power generation prediction will usher in broader development prospects.

Short-term prediction of photovoltaic power generation based on ICEEMDAN-SE-GAPSO-LSTM

This study proposes an improved adaptive noise-complete ensemble empirical mode decomposition (ICEEMDAN), sample entropy (SE), and genetic particle swarm optimization (GAPSO) algorithm-based short-term solar power forecast model. The photovoltaic power data is first decomposed using the ICEEMDAN algorithm to produce a number of eigenmode components with various properties. The sample entropy of each component is then calculated, and the components with similar sample entropy values are combined to create new modal components. These new modal components are then input into the GAPSO-optimized LSTM model for prediction, and the prediction results of each component are summed and reconstructed to produce the final prediction. The results completely validate the efficacy and dependability of the suggested method, using the measured data from a photovoltaic power station in Xinjiang, China as an example.

Photovoltaic power generation prediction and optimization configuration model based on GPR and improved PSO algorithm

As the growing demand for energy as well as the strengthening of environmental awareness, photovoltaic power generation, as a clean and renewable energy source, has gradually attracted people's attention and attention. To facilitate the dispatching and planning of power system, this study uses historical data and meteorological data to build a photovoltaic power generation prediction and configuration optimization model on the ground of Gaussian process regression and improved particle swarm optimization algorithm. The simulation results show that the regression prediction curve of the Gaussian process regression prediction model is the closest to the real curve, and the prediction curve is stable and not easily disturbed by noise data. The Root-mean-square deviation and the average absolute proportional error of the model are small, and the disparity in the predicted value and the true value of the model is small; The integration of multi factor data has improved the accuracy of prediction data, and the regression prediction effect is good. The improved Particle swarm optimization algorithm could continuously enhance in the search for the optimal solution, and the Rate of convergence is fast. The Pareto solution can provide different solutions suitable for photovoltaic power generation optimization. Reasonable optimization configuration can effectively reduce active power line loss and voltage deviation, with the maximum reduction values reaching 132kW and 0.028, respectively. The research and design of predictive models and optimized configuration models can promote the formation of smart grids.

Short-Term Photovoltaic Output Prediction Based on Decomposition and Reconstruction and XGBoost under Two Base Learners

Photovoltaic power generation prediction constitutes a significant research area within the realm of power system artificial intelligence. Accurate prediction of future photovoltaic output is imperative for the optimal dispatchment and secure operation of the power grid. This study introduces a photovoltaic prediction model, termed ICEEMDAN-Bagging-XGBoost, aimed at enhancing the accuracy of photovoltaic power generation predictions. In this paper, the original photovoltaic power data initially undergo decomposition utilizing the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) algorithm, with each intrinsic mode function (IMF) derived from this decomposition subsequently reconstructed into high-frequency, medium-frequency, and low-frequency components. Targeting the high-frequency and medium-frequency components of photovoltaic power, a limiting gradient boosting tree (XGBoost) is employed as the foundational learner in the Bagging parallel ensemble learning method, with the incorporation of a sparrow search algorithm (SSA) to refine the hyperparameters of XGBoost, thereby facilitating more nuanced tracking of the changes in the photovoltaic power’s high-frequency and medium-frequency components. Regarding the low-frequency components, XGBoost-Linear is utilized to enable rapid and precise prediction. In contrast with the conventional superposition reconstruction approach, this study employs XGBoost for the reconstruction of the prediction output’s high-frequency, intermediate-frequency, and low-frequency components. Ultimately, the efficacy of the proposed methodology is substantiated by the empirical operation data from a photovoltaic power station in Hebei Province, China. Relative to integrated and traditional single models, this paper’s model exhibits a markedly enhanced prediction accuracy, thereby offering greater applicational value in scenarios involving short-term photovoltaic power prediction.

Prediction of Photovoltaic power generation and analyzing of carbon emission reduction capacity in China

Prediction of Photovoltaic power generation and analyzing of carbon emission reduction capacity in China

Annual Forecast of Photovoltaic Power Generation Based on MLP Artificial Neural Networks

The intermittency of solar energy resources presents a significant challenge in balancing power generation and load demand. To enhance system consistency, forecasting photovoltaic solar energy is crucial. Among numerous techniques, Artificial Neural Network (ANN) is an efficient tool that can help simplify this problem and predict photovoltaic power generation based on various inputs such as weather data and panel characteristics. In this paper, we present the results of an annual forecast of photovoltaic power generation based on Multilayer Perceptrons (MLP), which provides valuable insights into the potential of MLP ANN for accurate and reliable prediction of photovoltaic power generation, thereby improving the efficiency and reliability of photovoltaic systems. The results were obtained based on data collected over a year and validated with data from the following year. Mean Squared Error (MSE) was utilized to quantify the error between the predicted and measured photovoltaic solar energy generation. The analysis demonstrated that this annual forecast of photovoltaic power generation is highly accurate.

Short-Term Photovoltaic Power Generation Prediction Model Based on Improved Data Decomposition and Time Convolution Network

In response to the volatility of photovoltaic power generation, this paper proposes a short-term photovoltaic power generation prediction model (HWOA-MVMD-TPA-TCN) based on a Hybrid Whale Optimization Algorithm (HWOA), multivariate variational mode decomposition (MVMD), temporal pattern attention mechanism (TPA), and temporal convolutional network (TCN). In order to improve the accuracy of photovoltaic power generation forecasting, HWOA-MVMD is used for data decomposition, the Minimum Mode Overlap Component (MMOC) is used as the objective function, the photovoltaic power generation sequence is decomposed into finite Intrinsic Mode Functions (IMFs) according to the optimal solution, and the training set is formed with key meteorological variable data such as total radiation (unit: W/m2), ambient temperature, and humidity. Then, the TPA-TCN model is used to train the sub-sequences, the final predicted values are obtained after superimposing the reconstruction of the prediction results, and finally the prediction error of the photovoltaic power generation data is studied. The proposed method is applied to real photovoltaic power generation data from a commercial center in Tianjin and is compared with HWOA-MVMD-BiLSTM, GWO-MVMD-TPA-TCN, and TPA-TCN prediction models. The simulation results demonstrate that the MAE value of the forecast method proposed in this paper is 1.95 MW and the RMSE value is 2.55 MW, which can be reduced by up to 33.74% and 38.85%, respectively. The HWOA-MVMD-TPA-TCN-based short-term photovoltaic power generation prediction model presented in this paper achieves higher prediction accuracy and superior performance, serving as a valuable reference for related research.

An implementation of inertia control strategy for grid-connected solar system using moth-flame optimization algorithm

As the solar power system power system grows rapidly, inertia control strategy (ICS) becomes crucial to enable stable grid integration. However, the existing ICS lacks of dynamic weather analysis with maximum power point tracking (MPPT) and fault-ride through (FRT) capabilities such as low voltage ride-through (LVRT) and high voltage ride-through (HVRT). In this work, an inertia weighting strategy and the Cauchy mutation operator are introduced to improve the moth-flame optimization (MFO) algorithm to support vector machine prediction of photovoltaic power generation. In this paper, the proposed adaptive VICS with variable moment of inertia (J) and damping factor ( D P ) demonstrates its effectiveness with faster frequency recovery, less overshooting and continuous stable operation under grid fault and dynamic weather. The MFO algorithm is used to implement inertia control strategies for grid-connected solar systems. Accurate simulation results confirm the inertia control of the emulsion and the control of the solar system. The results of the simulation show a significant improvement in frequency with the designed MFO and compared to Horse Herd Optimization (HHO). The proposed method contributes to improve photovoltaic energy prediction, reduces the impact of photovoltaic power penetration into the grid and maintains the system reliability.

A two‐stage scheduling model for urban distribution network resilience enhancement in ice storms

AbstractThis paper proposes a two‐stage stochastic scheduling model for urban distribution network resilience enhancement against ice storms, which coordinates mobile deicing equipment routing and distributed energy resources dispatching. An improved line ice thickness prediction model and a photovoltaic power generation prediction method in accordance with conditional generative adversarial networks are proposed to provide data boundaries for scheduling strategy. Facing the uncertainty of line failure, a two‐stage scenario‐based distribution network optimization model is established. At first stage, the mobile deicing equipment routing strategy is decided to mitigate the impact caused by ice storms. The Monte‐Carlo simulation method is introduced to describe the uncertainty of line failure due to ice acceleration. For the second stage, based on the results of photovoltaic forecasting and possible distribution line failure scenario generated by Monte‐Carlo simulation method, the optimal distributed energy resources dispatching strategy can be obtained through the mixed integer programming. The proposed model is simplified to a mixed integer linear programming model that can be solved by a commercial solver. The test results on the modified IEEE 33‐node system and modified 69‐node system demonstrate that the proposed method can effectively improve the resilience performance of urban distribution network under ice storms.

Short-term photovoltaic power prediction based on fractional Levy stable motion

Accurate prediction of photovoltaic (PV) power generation is the key to daily dispatch management and safe and stable grid operation. In order to improve the accuracy of the prediction, a finite iterative PV power prediction model with long range dependence (LRD) characteristics was developed using fractional Lévy stable motion (fLsm) and applied to a real dataset collected in the DKASC photovoltaic system in Alice Springs, Australia. The LRD prediction model considers the influence of current and past trends in the stochastic series on the future trends. Firstly, the calculation of the maximum steps prediction was introduced based on the maximum Lyapunov. The maximum prediction steps could provide the prediction steps for subsequent prediction models. Secondly, the order stochastic differential equation (FSDE) which describes the fLsm can be obtained. The parameters of the FSDE were estimated by using a novel characteristic function method. The PV power forecasting model with the LRD characteristics was obtained by discretization of FSDE. By comparing statistical performance indicators such as root max error, mean square error with Conv-LSTM, BiLSTM, and GA-LSTM models, the performance of the proposed fLsm model has been demonstrated. The proposed methods can provide better theoretical support for the stable and safe operation of PV grid connection. They have high reference value for grid dispatching department.

Research on Short-term Power Prediction of Distributed Photovoltaic Power Generation

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.

Comparative Study on the Precision of Photovoltaic Power Generation Prediction Based on Integrated Models

Comparative Study on the Precision of Photovoltaic Power Generation Prediction Based on Integrated Models