Journal articles: 'SOLAR ENERGY FORECASTING'

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Sangrody, Hossein, Morteza Sarailoo, Ning Zhou, Nhu Tran, Mahdi Motalleb, and Elham Foruzan. "Weather forecasting error in solar energy forecasting." IET Renewable Power Generation 11, no. 10 (July 11, 2017): 1274–80. http://dx.doi.org/10.1049/iet-rpg.2016.1043.

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Chaudhary, Pankaj, Rohith Gattu, Soundarajan Ezekiel, and James Allen Rodger. "Forecasting Solar Radiation." Journal of Cases on Information Technology 23, no. 4 (October 2021): 1–21. http://dx.doi.org/10.4018/jcit.296263.

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Renewable energy, such as solar and wind, has been increasing in popularity for over a decade. This is especially true in rural, underdeveloped areas, and urban households that desire energy independence. Renewable energy sources, such as solar, provide enhanced environmental benefits while simultaneously minimizing the carbon footprint. One popular technology that can capture solar energy is solar panels. The demand for solar panels has been on the rise due to increases in energy conversion efficiency, long-term financial advantages, and contributions to decreasing fossil fuel usage. However, solar panels need a steady supply of sunlight. This can be challenging in many situations, geographies, and environments. This paper uses multiple machine learning (ML) algorithms that can predict future values of solar radiation based on previously observed values and other environmental features measured without the use of complex equipment with methods that are computationally efficient so that forecasting can be done on consumer premises.

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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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A. G. M. Amarasinghe, P., N. S. Abeygunawardana, T. N. Jayasekara, E. A. J. P. Edirisinghe, and S. K. Abeygunawardane. "Ensemble models for solar power forecasting—a weather classification approach." AIMS Energy 8, no. 2 (2020): 252–71. http://dx.doi.org/10.3934/energy.2020.2.252.

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Paulescu, Marius, Nicoleta Stefu, Ciprian Dughir, Robert Blaga, Andreea Sabadus, Eugenia Paulescu, and Sorin Bojin. "Online Forecasting of the Solar Energy Production." Annals of West University of Timisoara - Physics 60, no. 1 (August 1, 2018): 104–10. http://dx.doi.org/10.2478/awutp-2018-0011.

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AbstractForecasting the solar energy production is a key issue in the large-scale integration of the photovoltaic plants into the existing electricity grid. This paper reports on the research progress in forecasting the solar energy production at the West University of Timisoara, Romania. Firstly, the experimental facilities commissioned on the Solar Platform for testing the forecasting models are briefly described. Secondly, a new tool for the online forecasting of the solar energy production is introduced. Preliminary tests show that the implemented procedure is a successful trade-off between simplicity and 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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Madhiarasan, Manoharan, Mohamed Louzazni, and Brahim Belmahdi. "Statistical Analysis of Novel Ensemble Recursive Radial Basis Function Neural Network Performance on Global Solar Irradiance Forecasting." Journal of Electrical and Computer Engineering 2023 (March 28, 2023): 1–10. http://dx.doi.org/10.1155/2023/2554355.

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Reliable operation of energy management systems, grid stability, and managing energy demand responses are becoming challenging because of the flickering nature of solar irradiance. Accurate forecasting of global solar irradiance, i.e., global horizontal irradiance (GHI), plays a significant role in energy policy-making and the energy market. This paper proposes a novel global solar irradiance forecasting model based on the ensemble recursive radial basis function neural networks (ERRBFNNs). The various atmospheric inputs based on the built ensemble recursive radial basis function neural networks make the network more stable and robust to climatic uncertainty. This paper statistically investigates the performance of novel feed-forward neural networks based on forecasting models with various hidden nodes for global solar irradiance forecasting applications. We validated the proposed ERRBFNN global solar irradiance forecasting model using real-time data sets. The simulation results confirm that the proposed ensemble recursive radial basis function neural network based on global solar irradiance forecasting improves the accuracy, generalization, and network stability. Furthermore, the proposed ERRBFNN lowers the forecasting error to the least compared to other state-of-the-art forecasting models.

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Al-Ali, Elham M., Yassine Hajji, Yahia Said, Manel Hleili, Amal M. Alanzi, Ali H. Laatar, and Mohamed Atri. "Solar Energy Production Forecasting Based on a Hybrid CNN-LSTM-Transformer Model." Mathematics 11, no. 3 (January 28, 2023): 676. http://dx.doi.org/10.3390/math11030676.

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Green energy is very important for developing new cities with high energy consumption, in addition to helping environment preservation. Integrating solar energy into a grid is very challenging and requires precise forecasting of energy production. Recent advances in Artificial Intelligence have been very promising. Particularly, Deep Learning technologies have achieved great results in short-term time-series forecasting. Thus, it is very suitable to use these techniques for solar energy production forecasting. In this work, a combination of a Convolutional Neural Network (CNN), a Long Short-Term Memory (LSTM) network, and a Transformer was used for solar energy production forecasting. Besides, a clustering technique was applied for the correlation analysis of the input data. Relevant features in the historical data were selected using a self-organizing map. The hybrid CNN-LSTM-Transformer model was used for forecasting. The Fingrid open dataset was used for training and evaluating the proposed model. The experimental results demonstrated the efficiency of the proposed model in solar energy production forecasting. Compared to existing models and other combinations, such as LSTM-CNN, the proposed CNN-LSTM-Transformer model achieved the highest accuracy. The achieved results show that the proposed model can be used as a trusted forecasting technique that facilitates the integration of solar energy into grids.

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Chodakowska, Ewa, Joanicjusz Nazarko, Łukasz Nazarko, Hesham S. Rabayah, Raed M. Abendeh, and Rami Alawneh. "ARIMA Models in Solar Radiation Forecasting in Different Geographic Locations." Energies 16, no. 13 (June 28, 2023): 5029. http://dx.doi.org/10.3390/en16135029.

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The increasing demand for clean energy and the global shift towards renewable sources necessitate reliable solar radiation forecasting for the effective integration of solar energy into the energy system. Reliable solar radiation forecasting has become crucial for the design, planning, and operational management of energy systems, especially in the context of ambitious greenhouse gas emission goals. This paper presents a study on the application of auto-regressive integrated moving average (ARIMA) models for the seasonal forecasting of solar radiation in different climatic conditions. The performance and prediction capacity of ARIMA models are evaluated using data from Jordan and Poland. The essence of ARIMA modeling and analysis of the use of ARIMA models both as a reference model for evaluating other approaches and as a basic forecasting model for forecasting renewable energy generation are presented. The current state of renewable energy source utilization in selected countries and the adopted transition strategies to a more sustainable energy system are investigated. ARIMA models of two time series (for monthly and hourly data) are built for two locations and a forecast is developed. The research findings demonstrate that ARIMA models are suitable for solar radiation forecasting and can contribute to the stable long-term integration of solar energy into countries’ systems. However, it is crucial to develop location-specific models due to the variability of solar radiation characteristics. This study provides insights into the use of ARIMA models for solar radiation forecasting and highlights their potential for supporting the planning and operation of energy systems.

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Vennila, C., Anita Titus, T. Sri Sudha, U. Sreenivasulu, N. Pandu Ranga Reddy, K. Jamal, Dayadi Lakshmaiah, P. Jagadeesh, and Assefa Belay. "Forecasting Solar Energy Production Using Machine Learning." International Journal of Photoenergy 2022 (April 30, 2022): 1–7. http://dx.doi.org/10.1155/2022/7797488.

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When it comes to large-scale renewable energy plants, the future of solar power forecasting is vital to their success. For reliable predictions of solar electricity generation, one must take into consideration changes in weather patterns over time. In this paper, a hybrid model that integrates machine learning and statistical approaches is suggested for predicting future solar energy generation. In order to improve the accuracy of the suggested model, an ensemble of machine learning models was used in this study. The results of the simulation show that the proposed method has reduced placement cost, when compared with existing methods. When comparing the performance of an ensemble model that integrates all of the combination strategies to standard individual models, the suggested ensemble model outperformed the conventional individual models. According to the findings, a hybrid model that made use of both machine learning and statistics outperformed a model that made sole use of machine learning in its performance.

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Zwane, Nosipho, Henerica Tazvinga, Christina Botai, Miriam Murambadoro, Joel Botai, Jaco de Wit, Brighton Mabasa, Siphamandla Daniel, and Tafadzwanashe Mabhaudhi. "A Bibliometric Analysis of Solar Energy Forecasting Studies in Africa." Energies 15, no. 15 (July 29, 2022): 5520. http://dx.doi.org/10.3390/en15155520.

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Solar energy forecasting is considered an essential scientific aspect in supporting efforts to integrate solar energy into power grids. Moreover, solar energy forecasting plays an essential role in mitigating greenhouse gas emissions and conserving energy for future use. This study conducted a bibliometric analysis to assess solar energy forecasting research studies evolution at the continental (Africa) and southern Africa levels. Key aspects of analysis included (i) scientific research trends, (ii) nature of collaboration networks, (iii) co-occurrence of keywords and (iv) emerging themes in solar energy forecasting over the last two decades, between the years 2000–2021. The results indicate that solar energy forecasting research has, on average, expanded by 6.4% and 3.3% in Africa and southern Africa, respectively. Based on the study context, solar energy forecasting research only gained momentum in 2015, peaking in 2019, but it is generally still subtle. The scientific mapping illustrated that only South Africa ranks among the leading countries that have produced high numbers of published documents and also leads in contributions to the research area in both Africa and southern Africa. Three emerging topics were identified from the thematic map analysis—namely, “solar irradiance”, “artificial intelligence” and “clear sky”, which implies that researchers are paying attention to solar irradiance, using modelling techniques that incorporate machine learning techniques. Overall, this study contributes to scientific information on the potential bankability of renewable energy projects that could assist power utilities, governments and policymakers in Africa to enforce the green economy through accelerated decarbonisation of the energy systems and building relationships with developed countries for support and better transitioning to solar energy. From a Water–Energy–Food nexus perspective, the results of this work could assist the scientific community in Africa to take advantage of the inherent interconnectedness of water, energy and food resources, whilst also advancing the use of integrated solutions to shape the focus of solar energy research into a more systems thinking and transdisciplinary approach involving the interconnected primary resources and stakeholders pursuit of the Sustainable Development Goals.

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Cheon, Jae ho, Jung-Tae Lee, Hyun-Goo Kim, Yong-Heack Kang, Chang-Yeol Yun, Chang Ki Kim, Bo-Young Kim, et al. "Trend Review of Solar Energy Forecasting Technique." Journal of the Korean Solar Energy Society 39, no. 4 (August 1, 2019): 41–54. http://dx.doi.org/10.7836/kses.2019.39.4.041.

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Inman, Rich H., Hugo T. C. Pedro, and Carlos F. M. Coimbra. "Solar forecasting methods for renewable energy integration." Progress in Energy and Combustion Science 39, no. 6 (December 2013): 535–76. http://dx.doi.org/10.1016/j.pecs.2013.06.002.

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Wang, Huaning, Yihua Yan, Han He, Xin Huang, Xinghua Dai, Xiaoshuai Zhu, Zhanle Du, Hui Zhao, and Yan Yan. "Numerical Short-Term Solar Activity Forecasting." Proceedings of the International Astronomical Union 13, S335 (July 2017): 243–49. http://dx.doi.org/10.1017/s1743921318000534.

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AbstractIt is well known that the energy for solar eruptions comes from magnetic fields in solar active regions. Magnetic energy storage and dissipation are regarded as important physical processes in the solar corona. With incomplete theoretical modeling for eruptions in the solar atmosphere, activity forecasting is mainly supported with statistical models. Solar observations with high temporal and spatial resolution continuously from space well describe the evolution of activities in the solar atmosphere, and combined with three dimensional reconstruction of solar magnetic fields, makes numerical short-term (within hours to days) solar activity forecasting possible. In the current report, we propose the erupting frequency and main attack direction of solar eruptions as new forecasts and present the prospects for numerical short-term solar activity forecasting based on the magnetic topological framework in solar active regions.

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Sudharshan, Konduru, C. Naveen, Pradeep Vishnuram, Damodhara Venkata Siva Krishna Rao Krishna Rao Kasagani, and Benedetto Nastasi. "Systematic Review on Impact of Different Irradiance Forecasting Techniques for Solar Energy Prediction." Energies 15, no. 17 (August 28, 2022): 6267. http://dx.doi.org/10.3390/en15176267.

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As non-renewable energy sources are in the verge of exhaustion, the entire world turns towards renewable sources to fill its energy demand. In the near future, solar energy will be a major contributor of renewable energy, but the integration of unreliable solar energy sources directly into the grid makes the existing system complex. To reduce the complexity, a microgrid system is a better solution. Solar energy forecasting models improve the reliability of the solar plant in microgrid operations. Uncertainty in solar energy prediction is the challenge in generating reliable energy. Employing, understanding, training, and evaluating several forecasting models with available meteorological data will ensure the selection of an appropriate forecast model for any particular location. New strategies and approaches emerge day by day to increase the model accuracy, with an ultimate objective of minimizing uncertainty in forecasting. Conventional methods include a lot of differential mathematical calculations. Large data availability at solar stations make use of various Artificial Intelligence (AI) techniques for computing, forecasting, and predicting solar radiation energy. The recent evolution of ensemble and hybrid models predicts solar radiation accurately compared to all the models. This paper reviews various models in solar irradiance and power estimation which are tabulated by classification types mentioned.

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Korneychuk, B. V. "Forecasting the solar energy development in the region." Regional nye issledovaniya, no. 3 (2020): 16–25. http://dx.doi.org/10.5922/1994-5280-2020-3-2.

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The problem of forecasting the dynamics of the development of solar energy in the region in the context of the global trend of energy transition is considered. The urgency of the problem is due to the fact that forecasts of the development of solar energy are usually characterized by relatively large errors. To solve this problem, the author proposed a multi-trend approach to constructing a regional function for the growth of solar power capacity. The method is based on the description of the dynamics of power growth in the form of the average value of logistic, linear and exponential trends. The weighting factors are equal to values that are inversely proportional to the errors of the corresponding trends. Based on this method, forecasts of solar energy capacity were calculated for Africa, Asia, Europe, North America and South America for the period 2017–2019. The validity of the method is confirmed by the fact that these forecasts are characterized by a relatively low deviation from the actual data. The author has developed a forecast for these regions for the period 2020-2023. It is shown that the reason for the low reliability of most forecasts is the desire to use the logistic curve as a universal analysis tool. This approach absolutes the logistics trend and does not take into account the specifics of the region. However, for some regions, a linear or exponential trend can serve as the dominant growth trend in solar energy capacity. In particular, the reason for the systematic underestimation of forecasts for China was the ignorance of the exponential component of the growth of solar energy capacity.

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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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Pospíchal, Jiří, Martin Kubovčík, and Iveta Dirgová Luptáková. "Solar Irradiance Forecasting with Transformer Model." Applied Sciences 12, no. 17 (September 2, 2022): 8852. http://dx.doi.org/10.3390/app12178852.

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Solar energy is one of the most popular sources of renewable energy today. It is therefore essential to be able to predict solar power generation and adapt energy needs to these predictions. This paper uses the Transformer deep neural network model, in which the attention mechanism is typically applied in NLP or vision problems. Here, it is extended by combining features based on their spatiotemporal properties in solar irradiance prediction. The results were predicted for arbitrary long-time horizons since the prediction is always 1 day ahead, which can be included at the end along the timestep axis of the input data and the first timestep representing the oldest timestep removed. A maximum worst-case mean absolute percentage error of 3.45% for the one-day-ahead prediction was obtained, which gave better results than the directly competing methods.

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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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Mukaram, Muhammad Zillullah, and Fadhilah Yusof. "Solar radiation forecast using hybrid SARIMA and ANN model." Malaysian Journal of Fundamental and Applied Sciences 13, no. 4-1 (December 5, 2017): 346–50. http://dx.doi.org/10.11113/mjfas.v13n4-1.895.

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Solar Energy have an enormous potential for generating renewable electricity. In the tropics solar energy are abundance all year long but suffer from uncertainty caused by rain and clouds. Accurate prediction of solar radiation can increase the affectivity and productivity of solar energy sources. Monthly average of solar radiation data are obtained from stations in Malaysia. The data are modeled using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model, artificial neural network (ANN) model and Hybrid ANN and SARIMA model. The SARIMA model is a reliable tool in forecasting seasonal data, on the other hand the ANN model have been proven to be a good model in forecasting non-linear data. By combining both model a more accurate model can be obtained. Finally the forecasting performance each model is compared by using mean absolute error (MAE), the mean absolute percentage error (MAPE) and root mean square error (RMSE). The result shows that the hybrid model is better in forecasting solar radiation data.

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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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Mohsin, Syed Muhammad, Tahir Maqsood, and Sajjad Ahmed Madani. "Solar and Wind Energy Forecasting for Green and Intelligent Migration of Traditional Energy Sources." Sustainability 14, no. 23 (December 6, 2022): 16317. http://dx.doi.org/10.3390/su142316317.

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Fossil-fuel-based power generation leads to higher energy costs and environmental impacts. Solar and wind energy are abundant important renewable energy sources (RES) that make the largest contribution to replacing fossil-fuel-based energy consumption. However, the uncertain solar radiation and highly fluctuating weather parameters of solar and wind energy require an accurate and reliable forecasting mechanism for effective and efficient load management, cost reduction, green environment, and grid stability. From the existing literature, artificial neural networks (ANN) are a better means for prediction, but the ANN-based renewable energy forecasting techniques lose prediction accuracy due to the high uncertainty of input data and random determination of initial weights among different layers of ANN. Therefore, the objective of this study is to develop a harmony search algorithm (HSA)-optimized ANN model for reliable and accurate prediction of solar and wind energy. In this study, we combined ANN with HSA and provided ANN feedback for its weights adjustment to HSA, instead of ANN. Then, the HSA optimized weights were assigned to the edges of ANN instead of random weights, and this completes the training of ANN. Extensive simulations were carried out and our proposed HSA-optimized ANN model for solar irradiation forecast achieved the values of MSE = 0.04754, MAE = 0.18546, MAPE = 0.32430%, and RMSE = 0.21805, whereas our proposed HSA-optimized ANN model for wind speed prediction achieved the values of MSE = 0.30944, MAE = 0.47172, MAPE = 0.12896%, and RMSE = 0.55627. Simulation results prove the supremacy of our proposed HSA-optimized ANN models compared to state-of-the-art solar and wind energy forecasting techniques.

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Woo, JongRoul, Hye-Jeong Lee, and Sung-Yoon Huh. "Forecasting Solar Photovoltaic and Wind Power Deployment in South Korea: An Innovation Diffusion Approach." Journal of Energy Engineering 31, no. 1 (March 31, 2022): 16–29. http://dx.doi.org/10.5855/energy.2022.31.1.016.

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Al-Jaafreh, Tamer Mushal, and Abdullah Al-Odienat. "The Solar Energy Forecasting by Pearson Correlation using Deep Learning Techniques." EARTH SCIENCES AND HUMAN CONSTRUCTIONS 2 (August 2, 2022): 158–63. http://dx.doi.org/10.37394/232024.2022.2.19.

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Solar energy is one of the most important renewable energy sources (RES) with many advantages as compared to other types of sources. Climate change is gradually becoming a global challenge for the sustainable development of humanity. There will potentially be two key features, for future electricity systems, high penetration or even dominance of renewable energy sources for clean energy e.g., onshore/offshore wind and solar PV. Solar energy forecasting is essential for the energy market. Machine learning and deep learning techniques are commonly used for providing an accurate forecasting of the energy that will be produced. The weather factors are related to each other in terms of influence, a wide range of features that are necessary to consider in the prediction process. In this paper, the effect of some atmospheric factors like Evapotranspiration and soil temperature are investigated using deep learning techniques. Higher accuracy is achieved when new features related to solar irradiation were considered in the forecasting process.

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Manjili, Yashar Sahraei, Rolando Vega, and Mo M. Jamshidi. "Data-Analytic-Based Adaptive Solar Energy Forecasting Framework." IEEE Systems Journal 12, no. 1 (March 2018): 285–96. http://dx.doi.org/10.1109/jsyst.2017.2769483.

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Kaur, Amanpreet, Lukas Nonnenmacher, Hugo T. C. Pedro, and Carlos F. M. Coimbra. "Benefits of solar forecasting for energy imbalance markets." Renewable Energy 86 (February 2016): 819–30. http://dx.doi.org/10.1016/j.renene.2015.09.011.

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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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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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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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Pašić, Lejla, Azra Pašić, Alija Pašić, and István Vokony. "ANN-Based Large-Scale Cooperative Solar Generation Forecasting." Renewable Energy and Power Quality Journal 20 (September 2022): 559–63. http://dx.doi.org/10.24084/repqj20.367.

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In this work we introduce the concept and method of so-called cooperative solar generation forecasting, where geographically close data sources are utilized in order to improve forecasting accuracy. We devised and examined various largescale one-hour-ahead artificial neural networks based solar generation forecasting scenarios to prove the benefits of cooperation. The introduced cooperative solar generation forecasting method showed significant improvement in forecasting accuracy, especially when combined with previous generation data, where a root mean square error reduction of at least 50% could be achieved in the majority of cases. We believe these results point to a scientific and economical benefit of international cooperation in solar generation forecasting.

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Park, Jinwoong, Sungwoo Park, Jonghwa Shim, and Eenjun Hwang. "Domain Hybrid Day-Ahead Solar Radiation Forecasting Scheme." Remote Sensing 15, no. 6 (March 17, 2023): 1622. http://dx.doi.org/10.3390/rs15061622.

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Recently, energy procurement by renewable energy sources has increased. In particular, as solar power generation has a high penetration rate among them, solar radiation predictions at the site are attracting much attention for efficient operation. Various approaches have been proposed to forecast solar radiation accurately. Recently, hybrid models have been proposed to improve performance through forecasting in the frequency domain using past solar radiation. Since solar radiation data have a pattern, forecasting in the frequency domain can be effective. However, forecasting performance deteriorates on days when the weather suddenly changes. In this paper, we propose a domain hybrid forecasting model that can respond to weather changes and exhibit improved performance. The proposed model consists of two stages. In the first stage, forecasting is performed in the frequency domain using wavelet transform, complete ensemble empirical mode decomposition, and multilayer perceptron, while forecasting in the sequence domain is accomplished using light gradient boosting machine. In the second stage, a multilayer perceptron-based domain hybrid model is constructed using the forecast values of the first stage as the input. Compared with the frequency-domain model, our proposed model exhibits an improvement of up to 36.38% in the normalized root-mean-square error.

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Aslam, Muhammad, Jae-Myeong Lee, Hyung-Seung Kim, Seung-Jae Lee, and Sugwon Hong. "Deep Learning Models for Long-Term Solar Radiation Forecasting Considering Microgrid Installation: A Comparative Study." Energies 13, no. 1 (December 27, 2019): 147. http://dx.doi.org/10.3390/en13010147.

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Microgrid is becoming an essential part of the power grid regarding reliability, economy, and environment. Renewable energies are main sources of energy in microgrids. Long-term solar generation forecasting is an important issue in microgrid planning and design from an engineering point of view. Solar generation forecasting mainly depends on solar radiation forecasting. Long-term solar radiation forecasting can also be used for estimating the degradation-rate-influenced energy potentials of photovoltaic (PV) panel. In this paper, a comparative study of different deep learning approaches is carried out for forecasting one year ahead hourly and daily solar radiation. In the proposed method, state of the art deep learning and machine learning architectures like gated recurrent units (GRUs), long short term memory (LSTM), recurrent neural network (RNN), feed forward neural network (FFNN), and support vector regression (SVR) models are compared. The proposed method uses historical solar radiation data and clear sky global horizontal irradiance (GHI). Even though all the models performed well, GRU performed relatively better compared to the other models. The proposed models are also compared with traditional state of the art methods for long-term solar radiation forecasting, i.e., random forest regression (RFR). The proposed models outperformed the traditional method, hence proving their efficiency.

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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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Boland, John. "Characterising Seasonality of Solar Radiation and Solar Farm Output." Energies 13, no. 2 (January 18, 2020): 471. http://dx.doi.org/10.3390/en13020471.

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With the recent rapid increase in the use of roof top photovoltaic solar systems worldwide, and also, more recently, the dramatic escalation in building grid connected solar farms, especially in Australia, the need for more accurate methods of very short-term forecasting has become a focus of research. The International Energy Agency Tasks 46 and 16 have brought together groups of experts to further this research. In Australia, the Australian Renewable Energy Agency is funding consortia to improve the five minute forecasting of solar farm output, as this is the time scale of the electricity market. The first step in forecasting of either solar radiation or output from solar farms requires the representation of the inherent seasonality. One can characterise the seasonality in climate variables by using either a multiplicative or additive modelling approach. The multiplicative approach with respect to solar radiation can be done by calculating the clearness index, or alternatively estimating the clear sky index. The clearness index is defined as the division of the global solar radiation by the extraterrestrial radiation, a quantity determined only via astronomical formulae. To form the clear sky index one divides the global radiation by a clear sky model. For additive de-seasoning, one subtracts some form of a mean function from the solar radiation. That function could be simply the long term average at the time steps involved, or more formally the addition of terms involving a basis of the function space. An appropriate way to perform this operation is by using a Fourier series set of basis functions. This article will show that for various reasons the additive approach is superior. Also, the differences between the representation for solar energy versus solar farm output will be demonstrated. Finally, there is a short description of the subsequent steps in short-term forecasting.

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Aslam, Muhammad, Jae-Myeong Lee, Mustafa Altaha, Seung-Jae Lee, and Sugwon Hong. "AE-LSTM Based Deep Learning Model for Degradation Rate Influenced Energy Estimation of a PV System." Energies 13, no. 17 (August 24, 2020): 4373. http://dx.doi.org/10.3390/en13174373.

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With the increase in penetration of photovoltaics (PV) into the power system, the correct prediction of return on investment requires accurate prediction of decrease in power output over time. Degradation rates and corresponding degraded energy estimation must be known in order to predict power delivery accurately. Solar radiation plays a key role in long-term solar energy predictions. A combination of auto-encoder and long short-term memory (AE-LSTM) based deep learning approach is adopted for long-term solar radiation forecasting. First, the auto-encoder (AE) is trained for the feature extraction, and then fine-tuning with long short-term memory (LSTM) is done to get the final prediction. The input data consist of clear sky global horizontal irradiance (GHI) and historical solar radiation. After forecasting the solar radiation for three years, the corresponding degradation rate (DR) influenced energy potentials of an a-Si PV system is estimated. The estimated energy is useful economically for planning and installation of energy systems like microgrids, etc. The method of solar radiation forecasting and DR influenced energy estimation is compared with the traditional methods to show the efficiency of the proposed method.

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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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Zhou, Hangxia, Qian Liu, Ke Yan, and Yang Du. "Deep Learning Enhanced Solar Energy Forecasting with AI-Driven IoT." Wireless Communications and Mobile Computing 2021 (June 18, 2021): 1–11. http://dx.doi.org/10.1155/2021/9249387.

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Short-term photovoltaic (PV) energy generation forecasting models are important, stabilizing the power integration between the PV and the smart grid for artificial intelligence- (AI-) driven internet of things (IoT) modeling of smart cities. With the recent development of AI and IoT technologies, it is possible for deep learning techniques to achieve more accurate energy generation forecasting results for the PV systems. Difficulties exist for the traditional PV energy generation forecasting method considering external feature variables, such as the seasonality. In this study, we propose a hybrid deep learning method that combines the clustering techniques, convolutional neural network (CNN), long short-term memory (LSTM), and attention mechanism with the wireless sensor network to overcome the existing difficulties of the PV energy generation forecasting problem. The overall proposed method is divided into three stages, namely, clustering, training, and forecasting. In the clustering stage, correlation analysis and self-organizing mapping are employed to select the highest relevant factors in historical data. In the training stage, a convolutional neural network, long short-term memory neural network, and attention mechanism are combined to construct a hybrid deep learning model to perform the forecasting task. In the testing stage, the most appropriate training model is selected based on the month of the testing data. The experimental results showed significantly higher prediction accuracy rates for all time intervals compared to existing methods, including traditional artificial neural networks, long short-term memory neural networks, and an algorithm combining long short-term memory neural network and attention mechanism.

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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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Hasanah, P., S. A. Wiradinata, and M. Azka. "Forecasting approach for solar power based on weather parameters (Case study: East Kalimantan)." Journal of Physics: Conference Series 2106, no. 1 (November 1, 2021): 012022. http://dx.doi.org/10.1088/1742-6596/2106/1/012022.

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Abstract Solar Energy is the most popular among several clean energies. As a tropical country, Indonesia has big opportunity to develop solar power, particularly in East Kalimantan which spans around the equator. Solar energy generation, however, is influenced by weather parameters which give uncertain values of the amount of the captured energy. Therefore, this research is conducted to overcome the effect of weather towards solar energy. The aim of this research is to examine the model for sun power forecasting based on the data. The Artificial Neural Network (ANN) and Multiple Linear Regression have taken as the approach models to determine energy forecasting. This study used five input variables; temperature, precipitation level, humidity, wind speed, and surface pressure, while the solar radiation was taken as the output variable. Moreover, the daily solar power and weather data from East Kalimantan has been taken along the period of 27th July 2018 – 28th July 2021. The result of this study showed that the RMSE of ANN was slightly similar with the multiple linear regression methods which were calculated by 160.26 and 160.46 respectively. However, the ANN is preferable to use in the solar energy forecasting since the tendency of nonlinearity of the climate data.

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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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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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Milicevic, Marina, and Budimirka Marinovic. "Machine learning methods in forecasting solar photovoltaic energy production." Thermal Science, no. 00 (2023): 150. http://dx.doi.org/10.2298/tsci230402150m.

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Energy has an effective role in economic growth and development of societies. This paper is studying the impact of climate factors on performance of solar power plant using machine learning techniques for underlying relationship among factors that impact solar energy production and for forecasting monthly energy production. In this context this work provides two machine learning methods: Artificial Neural Network (ANN) for forecasting energy production and Decision Tree (DC) useful in understanding the relationships in energy production data. Both structures have horizontal irradiation, sunlight duration, average monthly air temperature, average maximal air temperature, average minimal air temperature and average monthly wind speed as inputs parameters and the energy production as output. Results have shown that used machine learning models perform effectively, ANN predicted the energy production of the PV power plant with a correlation coefficient (R) higher than 0.97. The results can help stakeholders in determining energy policy planning in order to overcome uncertainties associated with renewable energy resources.

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Borunda, Monica, Adrián Ramírez, Raul Garduno, Gerardo Ruíz, Sergio Hernandez, and O. A. Jaramillo. "Photovoltaic Power Generation Forecasting for Regional Assessment Using Machine Learning." Energies 15, no. 23 (November 24, 2022): 8895. http://dx.doi.org/10.3390/en15238895.

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Solar energy currently plays a significant role in supplying clean and renewable electric energy worldwide. Harnessing solar energy through PV plants requires problems such as site selection to be solved, for which long-term solar resource assessment and photovoltaic energy forecasting are fundamental issues. This paper proposes a fast-track methodology to address these two critical requirements when exploring a vast area to locate, in a first approximation, potential sites to build PV plants. This methodology retrieves solar radiation and temperature data from free access databases for the arbitrary division of the region of interest into land cells. Data clustering and probability techniques were then used to obtain the mean daily solar radiation per month per cell, and cells are clustered by radiation level into regions with similar solar resources, mapped monthly. Simultaneously, temperature probabilities are determined per cell and mapped. Then, PV energy is calculated, including heat losses. Finally, PV energy forecasting is accomplished by constructing the P50 and P95 estimations of the mean yearly PV energy. A case study in Mexico fully demonstrates the methodology using hourly data from 2000 to 2020 from NSRDB. The proposed methodology is validated by comparison with actual PV plant generation throughout the country.

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Li, Xianglong, Longfei Ma, Ping Chen, Hui Xu, Qijing Xing, Jiahui Yan, Siyue Lu, Haohao Fan, Lei Yang, and Yongqiang Cheng. "Probabilistic solar irradiance forecasting based on XGBoost." Energy Reports 8 (August 2022): 1087–95. http://dx.doi.org/10.1016/j.egyr.2022.02.251.

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Oh, Myeongchan, Chang Ki Kim, Boyoung Kim, Changyeol Yun, Yong-Heack Kang, and Hyun-Goo Kim. "Spatiotemporal Optimization for Short-Term Solar Forecasting Based on Satellite Imagery." Energies 14, no. 8 (April 15, 2021): 2216. http://dx.doi.org/10.3390/en14082216.

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Solar forecasting is essential for optimizing the integration of solar photovoltaic energy into a power grid. This study presents solar forecasting models based on satellite imagery. The cloud motion vector (CMV) model is the most popular satellite-image-based solar forecasting model. However, it assumes constant cloud states, and its accuracy is, thus, influenced by changes in local weather characteristics. To overcome this limitation, satellite images are used to provide spatial data for a new spatiotemporal optimized model for solar forecasting. Four satellite-image-based solar forecasting models (a persistence model, CMV, and two proposed models that use clear-sky index change) are evaluated. The error distributions of the models and their spatial characteristics over the test area are analyzed. All models exhibited different performances according to the forecast horizon and location. Spatiotemporal optimization of the best model is then conducted using best-model maps, and our results show that the skill score of the optimized model is 21% better than the previous CMV model. It is, thus, considered to be appropriate for use in short-term forecasting over large areas. The results of this study are expected to promote the use of spatial data in solar forecasting models, which could improve their accuracy and provide various insights for the planning and operation of photovoltaic plants.

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XIE, Jingrui, and Tao HONG. "Load forecasting using 24 solar terms." Journal of Modern Power Systems and Clean Energy 6, no. 2 (January 27, 2018): 208–14. http://dx.doi.org/10.1007/s40565-017-0374-0.

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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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Isma’il, Muhammad, and Salisu Aliyu. "Daily Solar Radiation Forecasting for Northwest Nigeria Using Long Short-Term Memory." International Journal of Science for Global Sustainability 9, no. 1 (March 31, 2023): 8. http://dx.doi.org/10.57233/ijsgs.v9i1.407.

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In order to ensure energy security and environmental sustainability, transition to renewable energy sources is required. One of the most viable and sustainable renewable energy sources is solar. However, developing solar energy systems requires solar radiation data which is scarce for most locations including Northwest Nigeria. In order to address this challenge, solar radiation is usually estimated from the available meteorological parameters. Several previous studies have used various methods including geospatial techniques and machine learning to predict monthly and yearly solar radiation, while few studies have focused on the estimation of daily solar radiation. Meanwhile, providing daily solar radiation data is necessary for the development of solar energy systems. Deep learning has been shown to be effective in solar radiation forecasting. To evaluate the performance of the deep learning method for daily solar radiation prediction, a Long Short-Term Memory (LSTM) based deep learning model was developed in this study. The forecasting model was created using daily solar radiation data collected over a 21-year period by the Nigerian Meteorological Agency in three major towns in North West Nigeria: Kano, Kaduna, and Katsina. The model was evaluated using two statistical indicators: coefficient of determination (R2) and Root Mean Square Error (RMSE). Results showed that R2 of 0.79 and 0.78 were obtained for the training and testing datasets respectively, while RMSE of 0.46 and 0.47 were obtained for the training and testing datasets respectively. Overall, the LSTM deep learning model has been proven to be effective in forecasting daily solar radiation.

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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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Ahmed, Rohail, Hafiz Muhammad Khurram Ali, and Qasim Habib. "Development of Decision Support System for Solar Farm Location on CPEC using GIS and Forecasting." International Journal of Advanced Natural Sciences and Engineering Researches 7, no. 5 (June 21, 2023): 93–99. http://dx.doi.org/10.59287/ijanser.908.

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Due to the increasing expansion of renewable energy sources, it is essential to use effective decision-making techniques when picking ideal places for solar farms. This research uses geographic information systems (GIS) and forecasting methods to offer a decision support system (DSS) for identifying solar farm locations along the China-Pakistan Economic Corridor (CPEC). Helping stakeholders, decisionmakers, and investors locate the best locations for solar farm projects inside the CPEC region is the goal. In order to examine numerous spatial and non-spatial elements that affect the feasibility of solar farms, the proposed DSS uses GIS technology. The DSS also uses forecasting techniques to determine the potential for future solar energy production. It is possible to anticipate sun irradiation levels using historical weather data and meteorological projections, which enables precise estimation of solar farm output. By incorporating forecasting methods, decision-making is improved, resulting in the selection of sites with high solar energy potential and a decreased chance of underperformance. Stakeholders can effectively interact with the proposed DSS thanks to its user-friendly interface. It simplifies data entry, analysis, and visualization, enabling users to explore multiple scenarios and assess the influence of various factors on the choice of solar farm placement. The use of the decision support system for identifying solar farm locationson the CPEC will aid in the region's continued sustainable development of renewable energy sources.

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Journal articles on the topic 'SOLAR ENERGY FORECASTING'

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