Short-term Photovoltaic Power Forecasting Research Articles

A novel learning approach for short-term photovoltaic power forecasting - A review and case studies

Integrating photovoltaic power into the power system can offer significant economic and environmental benefits. However, the intermittent and random nature of photovoltaic power generation poses a challenge to the current power system's planning and operation. Accurate photovoltaic power generation forecasting is crucial for delivering high-quality electric energy to consumers and increasing system reliability.In this study, a multi-stage approach is proposed. In the first stage, three decomposition techniques (Iterative Filtering decomposition, Variational Mode Decomposition, and Wavelet Packet Decomposition) are employed for time series decomposition. Then, for each Intrinsic Mode Function (IMF) component resulting from the decomposition block, five machine learning and three deep learning algorithms are utilized, serving as local forecasting models. In the final forecast phase, the best forecasting result for each regressor is selected during the reconstruction phase. Two years of photovoltaic power data recorded in three grid-connected photovoltaic systems installed in South Algeria were utilized for training and testing the proposed forecasting models. Upon comprehensive analysis and examination of the outcomes, the proposed method exhibits the lowest normalized Root Mean Square Error values across all forecast horizons and monitoring stations. Particularly, for forecasting steps at time intervals +1, +3, and +5, the proposed method attains an average normalized Root Mean Square Error, showcasing its efficacy: 0.709%, 2.097%, and 3.241% for station 1; 1.147%, 3.546%, and 5.347% for station 2; and 0.922%, 2.158%, and 4.539% for station 3. The experimental results underscore the superiority of our approach over conventional regression algorithms, thus substantiating its prowess in delivering robust and competitive performance outcomes.

Optimizing Short-Term Photovoltaic Power Forecasting: A Novel Approach with Gaussian Process Regression and Bayesian Hyperparameter Tuning

The inherent volatility of PV power introduces unpredictability to the power system, necessitating accurate forecasting of power generation. In this study, a machine learning (ML) model based on Gaussian process regression (GPR) for short-term PV power output forecasting is proposed. With its benefits in handling nonlinear relationships, estimating uncertainty, and generating probabilistic forecasts, GPR is an appropriate approach for addressing the problems caused by PV power generation’s irregularity. Additionally, Bayesian optimization to identify optimal hyper-parameter combinations for the ML model is utilized. The research leverages solar radiation intensity data collected at 60-min and 30-min intervals over periods of 1 year and 6 months, respectively. Comparative analysis reveals that the data set with 60-min intervals performs slightly better than the 30-min intervals data set. The proposed GPR model, coupled with Bayesian optimization, demonstrates superior performance compared to contemporary ML models and traditional neural network models. This superiority is evident in 98% and 90% improvements in root mean square errors compared to feed-forward neural network and artificial neural network models, respectively. This research contributes to advancing accurate and efficient forecasting methods for PV power output, thereby enhancing the reliability and stability of power systems.

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

Accurate short-term photovoltaic (PV) power forecasting can reduce the un- certainty of PV power generation, which is crucial for grid operation as well as energy dispatch. Considering the influence of seasonal and meteorological factors on short-term PV power prediction, a short-term PV power predic- tion method based on meteorological similarity day and sparrow search algo- rithm and bi-directional long and short-term memory network combination (SSA-BiLSTM) is proposed. Firstly, the correlation between meteorological factors and PV power generation is calculated by using Pearson coefficients, getting the strongly correlated meteorological factors affecting PV power generation; afterwards,the historical data of the strongly correlated meteorological factors are clustered by fuzzy C-means clustering to achieve meteorological similar day clustering; then, the best similar day is selected from the meteorological similar day according to the test day seasonal features and meteorological data, and Forming a training set with historical data, and training the original BiLSTM network. the SSA algorithm was used to find the optimal BiLSTM network parameters. Finally, Using the optimal parameters construct BiLSTM network to achieve short-term PV power prediction. The experiments were conducted with historical data from a PV power plant in Xinjiang, and also compared with existing prediction algorithms.The results show that the accuracy of PV power prediction in different weather conditions is 33.1 %, 31.9 % and 24.1 % higher than that under the same intelligent optimization algorithm and different neural networks, the accuracy of PV power prediction in different weather conditions is 27.9 %, 24.7 % and 18.0 % higher than that under the different intelligent algorithms and same neural network. Therefore, the algorithm in this paper has better accuracy in short-term PV power prediction under different seasons and different weather conditions.

Resource efficient PV power forecasting: Transductive transfer learning based hybrid deep learning model for smart grid in Industry 5.0

This paper presents an innovative approach for enhancing power output forecasting of Photovoltaic (PV) power plants in dynamic environmental conditions using a Hybrid Deep Learning Model (DLM). The hybrid DLM employs a synergy of Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) network, and Bidirectional LSTM (Bi-LSTM), effectively capturing spatial and temporal dependencies within weather data crucial for accurate predictions. To optimize the DLM’s performance efficiently, a unique Kepler Optimization Algorithm (KOA) is introduced for hyperparameter tuning, drawing inspiration from Kepler’s laws of planetary motion. By leveraging KOA, the DLM attains optimal hyperparameter configurations, elevating power output prediction precision. Additionally, this study integrates Transductive Transfer Learning (TTL) with the deep learning models to enhance resource efficiency. By leveraging knowledge gained from previously learned tasks, TTL enables the DLM to improve its forecasting capabilities while minimizing resource utilization. Datasets encompassing environmental parameters and PV plant-generated power across diverse sites are employed for DLM training and testing. Three hybrid models, amalgamating KOA, CNN, LSTM, and Bi-LSTM techniques, are introduced and evaluated. Comparative assessment of these models across distinct PV sites yields insightful observations. Performance evaluation, focused on short-term PV power forecasting, underscores the hybrid DLM’s superiority over individual CNN and LSTM models. This hybrid approach achieves remarkable accuracy and resilience in predicting power output under varying weather conditions, showcasing its potential for efficient PV power plant management.

Short-term photovoltaic power forecasting using meta-learning and numerical weather prediction independent Long Short-Term Memory models

Short-term photovoltaic (PV) power forecasting is essential for integrating renewable energy sources into the grid as it provides accurate and timely information on the expected output of PV systems. Deep learning (DL) networks have shown promising results in this area, but depending on the weather conditions and the particularities of each PV system, different DL architectures may perform best. This paper proposes a meta-learning method to improve one-hour-ahead deterministic forecasts of PV systems by dynamically blending the base forecasts of multiple DL models to learn under what conditions each model performs best. Four base models of different long short-term memory architectures are used to produce PV production forecasts without using numerical weather predictions, with the objective to enhance the generalizability of the proposed solution. The accuracy of the meta-learner is evaluated using three rooftop PV systems in Lisbon, Portugal. Results indicate that different base models perform best at different PV plants, and meta-learning can improve accuracy by up to 5% over the most accurate base model per plant and up to 4.5% over the equal-weighted combination of the base forecasts. These improvements are statistically significant and even larger during peak production hours.

Short-Term Photovoltaic Power Forecasting Based on a Novel Autoformer Model

Deep learning techniques excel at capturing and understanding the symmetry inherent in data patterns and non-linear properties of photovoltaic (PV) power, therefore they achieve excellent performance on short-term PV power forecasting. In order to produce more precise and detailed forecasting results, this research suggests a novel Autoformer model with De-Stationary Attention and Multi-Scale framework (ADAMS) for short-term PV power forecasting. In this approach, the multi-scale framework is applied to the Autoformer model to capture the inter-dependencies and specificities of each scale. Furthermore, the de-stationary attention is incorporated into an auto-correlation mechanism for more efficient non-stationary information extraction. Based on the operational data from a 1058.4 kW PV facility in Central Australia, the ADAMS model and the other six baseline models are compared with 5 min and 1 h temporal resolution PV power data predictions. The results show in terms of four performance measurements, the proposed method can handle the task of projecting short-term PV output more effectively than other methods. Taking the result of predicting the PV energy in the next 24 h based on the 1 h resolution data as an example, MSE is 0.280, MAE is 0.302, RMSE is 0.529, and adjusted R-squared is 0.824.

Optimal Graph Structure Based Short-Term Solar PV Power Forecasting Method Considering Surrounding Spatio-Temporal Correlations

Accurate short-term photovoltaic (PV) power forecasting is of great significance for the safe and stable operation of power system. Spatial information from neighboring PV sites contributes to improving forecasting performance. However, most of the current methods considering the spatial information of neighboring sites indiscriminately use all sites data for modeling, which will lead to information redundancy, resulting in low forecasting accuracy. Therefore, this paper proposes a short-term solar power forecasting method based on optimal graph structure considering surrounding spatio-temporal correlations. Firstly, the neighboring sites data is analyzed from the perspective of geographical and weather factors to select typical PV sites. Secondly, based on the complex network theory, a new index is proposed to evaluate the connectivity of the graph structure, which improves the predictive ability of the Graph Convolutional Network (GCN) model. Finally, considering numerical weather prediction (NWP) data, a hierarchical directed graph structure is constructed to indicate the unidirectional relationship between input samples, which is used as the input of GCN model to mine the spatio-temporal correlation around the targeted site. Through carrying out the case study, the proposed method shows excellent performance in improving accuracy of power forecasting compared with other benchmark methods.

Short-Term Forecasting and Uncertainty Analysis of Photovoltaic Power Based on the FCM-WOA-BILSTM Model

Aiming to solve the problem that photovoltaic power generation is always accompanied by uncertainty and the short-term prediction accuracy of photovoltaic power (PV) is not high, this paper proposes a method for short-term photovoltaic power forecasting (PPF) and uncertainty analysis using the fuzzy-c-means (FCM), whale optimization algorithm (WOA), bi-directional long short-term memory (BILSTM), and no-parametric kernel density estimation (NPKDE). First, the principal component analysis (PCA) is used to reduce the dimensionality of the daily feature vector, and then the FCM is used to divide the weather into four categories: sunny, cloudy, rainy, and extreme weather. Second, the WOA algorithm is used to train the hyperparameters of BILSTM, and finally, the optimized hyperparameters were used to construct a WOA-BILSTM prediction model to train the four types of weather samples after FCM clustering. The NPKDE method was used to calculate the probability density distribution of PV prediction errors and confidence intervals for PPF. The RMSEs of the FCM-WOA-BILSTM model are 2.46%, 4.89%, and 1.14% for sunny, cloudy, and rainy weather types, respectively. The simulation results of the calculation example show that compared with the BP, LSTM, GRU, PSO-BILSTM, and FCM-PSO-BP models, the proposed FCM-WOA-BILSTM model has higher prediction accuracy under various weather types, which verifies the effectiveness of the method. Moreover, the NPKDE method can accurately describe the probability density distribution of forecast errors.

A Review on forecasting the photovoltaic power Using Machine Learning

In this review paper on different forecasting method of the solar power output for effective generation of the power grid and proper management of transfer rate of energy per unit area occurred into the solar PV system. Essential part in focusing the prediction of solar power is irradiance and temperature. The irradiance can be forecasted by many algorithm and method is applied in prediction of generation of Short-term photovoltaic power and long term solar power forecasting. And many papers describes on numerical weather forecasting and some algorithm like neural networks or support vector regression for two step approach for predicting the PV power. In this review shown that methods like Bagging Model, deep learning, genetic algorithm, random forest, gradient boosting and artificial neural network. We found that for enhancing the performance of predicting PV power many authors proposed the ensemble method that is the hybrid models of different algorithm. And I found that on this review process ensemble methods show that good results and improve the forecasting solar PV power.

Deep Learning Neural Networks for Short-Term PV Power Forecasting via Sky Image Method

Solar photovoltaic (PV) power generation is prone to drastic changes due to cloud cover. The power is easily affected within a very short period of time. Thus, the accuracy of grasping cloud distribution is important for PV power forecasting. This study proposes a novel sky image method to obtain the cloud coverage rate used for short-term PV power forecasting. The authors developed an image analysis algorithm from the sky images obtained by an on-site whole sky imager (WSI). To verify the effectiveness of cloud coverage rate as the parameter for PV power forecast, four different combinations of weather features were used to compare the accuracy of short-term PV power forecasting. In addition to the artificial neural network (ANN) model, long short-term memory (LSTM) and the gated recurrent unit (GRU) were also introduced to compare their applicability conditions. After a comprehensive analysis, the coverage rate is the key weather feature, which can improve the accuracy by about 2% compared to the case without coverage feature. It also indicates that the LSTM and GRU models revealed better forecast results under different weather conditions, meaning that the cloud coverage rate proposed in this study has a significant benefit for short-term PV power forecasting.

Cloud Cover Forecast Based on Correlation Analysis on Satellite Images for Short-Term Photovoltaic Power Forecasting

Photovoltaic power generation must be predicted to counter the system instability caused by an increasing number of photovoltaic power-plant connections. In this study, a method for predicting the cloud volume and power generation using satellite images is proposed. Generally, solar irradiance and cloud cover have a high correlation. However, because the predicted solar irradiance is not provided by the Meteorological Administration or a weather site, cloud cover can be used instead of the predicted solar radiation. A lot of information, such as the direction and speed of movement of the cloud is contained in the satellite image. Therefore, the spatio-temporal correlation of the cloud is obtained from satellite images, and this correlation is presented pictorially. When the learning is complete, the current satellite image can be entered at the current time and the cloud value for the desired time can be obtained. In the case of the predictive model, the artificial neural network (ANN) model with the identical hyperparameters or setting values is used for data performance evaluation. Four cases of forecasting models are tested: cloud cover, visible image, infrared image, and a combination of the three variables. According to the result, the multivariable case showed the best performance for all test periods. Among single variable models, cloud cover presented a fair performance for short-term forecasting, and visible image presented a good performance for ultra-short-term forecasting.

Fuzzy Time Series Methods Applied to (In)Direct Short-Term Photovoltaic Power Forecasting

Solar photovoltaic energy has experienced significant growth in the last decade, as well as the challenges related to the intermittency of power generation inherent to this process. In this paper we propose to perform short-term forecasting of solar PV generation using fuzzy time series (FTS). Two FTS methods are proposed and evaluated to obtain a global horizontal irradiance (GHI) value. The first is the weighted method and the second is the fuzzy information granular method. Using the direct proportionality of the power with the GHI, the spatial smoothing process was applied, obtaining spatial irradiance on which a first-order low pass filter was applied to simulated power photovoltaic system generation. Thus, this study proposed indirect and direct forecasting of solar photovoltaic generation which was statistically evaluated and the results showed that the indirect prediction showed better performance with GHI than the power simulation. Error statistics, such as RMSE and MBE, show that the fuzzy information granular method performs better than the weighted method in GHI forecasting.

Development of a Low-Cost Data Acquisition System for Very Short-Term Photovoltaic Power Forecasting

The rising adoption of renewable energy sources means we must turn our eyes to limitations in traditional energy systems. Intermittency, if left unaddressed, may lead to several power-quality and energy-efficiency issues. The objective of this work is to develop a working tool to support photovoltaic energy forecast models for real-time operation applications. The current paradigm of intra-hour solar-power forecasting is to use image-based approaches to predict the state of cloud composition for short time horizons. Since the objective of intra-minute forecasting is to address high-frequency intermittency, data must provide information on and surrounding these events. For that purpose, acquisition by exception was chosen as the guiding principle. The system performs power measurements at 1 Hz frequency, and whenever it detects variations over a certain threshold, it saves the data 10 s before and 4 s after the detection point. A multilayer perceptron neural network was used to determine its relevance to the forecasting problem. With a thorough selection of attributes and network structures, the results show very low error with R2 greater than 0.93 for both input variables tested with a time horizon of 60 s. In conclusion, the data provided by the acquisition system yielded relevant information for forecasts up to 60 s ahead.

Stacking Ensemble Method with the RNN Meta-Learner for Short-Term PV Power Forecasting

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.

SolarNet: A hybrid reliable model based on convolutional neural network and variational mode decomposition for hourly photovoltaic power forecasting

Photovoltaic (PV) power generation has high uncertainties due to the randomness and imbalance nature of solar energy and meteorological parameters. Hence, accurate PV power forecasts are essential in the operation of PV power plants (PVPP) for short-term dispatches and power generation schedules. In this study, a novel convolutional neural network (CNN) model, namely SolarNet, is proposed for short-term PV output power forecasting under different weather conditions and seasons. The proposed CNN model is designed as a parallel pooling structure to increase the forecasting performance. This structure consists of max-pooling and average-pooling blocks. The input parameters are the measured historical solar radiation, temperature, humidity, and active power data. The power data is decomposed into sub-components with the variational mode decomposition method and a data preprocessing and reconstruction process is utilized to obtain deep input feature maps. After input parameters are converted to hue-saturation-value (HSV) color space, the subsets feed to the input of the network. The experimental studies are performed with a case study using a 23.40 kW PVPP dataset from the Desert Knowledge Australia Solar Centre. The design CNN model is also compared with benchmark deep learning methods. In the experiments, the average correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE) of the proposed method for 1-h different weather conditions are achieved as 0.9871, 0.3090, and 0.1750, respectively. The experimental results show that the proposed deep forecasting method has higher accuracy and stability in short-term PV power forecasting and outperforms the other deep learning methods.

Deep learning neural networks for short-term photovoltaic power forecasting

Accurate short-term forecasting of photovoltaic (PV) power is indispensable for controlling and designing smart energy management systems for microgrids. In this paper, different kinds of deep learning neural networks (DLNN) for short-term output PV power forecasting have been developed and compared: Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), Bidirectional GRU (BiGRU), One-Dimension Convolutional Neural Network (CNN1D), as well as other hybrid configurations such as CNN1D-LSTM and CNN1D-GRU. A database of the PV power produced by the microgrid installed at the University of Trieste (Italy) is used to train and comparatively test the neural networks. The performance has been evaluated over four different time horizons (1 min, 5 min, 30 min and 60 min), for one-Step and multi-step ahead. The results show that the investigated DLNNs provide very good accuracy, particularly in the case of 1 min time horizon with one-step ahead (correlation coefficient is close to 1), while for the case of multi-step ahead (up to 8 steps ahead) the results are found to be acceptable (correlation coefficient ranges between 96.9% and 98%).

Application of SRNN-GRU in Photovoltaic power Forecasting

Short-term photovoltaic power forecasting is of great significance for maintaining the security and stability of the power grid and coordinating the utilization of resources. As one of the Deep Learning Methods, Recurrent Neural Network (RNN) is widely used in time series prediction but lacks the ability of parallel computing. With good prediction effect, RNN is faced with the problem of long training time. In this paper, Sliced Recurrent Neural Network (SRNN) is applied to PV power prediction to guarantee the ability of parallel computing. The research result shows that compared to other commonly used models, SRNN can greatly speed up the training of Deep Learning Network with over 4 times higher training speed of the application of PV power prediction than that of ordinary RNN structure like LSTM and GRU. The accuracy of SRNN model is also improved by 0.1102 mae, which is significantly ahead of the others, as its parallel structure causes the more efficient parameter update, thus achieving ideal effect in PV prediction.

Spatial-Temporal Genetic-Based Attention Networks for Short-Term Photovoltaic Power Forecasting

Photovoltaic (PV) output power is significantly random and fluctuating due to its sensitivity to meteorological factors, making PV power forecasting a big challenge. Accurate short-term PV power forecasting plays a crucial role for the stable operation and maintenance management of PV systems. To achieve this target, the paper proposes a novel Spatial-Temporal Genetic-based Attention Networks (STGANet), which consists of a spatial-temporal module (STM) and a genetic-based attention module (GAM). STM serves to predict the missing solar irradiance to support the generation forecast, and contains a graph convolutional neural network to learn the spatial and temporal dependencies between historical meteorological data, while using dilated convolution as the non-linear part to simplify the network structure. The GAM efficiently explores for potential relationships in input features with attentional mechanism and uses genetic-based operation and LSTM which takes forecasting error as reference to find global optimal solutions and to avoid getting trapped in local optimal solutions. The model is verified through comparative experiment with several benchmark models using a real-world historical meteorological dataset and a power generation dataset of PV plants in southeastern China. The results have illustrated that the proposed model can provide better prediction performance in PV systems.

Artificial neural network models for global solar energy and photovoltaic power forecasting over India

ABSTRACT For design and development of solar-based photovoltaic (PV) system, accuracy in obtaining data of solar energy has been a prime concern. Forecasting of global solar energy in clear or sunny weather conditions are easily calculated; however, the challenge lies in estimating under the impact of foggy and cloudy conditions. Therefore, this research investigates and presents the Artificial Neural Network (ANN) models for global solar energy forecasting for distinct sky-conditions such as sunny, hazy, partly, and fully cloudy conditions. This research further applied for distinct climatic conditions namely composite, warm & humid, moderate, hot & dry, and cold & cloudy climate zone using meteorological parameters. Further, PV power forecasting has been essential for planning the operating capability of energy systems and its reliability. The present work explores the implementation of proposed model for short-term photovoltaic power forecast in solar energy systems. Lastly, comparative analysis of the developed model with other neural network models are carried out using statistical evaluation indices. It has been observed from the overall analysis that by using the Radial-Basis-Function-Neural-Network (RBFNN) model, a significant reduction in error has been noticed as compared to other methods. Further, it is noted that the sunny model provides improved performance for hot & dry climate zone; hazy model gives favorable output for composite climate zone; partly cloudy model is favorable for warm & humid climate zone; and lastly, fully cloudy model provides favorable results for cold & cloudy climate zone. The model has been implemented for broader application series and it has been observed that for short-term photovoltaic power forecasting under composite climatic zone, hazy model emerges out to provide better performance with Mean Absolute Percentage Error (MAPE) of 0.0019% followed by sunny, partly, and fully cloudy model with MAPE 0.024%, 0.054%, and 0.109% respectively. The models presented in this research will be useful for scholars to design and engineer the solar energy systems.

A novel non-iterative correction method for short-term photovoltaic power forecasting

Abstract Short-term photovoltaic (PV) power forecasting is of great significance for the real-time dispatching of power systems, but the accuracy of short-term forecasting of PV power is not satisfactory. Mastering the distribution characteristics of forecasting error and correcting the forecasting results are effective ways to improve the short-term forecasting accuracy. In this paper, the error distribution characteristics of short-term prediction results of PV power are studied, and then a non-iterative correction method for PV power short-term forecasting is proposed. The statistical result show that the error distribution is different in different seasons, the power forecasted error is strongly similar to the irradiance error distribution in numerical weather prediction (NWP). Therefore, this paper calculates the short-term forecasting results through seasonal models and uses non-iterative method to correct forecasting results, which can effective avoids the influence of accumulation errors. Compared with other methods, the root mean square error (RMSE) of this method is reduced by about 4.5%, and the mean absolute error (MAE) is reduced by about 2.6%, it shows the method can effectively improve the short-term forecasting accuracy of PV power.