Gotowe bibliografie tematyczne / SOLAR POWER FORECASTING

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Gotowa bibliografia na temat „SOLAR POWER FORECASTING”

Autor: Grafiati

Data publikacji: 11 września 2023

Data aktualizacji: 25 lipca 2025

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Artykuły w czasopismach na temat "SOLAR POWER FORECASTING"

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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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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Rozprawy doktorskie na temat "SOLAR POWER FORECASTING"

Wang, Zheng. "Solar Power Forecasting." Thesis, The University of Sydney, 2019. https://hdl.handle.net/2123/21248.

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Solar energy is a promising environmentally-friendly energy source. Yet its variability affects negatively the large-scale integration into the electricity grid and therefore accurate forecasting of the power generated by PV systems is needed. The objective of this thesis is to explore the possibility of using machine learning methods to accurately predict solar power. We first explored the potential of instance-based methods and proposed two new methods: the data source weighted nearest neighbour (DWkNN) and the extended Pattern Sequence Forecasting (PSF) algorithms. DWkNN uses multiple dat

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Isaksson, Emil, and Conde Mikael Karpe. "Solar Power Forecasting with Machine Learning Techniques." Thesis, KTH, Matematisk statistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229065.

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The increased competitiveness of solar PV panels as a renewable energy source has increased the number of PV panel installations in recent years. In the meantime, higher availability of data and computational power have enabled machine learning algorithms to perform improved predictions. As the need to predict solar PV energy output is essential for many actors in the energy industry, machine learning and time series models can be employed towards this end. In this study, a comparison of different machine learning techniques and time series models is performed across five different sites in Sw

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Almquist, Isabelle, Ellen Lindblom, and Alfred Birging. "Workplace Electric Vehicle Solar Smart Charging based on Solar Irradiance Forecasting." Thesis, Uppsala universitet, Institutionen för teknikvetenskaper, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-323319.

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The purpose of this bachelor thesis is to investigate different outcomes of the usage of photovoltaic (PV) power for electric vehicle (EV) charging adjacent to workplaces. In the investigated case, EV charging stations are assumed to be connected to photovoltaic systems as well as the electricity grid. The model used to simulate different scenarios is based on a goal of achieving constant power exchange with the grid by adjusting EV charging to a solar irradiance forecast. The model is implemented in MATLAB. This enables multiple simulations for varying input parameters. Data on solar irradian

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Kim, Byungyu. "Solar Energy Generation Forecasting and Power Output Optimization of Utility Scale Solar Field." DigitalCommons@CalPoly, 2020. https://digitalcommons.calpoly.edu/theses/2149.

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The optimization of photovoltaic (PV) power generation system requires an accurate system performance model capable of validating the PV system optimization design. Currently, many commercial PV system modeling programs are available, but those programs are not able to model PV systems on a distorted ground level. Furthermore, they were not designed to optimize PV systems that are already installed. To solve these types of problems, this thesis proposes an optimization method using model simulations and a MATLAB-based PV system performance model. The optimization method is particularly designe

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D, Pepe. "New techniques for solar power forecasting and building energy management." Doctoral thesis, Università di Siena, 2019. http://hdl.handle.net/11365/1072873.

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The electrical grid can no longer be considered a unidirectional means of distributing energy from conventional plants to the final users, but a Smart Grid, where strong interaction between producers and users takes place. In this context, the importance of independent renewable generation is constantly increasing, and new tools are needed in order to reliably manage conventional power plant operation, grid balancing, real-time unit dispatching, demand constraints and energy market requirements. This dissertation is focused on two aspects of this general problem: cost-optimal management of sma

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Rudd, Timothy Robert. "BENEFITS OF NEAR-TERM CLOUD LOCATION FORECASTING FOR LARGE SOLAR PV." DigitalCommons@CalPoly, 2011. https://digitalcommons.calpoly.edu/theses/597.

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As the ‘green’ energy movement continues to gain momentum, photovoltaic generation is becoming an increasingly popular source for new power generation. The primary focus of this paper is to demonstrate the benefits of close-to real-time cloud sensing for Photovoltaic generation. In order to benefit from this close-to real-time data, a source of cloud cover information is necessary. This paper looks into the potential of point insolation sensors to determine overhead cloud coverage. A look into design considerations and economic challenges of implementing such a monitoring system is include

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van, der Meer Dennis. "Spatio-temporal probabilistic forecasting of solar power, electricity consumption and net load." Licentiate thesis, Uppsala universitet, Fasta tillståndets fysik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-363448.

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The increasing penetration of renewable energy sources into the electricity generating mix poses challenges to the operational performance of the power system. Similarly, the push for energy efficiency and demand response—i.e., when electricity consumers are encouraged to alter their demand depending by means of a price signal—introduces variability on the consumption side as well. Forecasting is generally viewed as a cost-efficient method to mitigate the adverse effects of the aforementioned energy transition because it enables a grid operator to reduce the operational risk by, e.g., unit-com

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Barbieri, Florian Benjamin Eric. "Random Finite Sets Based Very Short-Term Solar Power Forecasting Through Cloud Tracking." Thesis, Curtin University, 2019. http://hdl.handle.net/20.500.11937/77126.

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Tracking clouds with a sky camera within a very short horizon below thirty seconds can be a solution to mitigate the effects of sunlight disruptions. A Probability Hypothesis Density (PHD) filter and a Cardinalised Probability Hypothesis Density (CPHD) filter were used on a set of pre-processed sky images. Both filters have been compared with the state-of-the-art methods for performance. It was found that both filters are suitable to perform very-short term irradiance forecasting.

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Uppling, Hugo, and Adam Eriksson. "Single and multiple step forecasting of solar power production: applying and evaluating potential models." Thesis, Uppsala universitet, Institutionen för teknikvetenskaper, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-384340.

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The aim of this thesis is to apply and evaluate potential forecasting models for solar power production, based on data from a photovoltaic facility in Sala, Sweden. The thesis evaluates single step forecasting models as well as multiple step forecasting models, where the three compared models for single step forecasting are persistence, autoregressive integrated moving average (ARIMA) and ARIMAX. ARIMAX is an ARIMA model that also takes exogenous predictors in consideration. In this thesis the evaluated exogenous predictor is wind speed. The two compared multiple step models are multiple step

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Lorenzo, Antonio Tomas, and Antonio Tomas Lorenzo. "Short-Term Irradiance Forecasting Using an Irradiance Monitoring Network, Satellite Imagery, and Data Assimilation." Diss., The University of Arizona, 2017. http://hdl.handle.net/10150/624494.

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Solar and other renewable power sources are becoming an integral part of the electrical grid in the United States. In the Southwest US, solar and wind power plants already serve over 20% of the electrical load during the daytime on sunny days in the Spring. While solar power produces fewer emissions and has a lower carbon footprint than burning fossil fuels, solar power is only generated during the daytime and it is variable due to clouds blocking the sun. Electric utilities that are required to maintain a reliable electricity supply benefit from anticipating the schedule of power output from

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Książki na temat "SOLAR POWER FORECASTING"

United States. Bureau of Labor Statistics, ed. Careers in solar power. U.S. Bureau of Labor Statistics, 2011.

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National Renewable Energy Laboratory (U.S.) and International Workshop on the Integration of Solar Power into Power Systems (3rd : 2013 : London, England), eds. Metrics for evaluating the accuracy of solar power forecasting. National Renewable Energy Laboratory, 2013.

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Ray, George, Bush Brian, National Renewable Energy Laboratory (U.S.), and Colorado Renewable Energy Conference (2009), eds. Estimating solar PV output using modern space/time geostatistics. National Renewable Energy Laboratory, 2009.

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National Renewable Energy Laboratory (U.S.) and IEEE Photovoltaic Specialists Conference (37th : 2011 : Seattle, Wash.), eds. An economic analysis of photovoltaics versus traditional energy sources: Where are we now and where might we be in the near future? : preprint. National Renewable Energy Laboratory, 2011.

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Solar Energy Technologies Program (U.S.), National Renewable Energy Laboratory (U.S.), and IEEE Photovoltaic Specialists Conference (37th : 2011 : Seattle, Wash.), eds. An economic analysis of photovoltaics versus traditional energy sources: Where are we now and where might we be in the near future? National Renewable Energy Laboratory, U.S. Dept. of Energy, Office of Energy Efficienty and Renewable Energy, 2011.

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Paulescu, Marius. Weather Modeling and Forecasting of PV Systems Operation. Springer London, 2013.

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Lipták, Béla G. Post-oil energy technology: The world's first solar-hydrogen demonstration power plant. CRC Press, 2009.

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Nelson, Brent P. Potential of Photovoltaics. National Renewable Energy Laboratory, 2008.

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Liptak, Bela G. Post-oil energy technology: After the age of fossil fuels. Taylor & Francis, 2008.

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Solar Irradiance and Photovoltaic Power Forecasting. Taylor & Francis Group, 2024.

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Części książek na temat "SOLAR POWER FORECASTING"

Khurana, Agrim, Ankit Dabas, Vaibhav Dhand, Rahul Kumar, Bhavnesh Kumar, and Arjun Tyagi. "Solar Power Forecasting." In Artificial Intelligence for Solar Photovoltaic Systems. CRC Press, 2022. http://dx.doi.org/10.1201/9781003222286-2.

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Zack, John W. "Wind and Solar Forecasting." In Power Electronics and Power Systems. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55581-2_4.

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Yang, Dazhi, and Jan Kleissl. "Data for Solar Forecasting." In Solar Irradiance and Photovoltaic Power Forecasting. CRC Press, 2024. http://dx.doi.org/10.1201/9781003203971-6.

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Yang, Dazhi, and Jan Kleissl. "Why We Do Solar Forecasting." In Solar Irradiance and Photovoltaic Power Forecasting. CRC Press, 2024. http://dx.doi.org/10.1201/9781003203971-1.

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Yang, Dazhi, and Jan Kleissl. "Post-Processing Solar Forecasts." In Solar Irradiance and Photovoltaic Power Forecasting. CRC Press, 2024. http://dx.doi.org/10.1201/9781003203971-8.

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Wang, Zheng, Irena Koprinska, and Mashud Rana. "Solar Power Forecasting Using Pattern Sequences." In Artificial Neural Networks and Machine Learning – ICANN 2017. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68612-7_55.

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Syu, Jia-Hao, Chi-Fang Chao, and Mu-En Wu. "Forecasting System for Solar-Power Generation." In Recent Challenges in Intelligent Information and Database Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1685-3_6.

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Yang, Dazhi, and Jan Kleissl. "Hierarchical Forecasting and Firm Power Delivery." In Solar Irradiance and Photovoltaic Power Forecasting. CRC Press, 2024. http://dx.doi.org/10.1201/9781003203971-12.

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Yang, Dazhi, and Jan Kleissl. "Base Methods for Solar Forecast Generation." In Solar Irradiance and Photovoltaic Power Forecasting. CRC Press, 2024. http://dx.doi.org/10.1201/9781003203971-7.

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Yang, Dazhi, and Jan Kleissl. "Philosophical Thinking Tools." In Solar Irradiance and Photovoltaic Power Forecasting. CRC Press, 2024. http://dx.doi.org/10.1201/9781003203971-2.

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Streszczenia konferencji na temat "SOLAR POWER FORECASTING"

Jain, Natasha, and Dinesh Naik. "AI based Solar Power Forecasting." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10726181.

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Kumar, Madhav, Santanu Borgohain, Kaibalya Prasad Panda, Surmila Thokchom, and Gayadhar Panda. "Smart Solar Forecasting: Machine Learning Approaches for Predicting Solar Power." In TENCON 2024 - 2024 IEEE Region 10 Conference (TENCON). IEEE, 2024. https://doi.org/10.1109/tencon61640.2024.10902995.

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Amnuaypongsa, Worachit, Wijarn Wangdee, and Jitkomut Songsiri. "Probabilistic Solar Power Forecasting Using Multi-Objective Quantile Regression." In 2024 18th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS). IEEE, 2024. http://dx.doi.org/10.1109/pmaps61648.2024.10667174.

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Hassna, Ait-Ali, Mourchid Fatima, Kobbane Abdellatif, and El Koutbi Mohammed. "Federate learning for Solar Power Forecasting in smart cities." In GLOBECOM 2024 - 2024 IEEE Global Communications Conference. IEEE, 2024. https://doi.org/10.1109/globecom52923.2024.10901217.

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Melnyk, Mykhaylo, and Mykola Medykovsky. "Data Processing Methods in Solar Power Generation Forecasting Tasks." In 2024 IEEE 19th International Conference on Computer Science and Information Technologies (CSIT). IEEE, 2024. https://doi.org/10.1109/csit65290.2024.10982622.

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K, Subathra, Abinaya R, Mahalakshmi S, Arun G R, and Venkatapathi K. "Optimizing Solar Energy Forecasting: A Comparative Study of ARIMA." In 2024 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS). IEEE, 2024. https://doi.org/10.1109/icpects62210.2024.10780077.

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Jascourt, Stephen D., Daniel Kirk-Davidhoff, and Christopher Cassidy. "Forecasting Solar Power and Irradiance – Lessons from Real-World Experiences." In American Solar Energy Society National Solar Conference 2016. International Solar Energy Society, 2016. http://dx.doi.org/10.18086/solar.2016.01.15.

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Bacher, Peder, Henrik Madsen, and Bengt Perers. "Short-Term Solar Collector Power Forecasting." In ISES Solar World Congress 2011. International Solar Energy Society, 2011. http://dx.doi.org/10.18086/swc.2011.28.03.

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Lee, Jeong-In, Young-Mee Shin, Il-Woo Lee Energy, and Sang-Ha Kim. "Solar Power Generation Forecasting Service." In 2019 International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2019. http://dx.doi.org/10.1109/ictc46691.2019.8939757.

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Panamtash, Hossein, and Qun Zhou. "Coherent Probabilistic Solar Power Forecasting." In 2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS). IEEE, 2018. http://dx.doi.org/10.1109/pmaps.2018.8440483.

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Raporty organizacyjne na temat "SOLAR POWER FORECASTING"

Seema, Seema, Andreas Theocharis, and Andreas Kassler. Evaluate Temporal and Spatio-Temporal Correlations for Different Prosumers Using Solar Power Generation Time Series Dataset. Karlstad University, 2024. http://dx.doi.org/10.59217/yjll7238.

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This study investigates the temporal and spatio-temporal correlations of solar power generation among different prosumers of Uppsala and Halmstad, Sweden. Using solar power generation data from seven prosumer in Uppsala and five in Halmstad, we evaluate the correlation of solar power production generation at specific locations correlates with itself over different time lags (autocorrelation). In addition, we examine the spatiotemporal correlations of solar power production at various locations over a range of lags using time shifted cross correlation. These spatio-temporal correlations can fac

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Haupt, Sue Ellen. A Public-Private-Acadmic Partnership to Advance Solar Power Forecasting. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1408392.

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Marquis, Melinda, Stan Benjamin, Eric James, kathy Lantz, and Christine Molling. A Public-Private-Academic Partnership to Advance Solar Power Forecasting. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1422824.

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Thomas, Samuel, Elaine Baker, Kamal Aryal, et al. Towards Climate Resilient Agriculture in Nepal: Solutions for smallholder farmers. International Centre for Integrated Mountain Development (ICIMOD), 2024. https://doi.org/10.53055/icimod.1077.

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Nepal, one of the world’s most climate-vulnerable countries, is facing severe impacts from climate change, particularly in its agricultural sector, which employs two-thirds of the population and contributes more than a quarter of the nation’s GDP. Smallholder farmers, the backbone of this sector, are grappling with rising temperatures, erratic monsoon patterns, droughts, and increasingly frequent extreme weather events. Adapting to these challenges through Climate-Resilient Agriculture (CRA) is essential to ensuring food security and safeguarding the livelihoods of millions. CRA incorporates n

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