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Rozprawy doktorskie na temat „SOLAR POWER FORECASTING”

Autor: Grafiati

Data publikacji: 11 września 2023

Data aktualizacji: 25 lipca 2025

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1

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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8

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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10

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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11

Mayol, Cotapos Carolina de los Ángeles. "Mitigation control against partial shading effects in large-scale photovoltaic power plants using an improved forecasting technique." Tesis, Universidad de Chile, 2017. http://repositorio.uchile.cl/handle/2250/144113.

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Magíster en Ciencias de la Ingeniería, Mención Eléctrica<br>En un trabajo previo se propuso un control de mitigación de efecto nube que permitía disminuir los efectos nocivos de la nubosidad parcial sobre parques fotovoltaicos en la frecuencia de sistemas eléctricos de potencia. Esto último sin la necesidad del uso de acumuladores de energía. La estrategia se basa en la operación sub-óptima de los parques (operación en deload) con tal de disponer de reservas de potencia. A pesar que la implementación del sistema nombrado mejoró la frecuencia del sistema de forma significativa en comparación al

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De, Jong Pieter. "Forecasting, integration, and storage of renewable energy generation in the Northeast of Brazil." Escola Politécnica, 2017. http://repositorio.ufba.br/ri/handle/ri/24167.

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Submitted by Pieter de Jong (pieterj@ufba.br) on 2017-09-06T00:37:40Z No. of bitstreams: 1 DE JONG, Pieter - Forecasting, integration, and storage of renewable energy generation in the Northeast of Brazil.pdf: 4104557 bytes, checksum: 286729716f48be77c37ddb6f4cf2c93d (MD5)<br>Approved for entry into archive by Vanessa Reis (vanessa.jamile@ufba.br) on 2017-09-06T10:26:26Z (GMT) No. of bitstreams: 1 DE JONG, Pieter - Forecasting, integration, and storage of renewable energy generation in the Northeast of Brazil.pdf: 4104557 bytes, checksum: 286729716f48be77c37ddb6f4cf2c93d (MD5)<br>Made avail

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13

Ghosh, Shibani. "A Real-time Management of Distribution Voltage Fluctuations due to High Solar Photovoltaic (PV) Penetrations." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/74424.

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Due to the rapid growth of grid-tied solar photovoltaic (PV) systems in the generation mix, the distribution grid will face complex operational challenges. High PV penetration can create overvoltages and voltage fluctuations in the network, which are major concerns for the grid operator. Traditional voltage control devices like switched capacitor banks or line voltage regulators can alleviate slow-moving fluctuations, but these devices need to operate more frequently than usual when PV generation fluctuates due to fast cloud movements. Such frequent operations will impact the life expectancy o

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14

DAVO', Federica. "Optimization and Forecasting Models for Electricity Market and Renewable Energies." Doctoral thesis, Università degli studi di Bergamo, 2017. http://hdl.handle.net/10446/77349.

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This thesis presents different optimization and forecasting models, with the focus on energy markets and renewable energy sources. The analysis approach is related to models for wind and solar power forecasts and those for electricity prices forecasts. The first study explores a Principal Component Analysis in combination with two post-processing techniques for the prediction of wind power and of solar irradiance produced over two large areas. The Principal Component Analysis is applied to reduce the datasets dimension. A Neural Network and an Analog Ensemble post-processing are then applied o

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15

Lopes, Francisco Manuel Tavares. "Short-term forecasting for direct normal irradiance with numerical weather prediction models in Alentejo (Southern Portugal): implications for concentration solar energy technologies." Doctoral thesis, Universidade de Évora, 2020. http://hdl.handle.net/10174/28724.

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With the potential to sustain the world’s energy needs, solar energy plays a major role for the renewable energy transition. However, inherent problems exist in solar energy forecasting, a very important tool for power plant operators that allows an efficient energy management and dispatch operations in the electric grid. In particular, concentrating solar power (CSP) systems, which rely on direct normal irradiance (DNI) and its high variability, which links uncertainty to the electrical energy outputs of CSP plants. The main atmospheric factors that influence DNI variation at surface are clou

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16

Wang, Cyun-Siang, and 王群翔. "Forecasting Solar Power Generation by Machine Learning：Case of Longjing Solar Power Plant." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/adz3e9.

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LI, KUANG-WEI, and 李光偉. "Forecasting for short Term of Solar Power." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/jfw93n.

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碩士<br>正修科技大學<br>電機工程研究所<br>105<br>Following the hydroelectric power generation and thermal power generation, nuclear has nowadays become one of the major options for the electricity generation since been invented. Though the nuclear has been considered as the most effective solution for the electricity generation, it also bears the odium for the high risk of the mass destruction if any accident occurred. The tendency toward "Nuclear Free" or "Anti-Nuclear" has triggered a heated debate after the Fukushima nuclear accident in Japan. Taiwan, as an island country, has the extremely high needs

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18

Nunes, Rui Miguel da Cunha. "Big Data techniques for Solar Power Forecasting." Master's thesis, 2017. https://hdl.handle.net/10216/108315.

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Nunes, Rui Miguel da Cunha. "Big Data techniques for Solar Power Forecasting." Dissertação, 2017. https://repositorio-aberto.up.pt/handle/10216/108315.

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Couto, Rui Manuel Gonçalves do. "Improving solar power forecasting through advanced feature engineering." Master's thesis, 2020. https://hdl.handle.net/10216/132804.

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The production of solar energy is undoubtedly dependent on weather conditions and the time of day in which it occurs. Therefore, this source of energy being extremely variable and difficult to predict with high accuracy after many studies, the need for a new approach surges in order to increase it. With this project, it is proposed the use of images as input for deep learning structures with the purpose to achieve automatic feature extraction that can be replicated to other data sets. The images are generated from a grid of numerical weather predictions of, particularly, surface downelling sho

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Couto, Rui Manuel Gonçalves do. "Improving solar power forecasting through advanced feature engineering." Dissertação, 2020. https://hdl.handle.net/10216/132804.

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The production of solar energy is undoubtedly dependent on weather conditions and the time of day in which it occurs. Therefore, this source of energy being extremely variable and difficult to predict with high accuracy after many studies, the need for a new approach surges in order to increase it. With this project, it is proposed the use of images as input for deep learning structures with the purpose to achieve automatic feature extraction that can be replicated to other data sets. The images are generated from a grid of numerical weather predictions of, particularly, surface downelling sho

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Li, Chun-Wei, and 李俊緯. "Forecasting Solar Power Generation by LSTM Neural Networks." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/fx994p.

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碩士<br>大同大學<br>電機工程學系(所)<br>107<br>Solar photovoltaic (PV) generation has received great attention in recent years due to the promotion of green environment awareness. Accurate forecasting of solar power benefits the preparation for switching other renewable energies into the power grids when PV power becomes low. In particular, the harmful consequence from the large peak and off-peak gaps of the so-called “duck curve” for PV power can be mitigated. Motivated by the fact that the contingency reserve typically requires thirty to sixty minutes to start up, we mainly focus on the PV power predicti

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SINGH, SAMEEKSHA. "SOLAR POWER FORECASTING USING DIFFERENT MACHINE LEARNING TECHNIQUES." Thesis, 2022. http://dspace.dtu.ac.in:8080/jspui/handle/repository/19247.

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The Sun is an ultimate source of energy for all the objects living on Earth and solar radiation estimation is utmost important as this radiation serves as a primary energy source of conversion for the photovoltaic panels and the solar thermal power plants. This solar radiation is not constant in every region but it depends on various climatological parameters like temperature, wind-speed and many more, so there is intermittency in its behavior which results in changes in the electrical energy production. The above few statements reveal the necessity of predicting solar radiation. P

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Mu, Ko-Ming, and 穆格銘. "Using Back Propagation Neural Network Technology in Solar Power Forecasting." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/49725797765823694082.

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碩士<br>國立臺灣師範大學<br>工業教育學系<br>104<br>Because solar irradiance is susceptible to clouds and substances in the air, the solar photovoltaic cannot produce stable power output. The power output of a photovoltaic module is influenced immediately when the module is sheltered from the clouds. Besides, the material of solar cell, air temperature, module’s position and orientation also affect the power output of the photovoltaic module. The main goal of the thesis is to develop the solar power forecasting with 24 hours ahead by applying back-propagation neural network technology. Some different combinati

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MIAO, HE-CIAN, and 繆和謙. "Short Term Wind Power and Solar Power Forecasting Using Adaptive Neuro-Fuzzy Inference System." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/98429976626273220177.

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碩士<br>聖約翰科技大學<br>電機工程系碩士班<br>103<br>This thesis proposes an adaptive network-based fuzzy inference system (ANFIS) based forecasting method for short-term wind power and solar power forecasting. An accurate forecasting method for power generation of the wind energy conversion system (WECS) and the photovoltaic (PV) system is urgent needed under the relevant issues associated with the high penetration of wind and solar power in the electricity system. To demonstrate the effectiveness of the proposed forecasting method， the method is tested on the practical information of wind power generation of

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Hsieh, Yeu-Chen, and 謝雨辰. "Forecasting Solar Power Production by Heterogeneous Data Streams and Multitask Learning." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/373zyg.

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碩士<br>國立臺灣大學<br>資訊工程學研究所<br>105<br>In recent years, solar energy has become a significant field of research across the globe because of the growing demand for renewable energy and its promising potential in sustainability aspects. Therefore, with the increasing integration of photovoltaic (PV) systems to the electrical grid, reliable prediction of the expected production output of PV systems is gaining importance as a basis for management and operation strategies. However, power production of PV systems is highly variable due to its dependence on solar radiance, meteorological conditions and o

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Tsai, Sung-Yen, and 蔡松諺. "Research on Forecasting Solar Power Generation in Taiwan Utilizing Data Mining Methods." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/57651692415950047040.

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碩士<br>國立中興大學<br>資訊管理學系所<br>105<br>Renewable energy such as solar power is becoming increasingly important as the consumption of non-renewable energy, but how to decide on the location of the solar energy plant? Though we can make the decision from the measurement of some high-precision instruments, it costs a lot. Apart from that, we thus can use prediction model for judging whether a location is suitable for the development of solar energy. In this study, we use the solar energy data and the data of the Central Weather Bureau, and the data interval is from January 1, 2015 to December 29, 20

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Hanafi, Rois Ahmad, and 何羅伊. "An-Hour-Ahead Solar Power Forecasting Using Artificial Neural Networks in Taiwan." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/dtdmp3.

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碩士<br>國立臺灣大學<br>電機工程學研究所<br>107<br>The development plan of the PV power plant in Taiwan with high penetration makes the grid operator has to prepare the strategies to mitigate its intermittency behaviour. The power grid must be more flexible to receive the fluctuated power from PV. One of the economical ways is to conduct a solar power forecasting. The solar power forecasting is made using artificial neural networks in this thesis. The networks are trained using backpropagation and extreme learning machine. The extreme learning machine was used due to its advantages in accuracy and computation

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VARANASI, JYOTHI. "FORECASTING OF WIND AND SOLAR POWER GENERATIONS FOR ENHANCING THEIR PENETRATIONS IN SMART GRID." Thesis, 2020. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18098.

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The vanishing conventional energy sources and global warming drive the world for the power generation from renewable energy sources. The main renewable sources namely solar power and wind power are uncertain and intermittent in nature. Wind & solar photo voltaic (PV) power forecasting with good accuracy promise the power sector for large scale integrations of wind & solar PV power generations into the grid. In the context of smart grid and deregulated electricity market, price forecasting is a challenging job for researchers. A rigorous literature review of wind power forecasting, solar

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Huang, Chih-Chun, and 黃致鈞. "Application of Parallel Elman Neural Network to Hourly Solar Power Generation Estimation and Forecasting." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/j5zh7q.

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碩士<br>國立高雄應用科技大學<br>電機工程系博碩士班<br>106<br>Based on the existed solar power generation data in Taiwan, this thesis applies parallel Elman Neural Network associated with solar radiation and system conversion efficiency as parameters to construct a Taiwan solar energy forecasting model. The forecasting accuracy is verified by the information of photovoltaic power station with different regions and sizes. As well as the prediction model is estimated by K-means and inverse distance weighting skills to improve the solar power generation in various regions of Taiwan. The reliability of the estimation r

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Mpfumali, Phathutshedzo. "Probabilistic solar power forecasting using partially linear additive quantile regression models: an application to South African data." Diss., 2019. http://hdl.handle.net/11602/1349.

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MSc (Statistics)<br>Department of Statistics<br>This study discusses an application of partially linear additive quantile regression models in predicting medium-term global solar irradiance using data from Tellerie radiometric station in South Africa for the period August 2009 to April 2010. Variables are selected using a least absolute shrinkage and selection operator (Lasso) via hierarchical interactions and the parameters of the developed models are estimated using the Barrodale and Roberts's algorithm. The best models are selected based on the Akaike information criterion (AIC), Bay

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HONG, NENG-KAI, and 洪能凱. "Short-term Solar Power Forecasting Based on Machine Learning Analysis with Meteorological Data and Sun Path Variation." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/fxz6q8.

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碩士<br>國立雲林科技大學<br>資訊管理系<br>104<br>The rising importance of solar energy comes along with the attention of solar power forecasting. Accurate forecasting of solar power output is crucial to power distribution and plant management. This study aims to compare performances of different forecasting algorithms coming from machine learning. Comparisons also make on using multiple types of datasets, different sets of input features, different parameter settings, and different solar power plants. In particular, we investigate the influences between the meteorological factors and power outputs, sun path

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Poshtkouhi, Shahab. "Analysis and Implementation of Fine-grained Distributed Maximum Power Point Tracking in Photovoltaic Systems." Thesis, 2011. http://hdl.handle.net/1807/31391.

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This thesis deals with quantifying the merits of Distributed Maximum Power Point Tracking (DMPPT), as well as providing solutions to achieve DMPPT in PV systems. A general method based on 3D modeling is developed to determine the energy yield of PV installations exploiting different levels of DMPPT granularity. Sub-string-level DMPPT is shown to have up to 30% more annual energy yield than panel-level DMPPT. A Multi-Input-Single-Output (MISO) dc-dc converter is proposed to achieve DMPPT in parallel-connected applications. A digital current-mode controller is used to operate the MISO converter

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SINGH, UPMA. "MODELLING AND OPTIMIZATION OF HYBRID RENEWABLE ENERGY SYSTEMS AND APPLICATIONS." Thesis, 2023. http://dspace.dtu.ac.in:8080/jspui/handle/repository/20066.

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In the last few years, several countries have accomplished their determined renewable energy targets to achieve their future energy requirements with the foremost aim to encourage sustainable growth with reduced emissions, mainly through the implementation of wind and solar energy. Wind and solar energy is critically important for the social and economic growth of any country. Moreover, reliable and precise wind and solar power prediction is crucial for the dispatch, unit commitment, and stable functioning of power systems. This makes it easier for grid operators of the power system

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