Enhanced Ultra-Short-Term PV Forecasting Using Sky Imagers: Integrating MCR and Cloud Cover Estimation

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.

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

Text mining is an emerging topic that advances the review of academic literature. This paper presents a preliminary study on how to review solar irradiance and photovoltaic (PV) power forecasting (both topics combined as “solar forecasting” for short) using text mining, which serves as the first part of a forthcoming series of text mining applications in solar forecasting. This study contains three main contributions: (1) establishing the technological infrastructure (authors, journals & conferences, publications, and organizations) of solar forecasting via the top 1000 papers returned by a Google Scholar search; (2) consolidating the frequently-used abbreviations in solar forecasting by mining the full texts of 249 ScienceDirect publications; and (3) identifying key innovations in recent advances in solar forecasting (e.g., shadow camera, forecast reconciliation). As most of the steps involved in the above analysis are automated via an application programming interface, the presented method can be transferred to other solar engineering topics, or any other scientific domain, by means of changing the search word. The authors acknowledge that text mining, at its present stage, serves as a complement to, but not a replacement of, conventional review papers.

Ultra-short-term photovoltaic (PV) power forecasting can support the real-time dispatching of the power grid. However, PV power has great fluctuations due to various meteorological factors, which increase energy prices and cause difficulties in managing the grid. This article proposes an ultra-short-term PV power forecasting model based on the optimal frequency-domain decomposition and deep learning. First, the optimal frequency demarcation points for decomposition components are obtained through frequency-domain analysis. Then, the PV power is decomposed into the low-frequency and high-frequency components, which supports the rationality of decomposition results and solves the problem that the current decomposition model only uses the direct decomposition method and the decomposition components are not physical. Then, a convolutional neural network (CNN) is used to forecast the low-frequency and high-frequency components, and the final forecasting result is obtained by addition reconstruction. Based on the actual PV data in heavy rain days, the mean absolute percentage error (MAPE) of the proposed forecasting model is decreased by 52.97%, 64.07%, and 31.21%, compared with discrete wavelet transform, variational mode decomposition, and direct prediction models. In addition, compared with recurrent neural network and long-short-term memory model, the MAPE of the CNN forecasting model is decreased by 23.64% and 46.22%, and the training efficiency of the CNN forecasting model is improved by 85.63% and 87.68%. The results fully show that the proposed model in this article can improve both forecasting accuracy and time efficiency significantly.

With the increase of solar photovoltaic (PV) penetration in power system, the impact of random fluctuation of PV power on the secure operation of power grid becomes more and more serious. High-precision PV power forecasting can effectively promote the grid's accommodation of PV power generation. Cloud is the most important factor affecting the surface irradiance and PV power. For the ultra-short-term solar PV power forecast considering the influence of cloud movement, it is necessary to be able to obtain the surface irradiance according to the sky cloud observation data. Therefore, in order to accurately achieve the real-time mapping relationship between sky image and surface irradiance, a hybrid mapping model based on deep learning applied for solar PV power forecasting is proposed in this article. First, the sky image data are clustered based on the feature extraction of convolutional autoencoder and K -means clustering algorithm after preprocess stage. Second, a hybrid mapping model based on deep learning methods are established for surface irradiance. Finally, the simulation results are compared and evaluated with different deep learning methods (CNN, LSTM, and ANN). The results show that the proposed model in this article has higher accuracy and can maintain robustness under different weather conditions.

This study proposes a novel Debiased Active Learning (DAL) approach for solar photovoltaic (PV) power prediction based on ground-based sky images. Conventional uncertainty-based sampling methods often suffer from selection bias, particularly under class-imbalanced conditions where clear-sky samples dominate over cloudy-sky samples. To address this issue, the proposed DAL method estimates a debiasing matrix from a small, trusted validation dataset and uses it to correct the model’s predictive probabilities before uncertainty sampling. Using the SKIPP’D (Sky Images and Photovoltaic Power Dataset) dataset(≈300,000 paired sky images and PV power values), the proposed DAL significantly improves labeling efficiency and prediction accuracy compared to traditional uncertainty-based active learning approaches.

With the increasing global warming and enormous pollution, it is very obvious to generate power from renewable energy sources. In fact, photo voltaic (PV) power is a main renewable energy source for sustainable power generation. But PV power generation is uncertain and intermittent in nature. Stable and reliable power supply may not be feasible with PV power. Power supply reliability is important as much as its availability. To handle the inconsistency of PV power generation, power sector is highly dependent on forecasting methods and techniques. In this paper the feed forward neural network (FFNN) of artificial neural networks(ANN) with optimization technique particle swarm optimization (PSO) and support vector regression (SVR) are used in short term PV power generation forecasting and their performance is compared with the error calculation in terms of the mean absolute error (MAE) and root mean squared error (RMSE). The PV power and meteorological data of solar irradiation and temperature of Kolkata region of India was used for forecasting. Similar days of the year sunny days, cloudy days and rainy days are clustered using k-means clustering technique. Thereafter, a feed forward neural network is implemented to forecast a day head PV power and week ahead photo voltaic power. PSO is used to optimize the weights of the neural network. The performance of the proposed forecasting method is compared to a data mining approach, support vector regression (SVR).In this paper, the significance of proper selection of input parameters in PV power forecasting is emphasized. With the application of K-means clustering, the accuracy of ANN-PSO approach is improved significantly in a day ahead PV power forecasting and a week ahead PV power forecasting with a good margin. Even the accuracy of ANN-PSO approach after K-means clustering is better than SVR model in a week ahead forecasting

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.

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.

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

A cross-modal deep learning method for enhancing photovoltaic power forecasting with satellite imagery and time series data

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.

Photovoltaic power forecast based on satellite images considering effects of solar position

Motivated by factors such as the reduction in cost and the need for a shift towards achieving UN’s Sustainable Development Goals, PV (Photovoltaic) power generation is getting more attention in the cold regions of the Nordic countries and Canada. The cold climate and the albedo effect of snow in these regions present favorable operating conditions for PV cells and an opportunity to realize the seasonal matching of generation and consumption respectively. However, the erratic nature of PV brings a threat to the operation of the grid. PV power forecasting has been used as an economical solution to minimize and even overcome this limitation. This paper is therefore a comprehensive review of machine learning-based PV output power forecasting models in the literature in the context of Nordic climate. The impact of meteorological parameters and the soiling effect due to snow, which is unique to this climate, on the performance of a prediction model is discussed. PV power forecasting models in the literature are systematically classified into multiple groups and each group is analyzed and important suggestions are made for choosing a better model for these regions. Ensemble methods, optimization algorithms, time-series decomposition, and weather clustering are identified as important techniques that can be used to enhance performance. And notably, this work proposed two conceptual approaches that can be used to incorporate the effect of snow on PV power forecasting. Future research needs to focus on this area, which is crucial for the development of PV in these regions.

One major challenge with integrating photovoltaic (PV) systems into the grid is that its power generation is intermittent and uncontrollable due to the variation in solar radiation. An accurate PV power forecasting is crucial to the safe operation of the grid connected PV power station. In this work, a combined model with three different PV forecasting models is proposed based on a rough set method. The combination weights for each individual model are determined by rough set method according to its significance degree of condition attribute. The three different forecasting models include a past-power persistence model, a support vector machine (SVM) model and a similar data prediction model. The case study results show that, in comparison with each single forecasting model, the proposed combined model can identify the amount of useful information in a more effective manner.