Journal articles: 'SOLAR POWER FORECASTING'

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El hendouzi, Abdelhakim, and Abdennaser Bourouhou. "Solar Photovoltaic Power Forecasting." Journal of Electrical and Computer Engineering 2020 (December 31, 2020): 1–21. http://dx.doi.org/10.1155/2020/8819925.

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The management of clean energy is usually the key for environmental, economic, and sustainable developments. In the meantime, the energy management system (EMS) ensures the clean energy which includes many sources grouped in a small power plant such as microgrid (MG). In this case, the forecasting methods are used for helping the EMS and allow the high efficiency to the clean energy. The aim of this review paper is providing the necessary data about the basic principles and standards of photovoltaic (PV) power forecasting by stating numerous research studies carried out on the PV power forecasting topic specifically in the short-term time horizon which is advantageous for the EMS and grid operator. At the same time, this contribution can offer a state of the art in different methods and approaches used for PV power forecasting along with a careful study of different time and spatial horizons. Furthermore, this current review paper can support the tenders in the PV power forecasting.

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K., D., and Isha I. "Solar Power Forecasting: A Review." International Journal of Computer Applications 145, no. 6 (July 15, 2016): 28–50. http://dx.doi.org/10.5120/ijca2016910728.

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Iheanetu, Kelachukwu J. "Solar Photovoltaic Power Forecasting: A Review." Sustainability 14, no. 24 (December 19, 2022): 17005. http://dx.doi.org/10.3390/su142417005.

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The recent global warming effect has brought into focus different solutions for combating climate change. The generation of climate-friendly renewable energy alternatives has been vastly improved and commercialized for power generation. As a result of this industrial revolution, solar photovoltaic (PV) systems have drawn much attention as a power generation source for varying applications, including the main utility-grid power supply. There has been tremendous growth in both on- and off-grid solar PV installations in the last few years. This trend is expected to continue over the next few years as government legislation and awareness campaigns increase to encourage a shift toward using renewable energy alternatives. Despite the numerous advantages of solar PV power generation, the highly variable nature of the sun’s irradiance in different seasons of various geopolitical areas/regions can significantly affect the expected energy yield. This variation directly impacts the profitability or economic viability of the system, and cannot be neglected. To overcome this challenge, various procedures have been applied to forecast the generated solar PV energy. This study provides a comprehensive and systematic review of recent advances in solar PV power forecasting techniques with a focus on data-driven procedures. It critically analyzes recent studies on solar PV power forecasting to highlight the strengths and weaknesses of the techniques or models implemented. The clarity provided will form a basis for higher accuracy in future models and applications.

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Kim, Kihan, and Jin Hur. "Weighting Factor Selection of the Ensemble Model for Improving Forecast Accuracy of Photovoltaic Generating Resources." Energies 12, no. 17 (August 28, 2019): 3315. http://dx.doi.org/10.3390/en12173315.

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Among renewable energy sources, solar power is rapidly growing as a major power source for future power systems. However, solar power has uncertainty due to the effects of weather factors, and if the penetration rate of solar power in the future increases, it could reduce the reliability of the power system. A study of accurate solar power forecasting should be done to improve the stability of the power system operation. Using the empirical data from solar power plants in South Korea, the short-term forecasting of solar power outputs were carried out for 2016. We performed solar power forecasting with the support vector regression (SVR) model, the naïve Bayes classifier (NBC), and the hourly regression model. We proposed the ensemble method including the selection of weighting factors for each model to improve forecasting accuracy. The forecasted solar power generation error was indicated using normalized mean absolute error (NMAE) compared to the plant capacity. For the ensemble method, the results of each forecasting model were weighted with the reciprocal of the standard deviation of the forecast error, thus improving the solar power outputs forecast accuracy.

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Divya, R., and S. Umamaheswari. "Solar Power Forecasting Methods – A Review." International Journal of Advanced Science and Engineering 9, no. 1 (August 1, 2022): 2591–98. http://dx.doi.org/10.29294/ijase.9.1.2022.2591-2598.

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Okhorzina, Alena, Alexey Yurchenko, and Artem Kozloff. "Autonomous Solar-Wind Power Forecasting Systems." Advanced Materials Research 1097 (April 2015): 59–62. http://dx.doi.org/10.4028/www.scientific.net/amr.1097.59.

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The paper reports on the results of climatic testing of silicon photovoltaic modules and photovoltaic power systems conducted in Russia (Siberia and the Far East). The monitoring system to control the power system work was developed. Testing over 17 years and a large amount of experimental studies enabled us to develop a precise mathematical model of the photovoltaic module in natural environment taking into account climatic and hardware factors.

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Bacher, Peder, Henrik Madsen, and Henrik Aalborg Nielsen. "Online short-term solar power forecasting." Solar Energy 83, no. 10 (October 2009): 1772–83. http://dx.doi.org/10.1016/j.solener.2009.05.016.

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Kumar, R. Dhilip, Prakash K, P. Abirama Sundari, and Sathya S. "A Hybrid Machine Learning Model for Solar Power Forecasting." E3S Web of Conferences 387 (2023): 04003. http://dx.doi.org/10.1051/e3sconf/202338704003.

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The paper presents a near investigation of different AI procedures for solar power forecasting. The objective of the research is to identify the most accurate and efficient machine learning algorithms for solar power forecasting. The paper also considers different parameters such as weather conditions, solar radiation, and time of day in the forecasting model. This paper proposes a hybrid machine learning model for solar power forecasting that consolidates the strengths of multiple algorithms, including support vector regression, random forest regression, and artificial neural network. However, the study also highlights the importance of incorporating domain knowledge and feature engineering in machine learning models for better forecasting accuracy.

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Nath, N. C., W. Sae-Tang, and C. Pirak. "Machine Learning-Based Solar Power Energy Forecasting." Journal of the Society of Automotive Engineers Malaysia 4, no. 3 (September 1, 2020): 307–22. http://dx.doi.org/10.56381/jsaem.v4i3.25.

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The expanding interest in energy is one of the main motivations behind the integration of solar energy into electric grids or networks. The exact expectation of solar oriented irradiance variety can improve the nature of administration. This coordination of solar-based vitality and exact expectations can help in better arranging and distributing of energy. Discovering vitality sources to fulfil the world’s developing interest is one of the general public’s major difficulties for the coming fifty years. In this research, two machine learning techniques utilized for hourly solar power forecasting are presented. The solar power prediction model becomes robust and efficient for solar power energy forecasting once the redundant information is removed from raw data, experimental data is transformed into a settled range, the best features selection method is chosen, four different weather profiles are made based on different weather conditions and the right time series machine learning algorithm is chosen.

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Arias, Mariz B., and Sungwoo Bae. "Design Models for Power Flow Management of a Grid-Connected Solar Photovoltaic System with Energy Storage System." Energies 13, no. 9 (April 29, 2020): 2137. http://dx.doi.org/10.3390/en13092137.

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This paper provides models for managing and investigating the power flow of a grid-connected solar photovoltaic (PV) system with an energy storage system (ESS) supplying the residential load. This paper presents a combination of models in forecasting solar PV power, forecasting load power, and determining battery capacity of the ESS, to improve the overall quality of the power flow management of a grid-connected solar PV system. Big data tools were used to formulate the solar PV power forecasting model and load power forecasting model, in which real historical solar electricity data of actual solar homes in Australia were used to improve the quality of the forecasting models. In addition, the time-of-use electricity pricing was also considered in managing the power flow, to provide the minimum cost of electricity from the grid to the residential load. The output of this model presents the power flow profiles, including the solar PV power, battery power, grid power, and load power of weekend and weekday in a summer season. The battery state-of-charge of the ESS was also presented. Therefore, this model may help power system engineers to investigate the power flow of each system of a grid-connected solar PV system and help in the management decision for the improvement of the overall quality of the power management of the system.

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Erlapally, Deekshitha, K. Anuradha, G. Karuna, V. Srilakshmi, and K. Adilakshmi. "Survey Analysis of Solar Power Generation Forecasting." E3S Web of Conferences 309 (2021): 01039. http://dx.doi.org/10.1051/e3sconf/202130901039.

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Solar power is the conversion of sunlight into electricity using solar photovoltaic cells as a source of energy. There are various applications for solar power; here is information on PV cell generation. We seek to understand the behavior of solar power plants through the data generated by the photovoltaic modules and the power generation in different weather conditions in India. The goal of this survey is to give a thorough assessment and study of machine learning, deep learning and artificial intelligence. Artificial intelligence (AI) models as well as information preprocessing techniques, parameter selection algorithms and predictive performance evaluations are used in machine learning and deep learning models for predicting renewable energies. But in case of time series data we can predict only the errors using a linear regression model, we can also calculate things like root mean square error (RMSE), mean absolute error (MSE), mean bias error (MBE) and mean absolute percentage error (MAPE). By the analysis of weather condition also we can predict the consumption of current by solar for every 15 minutes, 1day, and 1week or even for 1 month and find the accuracy.

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Kochneva, Elena. "Solar power generation short-term forecasting model’s implementation experience." MATEC Web of Conferences 208 (2018): 04005. http://dx.doi.org/10.1051/matecconf/201820804005.

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Recently there is significant increase in the installed capacity of solar power plants in Russia. Thereby there are issues of solar power plants owners information support for participation in wholesale electricity market. The paper describes the experience of short term forecasting system practical implementation. The system is proposed for forecasting the solar power plant generation “a day ahead” as a part of the software for automatic meter reading systems “Energosfera”. The short-term forecasting program modules structure, key parameters and characteristics used during the forecasting process description is presented.

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Eroshenko, Stanislav, Elena Kochneva, Pavel Kruchkov, and Aleksandra Khalyasmaa. "Solar Power Plant Generation Short-Term Forecasting Model." MATEC Web of Conferences 208 (2018): 04004. http://dx.doi.org/10.1051/matecconf/201820804004.

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Recently, renewable generation plays an increasingly important role in the energy balance. Solar energy is developing at a rapid pace, while the solar power plants output depends on weather conditions. Solar power plant output short-term forecasting is an urgent issue. The future electricity generation qualitative forecasts allow electricity producers and network operators to actively manage the variable capacity of solar power plants, and thereby to optimally integrate the solar resources into the country's overall power system. The article presents one of the possible approaches to the solution of the short-term forecasting problem of a solar power plant output.

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Wu, Yuan-Kang, Cheng-Liang Huang, Quoc-Thang Phan, and Yuan-Yao Li. "Completed Review of Various Solar Power Forecasting Techniques Considering Different Viewpoints." Energies 15, no. 9 (May 2, 2022): 3320. http://dx.doi.org/10.3390/en15093320.

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Solar power has rapidly become an increasingly important energy source in many countries over recent years; however, the intermittent nature of photovoltaic (PV) power generation has a significant impact on existing power systems. To reduce this uncertainty and maintain system security, precise solar power forecasting methods are required. This study summarizes and compares various PV power forecasting approaches, including time-series statistical methods, physical methods, ensemble methods, and machine and deep learning methods, the last of which there is a particular focus. In addition, various optimization algorithms for model parameters are summarized, the crucial factors that influence PV power forecasts are investigated, and input selection for PV power generation forecasting models are discussed. Probabilistic forecasting is expected to play a key role in the PV power forecasting required to meet the challenges faced by modern grid systems, and so this study provides a comparative analysis of existing deterministic and probabilistic forecasting models. Additionally, the importance of data processing techniques that enhance forecasting performance are highlighted. In comparison with the extant literature, this paper addresses more of the issues concerning the application of deep and machine learning to PV power forecasting. Based on the survey results, a complete and comprehensive solar power forecasting process must include data processing and feature extraction capabilities, a powerful deep learning structure for training, and a method to evaluate the uncertainty in its predictions.

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Polo, Jesús, Nuria Martín-Chivelet, Miguel Alonso-Abella, Carlos Sanz-Saiz, José Cuenca, and Marina de la Cruz. "Exploring the PV Power Forecasting at Building Façades Using Gradient Boosting Methods." Energies 16, no. 3 (February 2, 2023): 1495. http://dx.doi.org/10.3390/en16031495.

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Solar power forecasting is of high interest in managing any power system based on solar energy. In the case of photovoltaic (PV) systems, and building integrated PV (BIPV) in particular, it may help to better operate the power grid and to manage the power load and storage. Power forecasting directly based on PV time series has some advantages over solar irradiance forecasting first and PV power modeling afterwards. In this paper, the power forecasting for BIPV systems in a vertical façade is studied using machine learning algorithms based on decision trees. The forecasting scheme employs the skforecast library from the Python environment, which facilitates the implementation of different schemes for both deterministic and probabilistic forecasting applications. Firstly, deterministic forecasting of hourly BIPV power was performed with XGBoost and Random Forest algorithms for different cases, showing an improvement in forecasting accuracy when some exogenous variables were used. Secondly, probabilistic forecasting was performed with XGBoost combined with the Bootstrap method. The results of this paper show the capabilities of Random Forest and gradient boosting algorithms, such as XGBoost, to work as regressors in time series forecasting of BIPV power. Mean absolute error in the deterministic forecast, using the most influencing exogenous variables, were around 40% and close below 30% for the south and east array, respectively.

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Park, Taeseop, Keunju Song, Jaeik Jeong, and Hongseok Kim. "Convolutional Autoencoder-Based Anomaly Detection for Photovoltaic Power Forecasting of Virtual Power Plants." Energies 16, no. 14 (July 11, 2023): 5293. http://dx.doi.org/10.3390/en16145293.

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Machine learning-based time-series forecasting has recently been intensively studied. Deep learning (DL), specifically deep neural networks (DNN) and long short-term memory (LSTM), are the popular approaches for this purpose. However, these methods have several problems. First, DNN needs a lot of data to avoid over-fitting. Without sufficient data, the model cannot be generalized so it may not be good for unseen data. Second, impaired data affect forecasting accuracy. In general, one trains a model assuming that normal data enters the input. However, when anomalous data enters the input, the forecasting accuracy of the model may decrease substantially, which emphasizes the importance of data integrity. This paper focuses on these two problems. In time-series forecasting, especially for photovoltaic (PV) forecasting, data from solar power plants are not sufficient. As solar panels are newly installed, a sufficiently long period of data cannot be obtained. We also find that many solar power plants may contain a substantial amount of anomalous data, e.g., 30%. In this regard, we propose a data preprocessing technique leveraging convolutional autoencoder and principal component analysis (PCA) to use insufficient data with a high rate of anomaly. We compare the performance of the PV forecasting model after applying the proposed anomaly detection in constructing a virtual power plant (VPP). Extensive experiments with 2517 PV sites in the Republic of Korea, which are used for VPP construction, confirm that the proposed technique can filter out anomaly PV sites with very high accuracy, e.g., 99%, which in turn contributes to reducing the forecasting error by 23%.

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万, 贝. "Review of Solar Photovoltaic Power Generation Forecasting." Journal of Sensor Technology and Application 09, no. 01 (2021): 1–6. http://dx.doi.org/10.12677/jsta.2021.91001.

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Elsaraiti, Meftah, and Adel Merabet. "Solar Power Forecasting Using Deep Learning Techniques." IEEE Access 10 (2022): 31692–98. http://dx.doi.org/10.1109/access.2022.3160484.

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Mittal, Amit Kumar, Dr Kirti Mathur, and Shivangi Mittal. "A Review on forecasting the photovoltaic power Using Machine Learning." Journal of Physics: Conference Series 2286, no. 1 (July 1, 2022): 012010. http://dx.doi.org/10.1088/1742-6596/2286/1/012010.

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Abstract 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.

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Li, Wang, Zhang, Xin, and Liu. "Recurrent Neural Networks Based Photovoltaic Power Forecasting Approach." Energies 12, no. 13 (July 1, 2019): 2538. http://dx.doi.org/10.3390/en12132538.

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The intermittency of solar energy resources has brought a big challenge for the optimization and planning of a future smart grid. To reduce the intermittency, an accurate prediction of photovoltaic (PV) power generation is very important. Therefore, this paper proposes a new forecasting method based on the recurrent neural network (RNN). At first, the entire solar power time series data is divided into inter-day data and intra-day data. Then, we apply RNN to discover the nonlinear features and invariant structures exhibited in the adjacent days and intra-day data. After that, a new point prediction model is proposed, only by taking the previous PV power data as input without weather information. The forecasting horizons are set from 15 to 90 minutes. The proposed forecasting method is tested by using real solar power in Flanders, Belgium. The classical persistence method (Persistence), back propagation neural network (BPNN), radial basis function (RBF) neural network and support vector machine (SVM), and long short-term memory (LSTM) networks are adopted as benchmarks. Extensive results show that the proposed forecasting method exhibits a good forecasting quality on very short-term forecasting, which demonstrates the feasibility and effectiveness of the proposed forecasting model.

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Assaf, Abbas Mohammed, Habibollah Haron, Haza Nuzly Abdull Hamed, Fuad A. Ghaleb, Sultan Noman Qasem, and Abdullah M. Albarrak. "A Review on Neural Network Based Models for Short Term Solar Irradiance Forecasting." Applied Sciences 13, no. 14 (July 19, 2023): 8332. http://dx.doi.org/10.3390/app13148332.

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The accuracy of solar energy forecasting is critical for power system planning, management, and operation in the global electric energy grid. Therefore, it is crucial to ensure a constant and sustainable power supply to consumers. However, existing statistical and machine learning algorithms are not reliable for forecasting due to the sporadic nature of solar energy data. Several factors influence the performance of solar irradiance, such as forecasting horizon, weather classification, and performance evaluation metrics. Therefore, we provide a review paper on deep learning-based solar irradiance forecasting models. These models include Long Short-Term Memory (LTSM), Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), Generative Adversarial Networks (GAN), Attention Mechanism (AM), and other existing hybrid models. Based on our analysis, deep learning models perform better than conventional models in solar forecasting applications, especially in combination with some techniques that enhance the extraction of features. Furthermore, the use of data augmentation techniques to improve deep learning performance is useful, especially for deep networks. Thus, this paper is expected to provide a baseline analysis for future researchers to select the most appropriate approaches for photovoltaic power forecasting, wind power forecasting, and electricity consumption forecasting in the medium term and long term.

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Wang, Fei, Yili Yu, Zhanyao Zhang, Jie Li, Zhao Zhen, and Kangping Li. "Wavelet Decomposition and Convolutional LSTM Networks Based Improved Deep Learning Model for Solar Irradiance Forecasting." Applied Sciences 8, no. 8 (August 1, 2018): 1286. http://dx.doi.org/10.3390/app8081286.

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Solar photovoltaic (PV) power forecasting has become an important issue with regard to the power grid in terms of the effective integration of large-scale PV plants. As the main influence factor of PV power generation, solar irradiance and its accurate forecasting are the prerequisite for solar PV power forecasting. However, previous forecasting approaches using manual feature extraction (MFE), traditional modeling and single deep learning (DL) models could not satisfy the performance requirements in partial scenarios with complex fluctuations. Therefore, an improved DL model based on wavelet decomposition (WD), the Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) is proposed for day-ahead solar irradiance forecasting. Given the high dependency of solar irradiance on weather status, the proposed model is individually established under four general weather type (i.e., sunny, cloudy, rainy and heavy rainy). For certain weather types, the raw solar irradiance sequence is decomposed into several subsequences via discrete wavelet transformation. Then each subsequence is fed into the CNN based local feature extractor to automatically learn the abstract feature representation from the raw subsequence data. Since the extracted features of each subsequence are also time series data, they are individually transported to LSTM to construct the subsequence forecasting model. In the end, the final solar irradiance forecasting results under certain weather types are obtained via the wavelet reconstruction of these forecasted subsequences. This case study further verifies the enhanced forecasting accuracy of our proposed method via a comparison with traditional and single DL models.

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Wang, Ching-Hsin, Kuo-Ping Lin, Yu-Ming Lu, and Chih-Feng Wu. "Deep Belief Network with Seasonal Decomposition for Solar Power Output Forecasting." International Journal of Reliability, Quality and Safety Engineering 26, no. 06 (December 2019): 1950029. http://dx.doi.org/10.1142/s0218539319500293.

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Solar power is a type of renewable energy system that uses solar energy to produce electricity, and is regarded as one of the most important power sources in Taiwan. Since sunshine duration affects the amount of energy that can be generated by a solar power, the seasons of the year are important factors that should be considered for accurate solar power prediction. In the last decade, the use of artificial intelligence for forecasting systems have been quite popular, and the deep belief network (DBN) models started getting more attention. In this study, a seasonal deep belief network (SDBN) was developed to forecast monthly solar power output data. The SDBN was constructed by combining seasonal decomposition method and DBN. Further, this study used monthly solar power output data from the Taiwan Power Company. The results indicated that the proposed forecasting system demonstrated a superior performance in terms of forecasting accuracy. Also, the performance of autoregressive integrated moving average (ARIMA), generalized regression neural network (GRNN), and DBN obtained from a separate study were compared to the performance of the proposed SDBN model and showed that the latter was better than the other three models. Thus, the SDBN model can be used as an alternative method for monthly solar power output data forecasting.

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Wang, Hui, Jianbo Sun, and Weijun Wang. "Photovoltaic Power Forecasting Based on EEMD and a Variable-Weight Combination Forecasting Model." Sustainability 10, no. 8 (July 26, 2018): 2627. http://dx.doi.org/10.3390/su10082627.

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It is widely considered that solar energy will be one of the most competitive energy sources in the future, and solar energy currently accounts for high percentages of power generation in developed countries. However, its power generation capacity is significantly affected by several factors; therefore, accurate prediction of solar power generation is necessary. This paper proposes a photovoltaic (PV) power generation forecasting method based on ensemble empirical mode decomposition (EEMD) and variable-weight combination forecasting. First, EEMD is applied to decompose PV power data into components that are then combined into three groups: low-frequency, intermediate-frequency, and high-frequency. These three groups of sequences are individually predicted by the variable-weight combination forecasting model and added to obtain the final forecasting result. In addition, the design of the weights for combination forecasting was studied during the forecasting process. The comparison in the case study indicates that in PV power generation forecasting, the prediction results obtained by the individual forecasting and summing of the sequences after the EEMD are better than those from direct prediction. In addition, when the single prediction model is converted to a variable-weight combination forecasting model, the prediction accuracy is further improved by using the optimal weights.

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Wang, Yu, Hualei Zou, Xin Chen, Fanghua Zhang, and Jie Chen. "Adaptive Solar Power Forecasting based on Machine Learning Methods." Applied Sciences 8, no. 11 (November 12, 2018): 2224. http://dx.doi.org/10.3390/app8112224.

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Due to the existence of predicting errors in the power systems, such as solar power, wind power and load demand, the economic performance of power systems can be weakened accordingly. In this paper, we propose an adaptive solar power forecasting (ASPF) method for precise solar power forecasting, which captures the characteristics of forecasting errors and revises the predictions accordingly by combining data clustering, variable selection, and neural network. The proposed ASPF is thus quite general, and does not require any specific original forecasting method. We first propose the framework of ASPF, featuring the data identification and data updating. We then present the applied improved k-means clustering, the least angular regression algorithm, and BPNN, followed by the realization of ASPF, which is shown to improve as more data collected. Simulation results show the effectiveness of the proposed ASPF based on the trace-driven data.

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Haupt, Sue Ellen, Branko Kosović, Tara Jensen, Jeffrey K. Lazo, Jared A. Lee, Pedro A. Jiménez, James Cowie, et al. "Building the Sun4Cast System: Improvements in Solar Power Forecasting." Bulletin of the American Meteorological Society 99, no. 1 (January 1, 2018): 121–36. http://dx.doi.org/10.1175/bams-d-16-0221.1.

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Abstract As integration of solar power into the national electric grid rapidly increases, it becomes imperative to improve forecasting of this highly variable renewable resource. Thus, a team of researchers from the public, private, and academic sectors partnered to develop and assess a new solar power forecasting system, Sun4Cast. The partnership focused on improving decision-making for utilities and independent system operators, ultimately resulting in improved grid stability and cost savings for consumers. The project followed a value chain approach to determine key research and technology needs to reach desired results. Sun4Cast integrates various forecasting technologies across a spectrum of temporal and spatial scales to predict surface solar irradiance. Anchoring the system is WRF-Solar, a version of the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model optimized for solar irradiance prediction. Forecasts from multiple NWP models are blended via the Dynamic Integrated Forecast (DICast) System, which forms the basis of the system beyond about 6 h. For short-range (0–6 h) forecasts, Sun4Cast leverages several observation-based nowcasting technologies. These technologies are blended via the Nowcasting Expert System Integrator (NESI). The NESI and DICast systems are subsequently blended to produce short- to midterm irradiance forecasts for solar array locations. The irradiance forecasts are translated into power with uncertainties quantified using an analog ensemble approach and are provided to the industry partners for real-time decision-making. The Sun4Cast system ran operationally throughout 2015 and results were assessed. This paper analyzes the collaborative design process, discusses the project results, and provides recommendations for best-practice solar forecasting.

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Chang, Wen Yeau. "Comparison of Three Short Term Photovoltaic System Power Generation Forecasting Methods." Applied Mechanics and Materials 479-480 (December 2013): 585–89. http://dx.doi.org/10.4028/www.scientific.net/amm.479-480.585.

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An accurate forecasting method for solar power generation of the photovoltaic (PV) system is urgent needed under the relevant issues associated with the high penetration of solar power in the electricity system. This paper presents a comparison of three forecasting approaches on short term solar power generation of PV system. Three forecasting methods, namely, persistence method, back propagation neural network method, and radial basis function (RBF) neural network method, are investigated. To demonstrate the performance of three methods, the methods are tested on the practical information of solar power generation of a PV system. The performance is evaluated based on two indexes, namely, maximum absolute percent error and mean absolute percent error.

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Moreno, Guillermo, Carlos Santos, Pedro Martín, Francisco Javier Rodríguez, Rafael Peña, and Branislav Vuksanovic. "Intra-Day Solar Power Forecasting Strategy for Managing Virtual Power Plants." Sensors 21, no. 16 (August 22, 2021): 5648. http://dx.doi.org/10.3390/s21165648.

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Solar energy penetration has been on the rise worldwide during the past decade, attracting a growing interest in solar power forecasting over short time horizons. The increasing integration of these resources without accurate power forecasts hinders the grid operation and discourages the use of this renewable resource. To overcome this problem, Virtual Power Plants (VPPs) provide a solution to centralize the management of several installations to minimize the forecasting error. This paper introduces a method to efficiently produce intra-day accurate Photovoltaic (PV) power forecasts at different locations, by using free and available information. Prediction intervals, which are based on the Mean Absolute Error (MAE), account for the forecast uncertainty which provides additional information about the VPP node power generation. The performance of the forecasting strategy has been verified against the power generated by a real PV installation, and a set of ground-based meteorological stations in geographical proximity have been used to emulate a VPP. The forecasting approach is based on a Long Short-Term Memory (LSTM) network and shows similar errors to those obtained with other deep learning methods published in the literature, offering a MAE performance of 44.19 W/m2 under different lead times and launch times. By applying this technique to 8 VPP nodes, the global error is reduced by 12.37% in terms of the MAE, showing huge potential in this environment.

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Anuradha, K., Deekshitha Erlapally, G. Karuna, V. Srilakshmi, and K. Adilakshmi. "Analysis Of Solar Power Generation Forecasting Using Machine Learning Techniques." E3S Web of Conferences 309 (2021): 01163. http://dx.doi.org/10.1051/e3sconf/202130901163.

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Solar power is generated using photovoltaic (PV) systems all over the world. Because the output power of PV systems is alternating and highly dependent on environmental circumstances, solar power sources are unpredictable in nature. Irradiance, humidity, PV surface temperature, and wind speed are only a few of these variables. Because of the unpredictability in photovoltaic generating, it’s crucial to plan ahead for solar power generation as in solar power forecasting is required for electric grid. Solar power generation is weather-dependent and unpredictable, this forecast is complex and difficult. The impacts of various environmental conditions on the output of a PV system are discussed. Machine Learning (ML) algorithms have shown great results in time series forecasting and so can be used to anticipate power with weather conditions as model inputs. The use of multiple machine learning, Deep learning and artificial neural network techniques to perform solar power forecasting. Here in this regression models from machine learning techniques like support vector machine regressor, random forest regressor and linear regression model from which random forest regressor beaten the other two regression models with vast accuracy.

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Abdullah, Nor Azliana, Nasrudin Abd Rahim, Chin Kim Gan, and Noriah Nor Adzman. "Forecasting Solar Power Using Hybrid Firefly and Particle Swarm Optimization (HFPSO) for Optimizing the Parameters in a Wavelet Transform-Adaptive Neuro Fuzzy Inference System (WT-ANFIS)." Applied Sciences 9, no. 16 (August 7, 2019): 3214. http://dx.doi.org/10.3390/app9163214.

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Solar power generation deals with uncertainty and intermittency issues that lead to some difficulties in controlling the whole grid system due to imbalanced power production and power demand. The forecasting of solar power is an effort in securing the integration of renewable energy into the grid. This work proposes a forecasting model called WT-ANFIS-HFPSO which combines the wavelet transform (WT), adaptive neuro-fuzzy inference system (ANFIS) and hybrid firefly and particle swarm optimization algorithm (HFPSO). In the proposed work, the WT model is used to eliminate the noise in the meteorological data and solar power data whereby the ANFIS is functioning as the forecasting model of the hourly solar power data. The HFPSO is the hybridization of the firefly (FF) and particle swarm optimization (PSO) algorithm, which is employed in optimizing the premise parameters of the ANFIS to increase the accuracy of the model. The results obtained from WT-ANFIS-HFPSO are then compared with several other forecasting strategies. From the comparative analysis, the WT-ANFIS-HFPSO showed superior performance in terms of statistical error analysis, confirming its reliability as an excellent forecaster of hourly solar power data.

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Zhen, Zhao, Zheng Wang, Fei Wang, Zengqiang Mi, and Kangping Li. "Research on a cloud image forecasting approach for solar power forecasting." Energy Procedia 142 (December 2017): 362–68. http://dx.doi.org/10.1016/j.egypro.2017.12.057.

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Chaouachi, Aymen, Rashad M. Kamel, and Ken Nagasaka. "Neural Network Ensemble-Based Solar Power Generation Short-Term Forecasting." Journal of Advanced Computational Intelligence and Intelligent Informatics 14, no. 1 (January 20, 2010): 69–75. http://dx.doi.org/10.20965/jaciii.2010.p0069.

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This paper presents the applicability of artificial neural networks for 24 hour ahead solar power generation forecasting of a 20 kW photovoltaic system, the developed forecasting is suitable for a reliable Microgrid energy management. In total four neural networks were proposed, namely: multi-layred perceptron, radial basis function, recurrent and a neural network ensemble consisting in ensemble of bagged networks. Forecasting reliability of the proposed neural networks was carried out in terms forecasting error performance basing on statistical and graphical methods. The experimental results showed that all the proposed networks achieved an acceptable forecasting accuracy. In term of comparison the neural network ensemble gives the highest precision forecasting comparing to the conventional networks. In fact, each network of the ensemble over-fits to some extent and leads to a diversity which enhances the noise tolerance and the forecasting generalization performance comparing to the conventional networks.

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Veda Swaroop, M., and P. Linga Reddy. "Solar and Wind Power Forecasting with Optimal ARIMA Parameters." International Journal of Engineering & Technology 7, no. 1.8 (February 9, 2018): 201. http://dx.doi.org/10.14419/ijet.v7i1.8.16402.

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The solar and wind renewable energy sources are gaining popularity to encourage green energy into the power system. The cost of generation of solar and wind energy sources are decreasing and competing with conventional coal-based generation. Therefore, it is very important to integrate these renewable sources into the power system. Integrating Solar and wind energy sources require to solve the uncertainty problem. Both the solar and wind energy generation is uncertain and not controllable. In this paper, sliding window optimal ARIMA forecasting algorithm is proposed to solve the uncertainty associated with solar and wind sources. The proposed forecasting method is used on the data collected from National Renewable Energy Laboratory website.

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D., KARDASH, LYUBIMENKO, E.N., KONDRATENKO, V., TYUTYUNNYK, N., and PRYDATKO I. "Study of the solar power plant power generation forecasting model." Journal of Electrical and power engineering 24, no. 1 (May 21, 2021): 73–76. http://dx.doi.org/10.31474/2074-2630-2021-1-73-76.

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The question of determining the possible capacity of a photovoltaic power plant is very acute due to the growing demand for renewable energy, coupled with the fact that during the day we have limited time to generate energy from such a source. Thus, based on the obtained analytical data, which allows to predict weather conditions, it is possible to regulate the amount of energy supplied to the network in a certain way due to more maneuverable power plants. In previous years, electrical engineering scientists and researchers from different countries have developed and implemented methods for determining weather conditions, such as clouds, air temperature, atmospheric dust and others, as well as their impact on the energy output of a solar power plant. A photovoltaic panel is a complex nonlinear object with many variables. In addition to the structural features of the module, the output is most affected by solar radiation and panel temperature. When researching the prediction of the amount of energy produced, it is important to find sufficiently reliable and consistent data. At the forefront of these issues are US universities and research centers. For example, the University of Nevada in Las Vegas, in 2006 put into operation a set of measurements of weather conditions: the level of sunlight, ambient temperature, wind speed, humidity and others. When calculating the power generated by the panels, it is assumed that the system operates at the point of maximum power. The scheme works as follows: we set the values of temperature (Temperature) and irradiation (Irradiance); we apply voltage to the output terminals of the array by changing its value from 0 to Voc. We take current readings at each point, we find the power for each point, we find the maximum among the obtained array of points. Repeat over the entire range of input values. Thus, we obtain a graph of the output power of Figs. 4 pre-considering the losses in the inverter.

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Nam, Seungbeom, and Jin Hur. "Probabilistic Forecasting Model of Solar Power Outputs Based on the Naïve Bayes Classifier and Kriging Models." Energies 11, no. 11 (November 1, 2018): 2982. http://dx.doi.org/10.3390/en11112982.

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Solar power’s variability makes managing power system planning and operation difficult. Facilitating a high level of integration of solar power resources into a grid requires maintaining the fundamental power system so that it is stable when interconnected. Accurate and reliable forecasting helps to maintain the system safely given large-scale solar power resources; this paper therefore proposes a probabilistic forecasting approach to solar resources using the R statistics program, applying a hybrid model that considers spatio-temporal peculiarities. Information on how the weather varies at sites of interest is often unavailable, so we use a spatial modeling procedure called kriging to estimate precise data at the solar power plants. The kriging method implements interpolation with geographical property data. In this paper, we perform day-ahead forecasts of solar power based on the probability in one-hour intervals by using a Naïve Bayes Classifier model, which is a classification algorithm. We augment forecasting by taking into account the overall data distribution and applying the Gaussian probability distribution. To validate the proposed hybrid forecasting model, we perform a comparison of the proposed model with a persistence model using the normalized mean absolute error (NMAE). Furthermore, we use empirical data from South Korea’s meteorological towers (MET) to interpolate weather variables at points of interest.

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Lim, Su-Chang, Jun-Ho Huh, Seok-Hoon Hong, Chul-Young Park, and Jong-Chan Kim. "Solar Power Forecasting Using CNN-LSTM Hybrid Model." Energies 15, no. 21 (November 4, 2022): 8233. http://dx.doi.org/10.3390/en15218233.

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Photovoltaic (PV) technology converts solar energy into electrical energy, and the PV industry is an essential renewable energy industry. However, the amount of power generated through PV systems is closely related to unpredictable and uncontrollable environmental factors such as solar radiation, temperature, humidity, cloud cover, and wind speed. Particularly, changes in temperature and solar radiation can substantially affect power generation, causing a sudden surplus or reduction in the power output. Nevertheless, accurately predicting the energy produced by PV power generation systems is crucial. This paper proposes a hybrid model comprising a convolutional neural network (CNN) and long short-term memory (LSTM) for stable power generation forecasting. The CNN classifies weather conditions, while the LSTM learns power generation patterns based on the weather conditions. The proposed model was trained and tested using the PV power output data from a power plant in Busan, Korea. Quantitative and qualitative evaluations were performed to verify the performance of the model. The proposed model achieved a mean absolute percentage error of 4.58 on a sunny day and 7.06 on a cloudy day in the quantitative evaluation. The experimental results suggest that precise power generation forecasting is possible using the proposed model according to instantaneous changes in power generation patterns. Moreover, the proposed model can help optimize PV power plant operations.

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Carrera, Berny, and Kwanho Kim. "Comparison Analysis of Machine Learning Techniques for Photovoltaic Prediction Using Weather Sensor Data." Sensors 20, no. 11 (June 1, 2020): 3129. http://dx.doi.org/10.3390/s20113129.

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Over the past few years, solar power has significantly increased in popularity as a renewable energy. In the context of electricity generation, solar power offers clean and accessible energy, as it is not associated with global warming and pollution. The main challenge of solar power is its uncontrollable fluctuation since it is highly depending on other weather variables. Thus, forecasting energy generation is important for smart grid operators and solar electricity providers since they are required to ensure the power continuity in order to dispatch and properly prepare to store the energy. In this study, we propose an efficient comparison framework for forecasting the solar power that will be generated 36 h in advance from Yeongam solar power plant located in South Jeolla Province, South Korea. The results show a comparative analysis of the state-of-the-art techniques for solar power generation.

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Sedai, Ashish, Rabin Dhakal, Shishir Gautam, Anibesh Dhamala, Argenis Bilbao, Qin Wang, Adam Wigington, and Suhas Pol. "Performance Analysis of Statistical, Machine Learning and Deep Learning Models in Long-Term Forecasting of Solar Power Production." Forecasting 5, no. 1 (February 22, 2023): 256–84. http://dx.doi.org/10.3390/forecast5010014.

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The Machine Learning/Deep Learning (ML/DL) forecasting model has helped stakeholders overcome uncertainties associated with renewable energy resources and time planning for probable near-term power fluctuations. Nevertheless, the effectiveness of long-term forecasting of renewable energy resources using an existing ML/DL model is still debatable and needs additional research. Considering the constraints inherent in current empirical or physical-based forecasting models, the study utilizes ML/DL models to provide long-term predictions for solar power production. This study aims to examine the efficacy of several existing forecasting models. The study suggests approaches to enhance the accuracy of long-term forecasting of solar power generation for a case study power plant. It summarizes and compares the statistical model (ARIMA), ML model (SVR), DL models (LSTM, GRU, etc.), and ensemble models (RF, hybrid) with respect to long-term prediction. The performances of the univariate and multivariate models are summarized and compared based on their ability to accurately predict solar power generation for the next 1, 3, 5, and 15 days for a 100-kW solar power plant in Lubbock, TX, USA. Conclusions are drawn predicting the accuracy of various model changes with variation in the prediction time frame and input variables. In summary, the Random Forest model predicted long-term solar power generation with 50% better accuracy over the univariate statistical model and 10% better accuracy over multivariate ML/DL models.

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Mohamad Radzi, Putri Nor Liyana, Muhammad Naveed Akhter, Saad Mekhilef, and Noraisyah Mohamed Shah. "Review on the Application of Photovoltaic Forecasting Using Machine Learning for Very Short- to Long-Term Forecasting." Sustainability 15, no. 4 (February 6, 2023): 2942. http://dx.doi.org/10.3390/su15042942.

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Advancements in renewable energy technology have significantly reduced the consumer dependence on conventional energy sources for power generation. Solar energy has proven to be a sustainable source of power generation compared to other renewable energy sources. The performance of a photovoltaic (PV) system is highly dependent on the amount of solar penetration to the solar cell, the type of climatic season, the temperature of the surroundings, and the environmental humidity. Unfortunately, every renewable’s technology has its limitation. Consequently, this prevents the system from operating to a maximum or optimally. Achieving a precise PV system output power is crucial to overcoming solar power output instability and intermittency performance. This paper discusses an intensive review of machine learning, followed by the types of neural network models under supervised machine learning implemented in photovoltaic power forecasting. The literature of past researchers is collected, mainly focusing on the duration of forecasts for very short-, short-, and long-term forecasts in a photovoltaic system. The performance of forecasting is also evaluated according to a different type of input parameter and time-step resolution. Lastly, the crucial aspects of a conventional and hybrid model of machine learning and neural networks are reviewed comprehensively.

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Wang, Fei, Zhao Zhen, Chun Liu, Zengqiang Mi, Miadreza Shafie-khah, and João Catalão. "Time-Section Fusion Pattern Classification Based Day-Ahead Solar Irradiance Ensemble Forecasting Model Using Mutual Iterative Optimization." Energies 11, no. 1 (January 12, 2018): 184. http://dx.doi.org/10.3390/en11010184.

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Accurate solar PV power forecasting can provide expected future PV output power so as to help the system operator to dispatch traditional power plants to maintain the balance between supply and demand sides. However, under non-stationary weather conditions, such as cloudy or partly cloudy days, the variability of solar irradiance makes the accurate PV power forecasting a very hard task. Ensemble forecasting based on multiple models established by different theory has been proved as an effective means on improving forecasting accuracy. Classification modeling according to different patterns could reduce the complexity and difficulty of intro-class data fitting so as to improve the forecasting accuracy as well. When combining the two above points and focusing on the different fusion pattern specifically in terms of hourly time dimension, a time-section fusion pattern classification based day-ahead solar irradiance ensemble forecasting model using mutual iterative optimization is proposed, which contains multiple forecasting models based on wavelet decomposition (WD), fusion pattern classification model, and fusion models corresponding to each fusion pattern. First, the solar irradiance is forecasted using WD based models at different WD level. Second, the fusion pattern classification recognition model is trained and then applied to recognize the different fusion pattern at each hourly time section. At last, the final forecasting result is obtained using the optimal fusion model corresponding to the data fusion pattern. In addition, a mutual iterative optimization framework for the pattern classification and data fusion models is also proposed to improve the model’s performance. Simulations show that the mutual iterative optimization framework can effectively enhance the performance and coordination of pattern classification and data fusion models. The accuracy of the proposed solar irradiance day-ahead ensemble forecasting model is verified when compared with a standard Artificial Neural Network (ANN) forecasting model, five WD based models and a single ensemble forecasting model without time-section fusion classification.

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Park, Jinwoong, Jihoon Moon, Seungmin Jung, and Eenjun Hwang. "Multistep-Ahead Solar Radiation Forecasting Scheme Based on the Light Gradient Boosting Machine: A Case Study of Jeju Island." Remote Sensing 12, no. 14 (July 15, 2020): 2271. http://dx.doi.org/10.3390/rs12142271.

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Smart islands have focused on renewable energy sources, such as solar and wind, to achieve energy self-sufficiency. Because solar photovoltaic (PV) power has the advantage of less noise and easier installation than wind power, it is more flexible in selecting a location for installation. A PV power system can be operated more efficiently by predicting the amount of global solar radiation for solar power generation. Thus far, most studies have addressed day-ahead probabilistic forecasting to predict global solar radiation. However, day-ahead probabilistic forecasting has limitations in responding quickly to sudden changes in the external environment. Although multistep-ahead (MSA) forecasting can be used for this purpose, traditional machine learning models are unsuitable because of the substantial training time. In this paper, we propose an accurate MSA global solar radiation forecasting model based on the light gradient boosting machine (LightGBM), which can handle the training-time problem and provide higher prediction performance compared to other boosting methods. To demonstrate the validity of the proposed model, we conducted a global solar radiation prediction for two regions on Jeju Island, the largest island in South Korea. The experiment results demonstrated that the proposed model can achieve better predictive performance than the tree-based ensemble and deep learning methods.

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Sherozbek, Jumaboev, Jaewoo Park, Mohammad Shaheer Akhtar, and O.-Bong Yang. "Transformers-Based Encoder Model for Forecasting Hourly Power Output of Transparent Photovoltaic Module Systems." Energies 16, no. 3 (January 27, 2023): 1353. http://dx.doi.org/10.3390/en16031353.

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Solar power generation is usually affected by different meteorological factors, such as solar radiation, cloud cover, rainfall, and temperature. This variability has shown a negative impact on the large-scale integration of solar energy into energy supply systems. For successful integration of solar energy into the electrical grid, it is necessary to predict the accurate power generation by solar panels. In this work, solar power generation forecasting for two types of solar system (non-transparent and transparent panels) was configured by the smart artificial intelligence (AI) modelling. For deep learning models, the dataset obtained from the target value of electricity generation in kWh and other features, such as weather conditions, solar radiance, and insolation. In PV power generation values from non-transparent and transparent solar panels were collected from 1 January to 31 December 2021 with an hourly interval. To prove the efficiency of the proposed model, several deep learning approaches RNN models, such as LSTM, GRU, and transformers models, were implemented. Transformers model for forecasting power generation expressed the best model for non-transparent and transparent solar panels with lower error rates for MAE 0.05 and 0.04, and RMSE 0.24 and 0.21, respectively. The proposed model showed efficient performance and proved effective in forecasting time-series data.

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Balal, Afshin, Yaser Pakzad Jafarabadi, Ayda Demir, Morris Igene, Michael Giesselmann, and Stephen Bayne. "Forecasting Solar Power Generation Utilizing Machine Learning Models in Lubbock." Emerging Science Journal 7, no. 4 (July 12, 2023): 1052–62. http://dx.doi.org/10.28991/esj-2023-07-04-02.

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Solar energy is a widely accessible, clean, and sustainable energy source. Solar power harvesting in order to generate electricity on smart grids is essential in light of the present global energy crisis. However, the highly variable nature of solar radiation poses unique challenges for accurately predicting solar photovoltaic (PV) power generation. Factors such as cloud cover, atmospheric conditions, and seasonal variations significantly impact the amount of solar energy available for conversion into electricity. Therefore, it is essential to precisely estimate the output of solar power in order to assess the potential of smart grids. This paper presents a study that utilizes various machine learning models to predict solar photovoltaic (PV) power generation in Lubbock, Texas. Mean Squared Error (MSE) and R² metrics are utilized to demonstrate the performance of each model. The results show that the Random Forest Regression (RFR) and Long Short-Term Memory (LSTM) models outperformed the other models, with a MSE of 2.06% and 2.23% and R² values of 0.977 and 0.975, respectively. In addition, RFR and LSTM demonstrate their capability to capture the intricate patterns and complex relationships inherent in solar power generation data. The developed machine learning models can aid solar PV investors in streamlining their processes and improving their planning for the production of solar energy. Doi: 10.28991/ESJ-2023-07-04-02 Full Text: PDF

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Singh, Yogesh, and Amarendra Singh. "Forecasting Solar Radiation by the Machine Learning Algorithm & their Different Techniques." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (November 30, 2022): 406–11. http://dx.doi.org/10.22214/ijraset.2022.47345.

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Abstract: The objective of this study is to give a summary of machine learning-based techniques for solar irradiation forecasting in this context. Despite the fact that numerous research describe methods like neural networks or support vector regression. Ranking the performance of such methods is difficult because of the diversity of the data collection, time step, forecasting horizon, setup, and performance indicators. The prediction inaccuracy is quite comparable overall. Others write. Global solar radiation recommended utilising ensemble forecasting or hybrid models to improve prediction accuracy. Forecasting the output power of solar systems is required for the smooth operation of the power grid or for the optimal control of the energy flows into the solar system. Prior to projecting the output of the solar system, it is essential to focus on solar irradiance. The two primary categories of methods for predicting the global solar radiation are machine learning algorithms and cloud pictures combined with physical models.

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Kulkarni, Sonali N., and Prashant Shingare. "Generation Forecasting Models for Wind and Solar Power." International Journal of Computer and Electrical Engineering 10, no. 4 (2018): 318–29. http://dx.doi.org/10.17706/ijcee.2018.10.4.318-329.

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Devi, A. Shobana, G. Maragatham, K. Boopathi, and M. R. Prabu. "Short-term solar power forecasting using satellite images." International Journal of Powertrains 10, no. 2 (2021): 125. http://dx.doi.org/10.1504/ijpt.2021.117457.

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Devi, A. Shobana, G. Maragatham, M. R. Prabu, and K. Boopathi. "Short-term solar power forecasting using satellite images." International Journal of Powertrains 10, no. 2 (2021): 125. http://dx.doi.org/10.1504/ijpt.2021.10040726.

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Sheng, Hanmin, Biplob Ray, Kai Chen, and Yuhua Cheng. "Solar Power Forecasting Based on Domain Adaptive Learning." IEEE Access 8 (2020): 198580–90. http://dx.doi.org/10.1109/access.2020.3034100.

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Bessa, Ricardo J., Artur Trindade, and Vladimiro Miranda. "Spatial-Temporal Solar Power Forecasting for Smart Grids." IEEE Transactions on Industrial Informatics 11, no. 1 (February 2015): 232–41. http://dx.doi.org/10.1109/tii.2014.2365703.

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Chaturvedi, D. K. "Forecasting of Solar Power using Quantum GA - GNN." International Journal of Computer Applications 128, no. 3 (October 15, 2015): 15–19. http://dx.doi.org/10.5120/ijca2015906478.

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Journal articles on the topic 'SOLAR POWER FORECASTING'

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