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Artykuły w czasopismach na temat „SOLAR POWER FORECASTING”

Autor: Grafiati

Data publikacji: 11 września 2023

Data aktualizacji: 25 lipca 2025

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1

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 forecas

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

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Poti, Keaobaka D., Raj M. Naidoo, Nsilulu T. Mbungu, and Ramesh C. Bansal. "Intelligent solar photovoltaic power forecasting." Energy Reports 9 (October 2023): 343–52. http://dx.doi.org/10.1016/j.egyr.2023.09.004.

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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 (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 forecast

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Yuan, Ziran, Pengli Zhang, Bo Ming, Xiaobo Zheng, and Lu Tian. "Joint Forecasting Method of Wind and Solar Outputs Considering Temporal and Spatial Correlation." Sustainability 15, no. 19 (2023): 14628. http://dx.doi.org/10.3390/su151914628.

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In response to the problem of low forecasting accuracy in wind and solar power outputs, this study proposes a joint forecasting method for wind and solar power outputs by using their spatiotemporal correlation. First, autocorrelation analysis and causal testing are used to screen the forecasting factors. Then, a convolutional neural network–long short-term memory (CNN-LSTM) is constructed and trained to extract features effectively. Finally, the independent, ensemble, and joint forecasting effects are compared, using a certain clean energy base as the research object. Results show that the for

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Энгель, Е. А., and Н. Е. Энгель. "IMPLEMENTING AN INTELLIGENT SYSTEM OF INDIRECT FORECASTING OF SOLAR POWER GENERATION AS COMPUTER SOFTWARE." Proceedings in Cybernetics 23, no. 1 (2024): 68–74. http://dx.doi.org/10.35266/1999-7604-2024-1-9.

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The forecasting of electric power generated by a solar power plant enables effective and safe control over electric networks which integrate a cluster of solar power plants. Penalty rates for the purchase of solar power at the day-ahead market, which deviates by more than 5 % of the maximum capacity of solar power plants from the provided hourly model of the day-ahead market of solar power generation, update the accuracy of the day-ahead market model through effective intelligent systems for forecasting solar power generation. It has been found that there is no accessible software for successf

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Iheanetu, Kelachukwu J. "Solar Photovoltaic Power Forecasting: A Review." Sustainability 14, no. 24 (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 year

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Mittal, Amit Kumar, and Kirti Mathur. "An Efficient Short-Term Solar Power Forecasting by Hybrid WOA-Based LSTM Model in Integrated Energy System." Indian Journal Of Science And Technology 17, no. 5 (2024): 397–408. http://dx.doi.org/10.17485/ijst/v17i5.2020.

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Objectives: Due to the irregular nature of sun irradiation and other meteorological conditions, solar power generation is constantly loaded with risks. When solar radiation data isn't captured and sky imaging equipment isn't available, improving forecasting becomes a more difficult endeavor. So our objective to improve the forecasting accuracy for next year solar power generation data. Methods: Our research used a real numerical solar power dataset of Australia and Germany and a standard approach for preprocessing. The feature selection in this research uses the Whale Optimization Algorithm (W

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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 (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

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10

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

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11

Chin, Kho Lee. "A Case Study of Using Long Short-Term Memory (LSTM) Algorithm in Solar Photovoltaic Power Forecasting." ASM Science Journal 18 (December 26, 2023): 1–8. http://dx.doi.org/10.32802/asmscj.2023.1162.

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Solar photovoltaic power plays an important role in distributed energy resources. The number of solar-powered electricity generation has increased steadily in recent years all over the world. This happens because it produces clean energy, and solar photovoltaic technology is continuously developing. One of the challenges in solar photovoltaic is that power generation is highly dependent on the dynamic changes of environmental parameters and asset operating conditions. Solar power forecasting can be a possible solution to maximise the electricity generation capability of the solar photovoltaic

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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 (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 (2009): 1772–83. http://dx.doi.org/10.1016/j.solener.2009.05.016.

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Amit Kumar Mittal. "Enhancing Solar Power Forecasting using Grasshopper optimization and Whale Optimization Algorithm." Journal of Electrical Systems 20, no. 3 (2024): 2054–59. http://dx.doi.org/10.52783/jes.4005.

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Solar power forecasting is essential for effectively integrating solar energy into the power system. Accurate forecasting allows for more effective power system planning, operation, and management. In this research work, we employed the Grasshopper Optimization Algorithm (GOA) and Whale Optimization Algorithm (WOA) to choose features for solar power forecasting utilizing time series data from the OPSD dataset. The dataset contains measurements taken at 15-minute intervals, giving a wealth of data for training and verifying forecasting algorithms. The WOA is used to adjust the parameters of a f

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16

Amit, Kumar Mittal, and Mathur Kirti. "An Efficient Short-Term Solar Power Forecasting by Hybrid WOA-Based LSTM Model in Integrated Energy System." Indian Journal of Science and Technology 17, no. 5 (2024): 397–408. https://doi.org/10.17485/IJST/v17i5.2020.

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Abstract <strong>Objectives:</strong> Due to the irregular nature of sun irradiation and other meteorological conditions, solar power generation is constantly loaded with risks. When solar radiation data isn't captured and sky imaging equipment isn't available, improving forecasting becomes a more difficult endeavor. So our objective to improve the forecasting accuracy for next year solar power generation data. <strong>Methods:</strong> Our research used a real numerical solar power dataset of Australia and Germany and a standard approach for preprocessing. The feature selection

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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 (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 s

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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 pro

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Lari, Ali Jassim, Antonio P. Sanfilippo, Dunia Bachour, and Daniel Perez-Astudillo. "Using Machine Learning Algorithms to Forecast Solar Energy Power Output." Electronics 14, no. 5 (2025): 866. https://doi.org/10.3390/electronics14050866.

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Solar energy is an inherently variable energy resource, and the ensuing uncertainty in matching energy demand presents a challenge in its operational use as an alternative energy source. The factors influencing solar energy power generation include geographic location, solar radiation, weather conditions, and solar panel performance. Solar energy forecasting is performed using machine learning for better accuracy and performance. Due to the variability of solar energy, the forecasting window is an important aspect of solar energy forecasting that must be integrated into any machine learning mo

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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 (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 s

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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 solut

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Huang, Cheng-Liang, Yuan-Kang Wu, Chin-Cheng Tsai, Jing-Shan Hong, and Yuan-Yao Li. "Revolutionizing Solar Power Forecasts by Correcting the Outputs of the WRF-SOLAR Model." Energies 17, no. 1 (2023): 88. http://dx.doi.org/10.3390/en17010088.

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Climate change poses a significant threat to humanity. Achieving net-zero emissions is a key goal in many countries. Among various energy resources, solar power generation is one of the prominent renewable energy sources. Previous studies have demonstrated that post-processing techniques such as bias correction can enhance the accuracy of solar power forecasting based on numerical weather prediction (NWP) models. To improve the post-processing technique, this study proposes a new day-ahead forecasting framework that integrates weather research and forecasting solar (WRF-Solar) irradiances and

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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 (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

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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, p

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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 (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, vari

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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 (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) wa

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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 (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 irradia

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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 (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

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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 (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 f

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Winster Praveenraj, D. David, Madeswaran A, Rishab Pastariya, Deepti Sharma, Kassem Abootharmahmoodshakir, and Anishkumar Dhablia. "Machine Learning Integration for Enhanced Solar Power Generation Forecasting." E3S Web of Conferences 540 (2024): 04007. http://dx.doi.org/10.1051/e3sconf/202454004007.

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This paper reviews the advancements in machine learning techniques for enhanced solar power generation forecasting. Solar energy, a potent alternative to traditional energy sources, is inherently intermittent due to its weather-dependent nature. Accurate forecasting of photovoltaic power generation (PVPG) is paramount for the stability and reliability of power systems. The review delves into a deep learning framework that leverages the long short-term memory (LSTM) network for precise PVPG forecasting. A novel approach, the physics-constrained LSTM (PCLSTM), is introduced, addressing the limit

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Li, Wang, Zhang, Xin, and Liu. "Recurrent Neural Networks Based Photovoltaic Power Forecasting Approach." Energies 12, no. 13 (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 predi

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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

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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 (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 compo

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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 diff

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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 (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 meteorol

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Rajnish, Sumit Saroha, and Manish Saini. "PV ENERGY FORECASTING USING DEEP LEARNING ALGORITHM." Suranaree Journal of Science and Technology 31, no. 2 (2024): 010298(1–11). http://dx.doi.org/10.55766/sujst-2024-02-e02972.

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Solar energy has vast potential in India which is a rapidly growing economy with diverse geographical features. Solar energy has intermittent behaviour and depends on geographical and weather conditions. Therefore, the reliability of the solar depends on the seamless operation of solar plants with the latest technologies. The main objective of power operator is to facilitate the renewable power sources intergeration for maintaining an uninterrupted power supply. To achieve this objective, researchers have employed various Deep Learning methods of machine learning, such as RNN, LSTM, CNN and SV

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Sucita, Tasma, Dadang Lukman Hakim, Rizky Heryanto Hidayahtulloh, and Diki Fahrizal. "Solar irradiation intensity forecasting for solar panel power output analyze." Indonesian Journal of Electrical Engineering and Computer Science 36, no. 1 (2024): 74. http://dx.doi.org/10.11591/ijeecs.v36.i1.pp74-85.

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Accurate forecasting of global horizontal irradiance (GHI) is critical for optimizing solar power plant (SPP) output, particularly in tropical locales where solar potential is high yet underutilized due to forecasting challenges. This research aims to enhance GHI prediction in one of the major cities of Indonesia, where existing models struggle with the area’s natural climate unpredictability. Our analysis harnesses a decade of data 2011-2020, including GHI, temperature, and the Sky Insolation Clearness Index, to calibrate and compare these methodologies. We evaluate and contrast the exponenti

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Tasma, Sucita Dadang Lukman Hakim Rizky Heryanto Hidayahtulloh Diki Fahrizal. "Solar irradiation intensity forecasting for solar panel power output analyze." Indonesian Journal of Electrical Engineering and Computer Science 36, no. 1 (2024): 74–85. https://doi.org/10.11591/ijeecs.v36.i1.pp74-85.

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Accurate forecasting of global horizontal irradiance (GHI) is critical for optimizing solar power plant (SPP) output, particularly in tropical locales where solar potential is high yet underutilized due to forecasting challenges. This research aims to enhance GHI prediction in one of the major cities of Indonesia, where existing models struggle with the area’s natural climate unpredictability. Our analysis harnesses a decade of data 2011-2020, including GHI, temperature, and the Sky Insolation Clearness Index, to calibrate and compare these methodologies. We evaluate and contrast the exp

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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 (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 o

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Haupt, Sue Ellen, Branko Kosović, Tara Jensen, et al. "Building the Sun4Cast System: Improvements in Solar Power Forecasting." Bulletin of the American Meteorological Society 99, no. 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

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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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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 (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 si

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Pandžić, Franko, and Tomislav Capuder. "Advances in Short-Term Solar Forecasting: A Review and Benchmark of Machine Learning Methods and Relevant Data Sources." Energies 17, no. 1 (2023): 97. http://dx.doi.org/10.3390/en17010097.

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Solar forecasting is becoming increasingly important due to the exponential growth in total global solar capacity each year. More photovoltaic (PV) penetration in the grid poses problems for grid stability due to the inherent intermittent and variable nature of PV power production. Therefore, forecasting of solar quantities becomes increasingly important to grid operators and market participants. This review presents the most recent relevant studies focusing on short-term forecasting of solar irradiance and PV power production. Recent research has increasingly turned to machine learning to add

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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 (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.

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A, Sevuga Pandian, Deepali Virmani, Denslin Brabin D.R., and Sk Riyaz Hussain. "WHALE SWARM OPTIMIZATION BASED ANFIS FOR PREDICTION IN FORECASTING APPLICATION." ICTACT Journal on Soft Computing 14, no. 3 (2024): 3237–42. http://dx.doi.org/10.21917/ijsc.2024.0454.

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This paper proposes a novel approach for solar power forecasting using an Adaptive Neuro-Fuzzy Inference System (ANFIS) enhanced with Whale Swarm Optimization (WSO). The synergy between ANFIS and WSO aims to overcome the limitations of traditional forecasting models by controlling the collective intelligence of a whale-inspired swarm algorithm. The WSO optimizes the parameters of the ANFIS, leading to improved accuracy in solar power predictions. The integration of WSO introduces a parallelism and exploration-exploitation balance inspired by the natural behaviors of whales, enhancing the ANFIS

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Jogunuri, Sravankumar, F. T. Josh, J. Jency Joseph, R. Meenal, R. Mohan Das, and S. Kannadhasan. "Forecasting hourly short-term solar photovoltaic power using machine learning models." International Journal of Power Electronics and Drive Systems (IJPEDS) 15, no. 4 (2024): 2553. http://dx.doi.org/10.11591/ijpeds.v15.i4.pp2553-2569.

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Forecasting solar photovoltaic power ensures a stable and dependable power grid. Given its dependence on stochastic weather conditions, predicting solar photovoltaic power accurately demands applying intelligent and sophisticated techniques capable of handling its inherent nonlinearity and volatility. Controlling electrical energy sources is an important strategy for reaching this energy balance because grid operators often have no control over use patterns. Accurately forecasting photovoltaic (PV) power generation from highly integrated solar plants to the grid is essential for grid stability

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Jogunuri, Sravankumar, F. T. Josh, J. Jency Joseph, R. Meenal, R. Mohan Das, and S. Kannadhasan. "Forecasting hourly short-term solar photovoltaic power using machine learning models." International Journal of Power Electronics and Drive Systems (IJPEDS) 15, no. 4 (2024): 2553–69. https://doi.org/10.11591/ijpeds.v15.i4.pp2553-2569.

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Forecasting solar photovoltaic power ensures a stable and dependable power grid. Given its dependence on stochastic weather conditions, predicting solar photovoltaic power accurately demands applying intelligent and sophisticated techniques capable of handling its inherent nonlinearity and volatility. Controlling electrical energy sources is an important strategy for reaching this energy balance because grid operators often have no control over use patterns. Accurately forecasting photovoltaic (PV) power generation from highly integrated solar plants to the grid is essential for grid stability

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Sedai, Ashish, Rabin Dhakal, Shishir Gautam, et al. "Performance Analysis of Statistical, Machine Learning and Deep Learning Models in Long-Term Forecasting of Solar Power Production." Forecasting 5, no. 1 (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 e

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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 (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 differe

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