Thematische Bibliographien / Photovoltaic forecasting

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  1. Zeitschriftenartikel
  2. Dissertationen
  3. Bücher
  4. Buchteile
  5. Konferenzberichte
  6. Berichte der Organisationen

Auswahl der wissenschaftlichen Literatur zum Thema „Photovoltaic forecasting“

Autor: Grafiati
Veröffentlicht am 11. Januar 2025

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Zeitschriftenartikel zum Thema "Photovoltaic forecasting"

1

Fan, Yuanliang, Han Wu, Jianli Lin, Zewen Li, Lingfei Li, Xinghua Huang, Weiming Chen und Beibei Chen. „A distributed photovoltaic short-term power forecasting model based on lightweight AI for edge computing“. Journal of Physics: Conference Series 2876, Nr. 1 (01.11.2024): 012050. http://dx.doi.org/10.1088/1742-6596/2876/1/012050.

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Abstract In recent years, an immense amount of distributed photovoltaics are integrated into low-voltage distribution network, producing a substantial volume of operational data. The centralized cloud data center cannot process massive amounts of data precisely and promptly. Therefore, the operational status of distributed photovoltaic systems in low-voltage distribution network becomes difficult to predict. However, edge computing in the distribution network enables local data processing to improve the forecasting service’s real-time reliability. In this regard, this paper proposes a distributed photovoltaic short-term power forecasting model based on lightweight AI algorithms. Firstly, based on the Pearson correlation coefficient method, an analysis is conducted on the historical operational data in the network to extract important meteorological features correlated with the photovoltaic power output. Secondly, a distributed photovoltaic power forecasting model for the distribution network is constructed based on the Xception and attention mechanism. Finally, the model is trained using pruning, which removes redundant parts of the model, resulting in a compact and efficient forecasting model. By validating real-world datasets, the results demonstrate that the model presented in this article has a smaller size and higher forecasting accuracy than other state-of-the-art forecasting models.
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Yang, Shu-Xia, Yang Zhang und Xiao-Yu Cheng. „Economic modeling of distributed photovoltaic penetration considering subsidies and countywide promotion policy: An empirical study in Beijing“. Journal of Renewable and Sustainable Energy 14, Nr. 5 (September 2022): 055301. http://dx.doi.org/10.1063/5.0102574.

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Distributed photovoltaic power generation will not only help to achieve the strategic targets of peaking carbon emissions and carbon neutrality but also cause a series of impacts on the power grid at the same time. Forecasting the long-term development of regionally distributed photovoltaics can provide a reference for power grid planning and stable operation. In this paper, considering the effect of factors such as subsidies and countywide promotion policy of photovoltaics, a forecasting model for the development tendency of regionally distributed photovoltaics based on system dynamics is established. Then, taking Beijing as an example, an empirical analysis is carried out, and the effect of the proportion of self-consumption and the time when the subsidy is adjusted on distributed photovoltaic penetration is explored through sensitivity analysis. The simulation results show that the installed capacity achieved by the countywide promotion policy will become the main source of the installed capacity growth of distributed photovoltaics in Beijing after 2024. To continuously boost distributed photovoltaic penetration, relevant policymakers should consider the appropriate time when the subsidy is adjusted according to the installation cost of photovoltaic systems.
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Matushkin, Dmytro. „PHOTOVOLTAIC GENERATION FORECASTING MODELS: CONCEPTUAL ENSEMBLE ARCHITECTURES“. System Research in Energy 2024, Nr. 4 (29.11.2024): 56–64. https://doi.org/10.15407/srenergy2024.04.056.

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The decisions regarding power regulation, energy resource planning, and integrating “green” energy into the electrical grid hinge on precise probabilistic forecasts. One of the potential strategies to enhance forecast accuracy is the utilization of ensemble forecasting methods. They represent an approach where multiple models collaborate to achieve superior results compared to what a single model could produce independently. These methods can be categorized into two main categories: competitive and collaborative ensembles. Competitive ensembles harness the diversity of parameters and data to create a rich pool of base models. This approach may encompass statistical analysis, noise filtering, and anomaly elimination. On the other hand, collaborative ensembles rely on the interaction among models to achieve better outcomes. These methods encompass strategies such as weighted predictions, voting, aggregation, and a combination of model results. The research of ensemble forecasting methods in the context of photovoltaic generation is highly relevant, as solar energy represents a crucial source of renewable energy. Accurate predictions of solar energy production address the challenges related to the efficient utilization of photovoltaic panels and their integration into the overall energy system. This paper investigates conceptual ensemble architectures for photovoltaic energy forecasting. These architectures encompass various methods of aggregating base models within an ensemble, allowing for the consideration of different aspects and peculiarities of solar data, such as solar irradiation intensity, meteorological conditions, geographic factors, and more. These conceptual models are developed based on well-established statistical, machine learning, and artificial intelligence methods. Therefore, this paper provides an overview of ensemble forecasting methods for renewable energy, covering competitive and collaborative ensembles, as well as developing conceptual models for solar energy forecasting. This work aims to elevate the accuracy and efficiency of forecasts in the realm of renewable energy, representing a significant step in the advancement of sustainable and environmentally friendly energy production. Keywords: probabilistic solar forecasting, ensemble model, forecast combination, competitive ensembles, collaborative ensembles, conceptual models.
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El hendouzi, Abdelhakim, und Abdennaser Bourouhou. „Solar Photovoltaic Power Forecasting“. Journal of Electrical and Computer Engineering 2020 (31.12.2020): 1–21. http://dx.doi.org/10.1155/2020/8819925.

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The management of clean energy is usually the key for environmental, economic, and sustainable developments. In the meantime, the energy management system (EMS) ensures the clean energy which includes many sources grouped in a small power plant such as microgrid (MG). In this case, the forecasting methods are used for helping the EMS and allow the high efficiency to the clean energy. The aim of this review paper is providing the necessary data about the basic principles and standards of photovoltaic (PV) power forecasting by stating numerous research studies carried out on the PV power forecasting topic specifically in the short-term time horizon which is advantageous for the EMS and grid operator. At the same time, this contribution can offer a state of the art in different methods and approaches used for PV power forecasting along with a careful study of different time and spatial horizons. Furthermore, this current review paper can support the tenders in the PV power forecasting.
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Chin, Kho Lee. „A Case Study of Using Long Short-Term Memory (LSTM) Algorithm in Solar Photovoltaic Power Forecasting“. ASM Science Journal 18 (26.12.2023): 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 system. This study implements the deep learning method, long short-term memory (LSTM) models for time series forecasting in solar photovoltaic power generation forecasting. The data set collected by The Ravina Project from 2010 to 2014 is used as the training data in the simulations. The root mean square value is used in this study to measure the forecasting error. The results show that the deep learning algorithm provides reliable forecasting results.
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Antonanzas, J., N. Osorio, R. Escobar, R. Urraca, F. J. Martinez-de-Pison und F. Antonanzas-Torres. „Review of photovoltaic power forecasting“. Solar Energy 136 (Oktober 2016): 78–111. http://dx.doi.org/10.1016/j.solener.2016.06.069.

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

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Okhorzina, Alena, Alexey Yurchenko und 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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Xinhui, Du, Wang Shuai und Zhang Juan. „Research on Marine Photovoltaic Power Forecasting Based on Wavelet Transform and Echo State Network“. Polish Maritime Research 24, s2 (28.08.2017): 53–59. http://dx.doi.org/10.1515/pomr-2017-0064.

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Abstract With the rapid development of photovoltaic power generation technology, photovoltaic power generation system has gradually become an important component of the integrated energy system of marine. High precision short-term photovoltaic power generation forecasting is becoming one of the key technologies in ship energy saving and ship energy efficiency improving. Aiming at the characteristics of marine photovoltaic power generation system, we designed a highprecision power forecasting model (WT+ESN) for marine photovoltaic power generation system with anti-marine environmental interference. In this model, the information mining of the photovoltaic system in marine environment is carried out based on wavelet theory, then the forecasting model basing on echo state network is construct ed. Lastly, three kinds of error metrics are compared with the three traditional models by Matlab, the result shows that the model has high forecasting accuracy and strong robustness to marine environmental factors, which is of great significance to save fuel for ships, improve the energy utilization rate and assist the power dispatching and fuel dispatching of the marine power generation system.
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Wang, Yusen, Wenlong Liao und Yuqing Chang. „Gated Recurrent Unit Network-Based Short-Term Photovoltaic Forecasting“. Energies 11, Nr. 8 (18.08.2018): 2163. http://dx.doi.org/10.3390/en11082163.

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Photovoltaic power has great volatility and intermittency due to environmental factors. Forecasting photovoltaic power is of great significance to ensure the safe and economical operation of distribution network. This paper proposes a novel approach to forecast short-term photovoltaic power based on a gated recurrent unit (GRU) network. Firstly, the Pearson coefficient is used to extract the main features that affect photovoltaic power output at the next moment, and qualitatively analyze the relationship between the historical photovoltaic power and the future photovoltaic power output. Secondly, the K-means method is utilized to divide training sets into several groups based on the similarities of each feature, and then GRU network training is applied to each group. The output of each GRU network is averaged to obtain the photovoltaic power output at the next moment. The case study shows that the proposed approach can effectively consider the influence of features and historical photovoltaic power on the future photovoltaic power output, and has higher accuracy than the traditional methods.
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Dissertationen zum Thema "Photovoltaic forecasting"

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Swanepoel, Paul. „A forecasting model for photovoltaic module energy production“. Thesis, Nelson Mandela Metropolitan University, 2011. http://hdl.handle.net/10948/1420.

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Energy is of concern for governments and economies all over the world. As conventional methods of energy production are facing the prospect of depleting fossil fuel reserves, economies are facing energy risks. With this tension, various threats arise in terms of energy supply security. A shift from intensive fossil fuel consumption to alternative energy consumption combined with the calculated use of fossil fuels needs to be implemented. Using the energy radiated from the sun and converted to electricity through photovoltaic energy conversion is one of the alternative and renewable sources to address the limited fossil fuel dilemma. South Africa receives an abundance of sunlight irradiance, but limited knowledge of the implementation and possible energy yield of photovoltaic energy production in South Africa is available. Photovoltaic energy yield knowledge is vital in applications for farms, rural areas and remote transmitting devices where the construction of electricity grids are not cost effective. In this study various meteorological and energy parameters about photovoltaics were captured in Port Elizabeth (South Africa) and analyzed, with data being recorded every few seconds. A model for mean daily photovoltaic power output was developed and the relationships between the independent variables analyzed. A model was developed that can forecast mean daily photovoltaic power output using only temperature derived variables and time. The mean daily photovoltaic power model can then easily be used to forecast daily photovoltaic energy output using the number of sunlight seconds in a given day.
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Cormode, Daniel. „Large and Small Photovoltaic Powerplants“. Diss., The University of Arizona, 2015. http://hdl.handle.net/10150/556469.

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The installed base of photovoltaic power plants in the United States has roughly doubled every 1 to 2 years between 2008 and 2015. The primary economic drivers of this are government mandates for renewable power, falling prices for all PV system components, 3rd party ownership models, and a generous tariff scheme known as net-metering. Other drivers include a desire for decreasing the environmental impact of electricity generation and a desire for some degree of independence from the local electric utility. The result is that in coming years, PV power will move from being a minor niche to a mainstream source of energy. As additional PV power comes online this will create challenges for the electric grid operators. We examine some problems related to large scale adoption of PV power in the United States. We do this by first discussing questions of reliability and efficiency at the PV system level. We measure the output of a fleet of small PV systems installed at Tucson Electric Power, and we characterize the degradation of those PV systems over several years. We develop methods to predict energy output from PV systems and quantify the impact of negatives such as partial shading, inverter inefficiency and malfunction of bypass diodes. Later we characterize the variability from large PV systems, including fleets of geographically diverse utility scale power plants. We also consider the power and energy requirements needed to smooth those systems, both from the perspective of an individual system and as a fleet. Finally we report on experiments from a utility scale PV plus battery hybrid system deployed near Tucson, Arizona where we characterize the ability of this system to produce smoothly ramping power as well as production of ancillary energy services such as frequency response.
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Chowdhury, Badrul Hasan. „Irradiance forecasting and dispatching central station photovoltaic power plants“. Diss., Virginia Polytechnic Institute and State University, 1987. http://hdl.handle.net/10919/82903.

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This dissertation introduces a new operational tool for integrating a photovoltaic (PV) system into the utility's generation mix. It is recognized at the outset, that much of the existing research concentrated on the central PV system and its operations have concluded that technical problems in PV operation will override any value or credit that can be earned by a PV system, and that penetration of a PV plant in the utility will be severely limited. These are real problems and their solutions are sought in this dissertation. Judging from the drawbacks of the static approach, it is felt that a new approach or methodology needs to be developed which would give a central station PV plant its due share of credit. This dissertation deals mainly with the development and implementation of this new approach -- a dynamic rule-based dispatch algorithm which takes into account the problems faced by the dispatch operator during a dispatch interval and channels those into a knowledge base. The new dynamic dispatch requires forecasts of photovoltaic generations at the beginning of each dispatch interval. A Box-Jenkins time-series method is used to model the sub-hourly solar irradiance. The irradiance data at any specific site is stripped of its periodicities using a pre-whitening process which involves parameterization of certain known atmospheric phenomena. The pre-whitened data series is considered stationary, although some non-stationarity might be introduced by the discontinuities in the data collection during night hours. This model is extended to yield forecast equations which are then used to predict the photovoltaic output expected to occur at certain lead times coinciding with the economic dispatch intervals. A rule-based (RB) dispatch algorithm is developed in this dissertation. The RB is introduced to operate as a substitute for the dispatch operator. Some of the dispatcher's functions are routine jobs, while some require specialized knowledge or experience. The RB is given these two qualities through a number of rules. This algorithm works in tandem with a conventional economic dispatch algorithm. The functions of the two are coordinated by another algorithm which oversees the now of information and records them. The RB gives one of 16 possible solutions as and when required. These solutions are written as rules which manipulate the non-committable generation to achieve an optimal solution. The RB system during its operation supervises the fact that the PV generation are kept at the maximum level possible under all constraints. The case study revealed that the thermal generating units which are scheduled by the unit commitment are able to absorb most of the small to medium variations present in the PV generations. In cases of large variations during a single interval, the thermal generators reach their response limits before they can reach their maximum or minimum generation, thus causing mismatches in the load and generation. The mismatches are then picked up by the non-committable sources of generation, comprised of pumped storage units, hydro generation plant, or by interconnection tie-lines. If none of these are sufficient, changes are made in the PV generation schedule. It is concluded that results depend on the time of the year and the specific utility. The time of the year information is reflected in the load demand profile. Most utilities in the U.S. have single peaks in summer and double peaks in winter. Also, the time of the peak load occurrence varies with season. The utility generating capacity mix influences the results greatly.
Ph. D.
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Carriere, Thomas. „Towards seamless value-oriented forecasting and data-driven market valorisation of photovoltaic production“. Thesis, Université Paris sciences et lettres, 2020. http://www.theses.fr/2020UPSLM019.

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La décarbonation de la production d’électricité à échelle mondiale est un élément de réponse clé face aux pressions exercées par les différents enjeux environnementaux. Par ailleurs, la baisse des coûts de la filière photovoltaïque (PV) ouvre la voie à une augmentation significative de la production PV dans le monde. L’objectif principal de cette thèse est alors de maximiser le revenu d’un producteur d’énergie PV sous incertitude des prix de marché et de la production. Pour cela, un modèle de prévision probabiliste de la production PV à court (5 minutes) et moyen (24 heures) terme est proposé. Ce modèle est couplé à une méthode de participation au marché maximisant l’espérance du revenu. Dans un second temps, le couplage entre une centrale PV et une batterie est étudié, et une analyse de sensibilité des résultats est réalisée pour étudier la rentabilité et le dimensionnement de tels systèmes. Une méthode de participation alternative est proposée, pour lequel un réseau de neurones artificiel apprend à participer avec ou sans batterie au marché de l’électricité, ce qui permet de simplifier le processus de valorisation de l'énergie PV en diminuant le nombre de modèles requis
The decarbonation of electricity production on a global scale is a key element in responding to the pressures of different environmental issues. In addition, the decrease in the costs of the photovoltaic (PV) sector is paving the way for a significant increase in PV production worldwide. The main objective of this thesis is then to maximize the income of a PV energy producer under uncertainty of market prices and production. For this purpose, a probabilistic forecast model of short (5 minutes) and medium (24 hours) term PV production is proposed. This model is coupled with a market participation method that maximizes income expectation. In a second step, the coupling between a PV plant and a battery is studied, and a sensitivity analysis of the results is carried out to study the profitability and sizing of such systems. An alternative participation method is proposed, for which an artificial neural network learns to participate with or without batteries in the electricity market, thus simplifying the process of PV energy valuation by reducing the number of models required
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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 included. The benefits of cloud location sensing are examined using computer simulations to target important time-scales and options available to plant operators. Finally, the economics of advanced forecasting options will be examined in order to determine the benefit to plant operators.
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Nobre, André Maia. „Short-term solar irradiance forecasting and photovoltaic systems performance in a tropical climate in Singapore“. reponame:Repositório Institucional da UFSC, 2015. https://repositorio.ufsc.br/xmlui/handle/123456789/162682.

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Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia Civil, Florianópolis, 2015.
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A humanidade usou e continua consumindo em grande quantidade os recursos não-renováveis do planeta como petróleo, gás natural e carvão mineral para suprir suas necessidades energéticas. Somente nas últimas duas décadas que outras fontes de energia renováveis, como a solar fotovoltaica e a eólica, passaram a se tornar relevantes na geração de energia elétrica em nível mundial. Instalações de sistemas fotovoltaicos ao redor do mundo atingiram crescimento da ordem de 40% durante os últimos quinze anos. Entretanto, a grande maioria destes sistemas, (acima de 90%), estão localizados em regiões onde o recurso solar não é tão abundante, ou seja, fora da região dos trópicos do planeta. Devido a este fato, ao tentar incorporar a energia solar fotovoltaica às redes elétricas, uma pergunta que sempre surge está relacionada a variação desta forma de geração de energia elétrica com a produção alternante durante o dia devido ao movimento das nuvens e total ausência no período noturno. Mesmo assim, em alguns países, já se atinge percentuais em torno de 5 a 10% de contribuição da energia elétrica proveniente de energia solar fotovoltaica. Passa a ser desafiador a inserção dessa fonte de energia à rede, de maneira intensiva, em paralelo com os recursos já existentes (em sua maioria ainda de origem fóssil). Nesta tese, foi avaliada a previsão do recurso solar em curtíssimo prazo (como 15-min, 30-min e uma hora) para uma região tropical do planeta, neste caso em Cingapura, ilha que se localiza próxima à linha do equador, no Sudeste Asiático. Esta tese foca em métodos existentes de previsão de irradiância, mas também explora uma nova proposta híbrida, adaptada a uma localidade tropical. Além das previsões de irradiação solar, simulações de sistemas fotovoltaicos e o cálculo de seu desempenho foram estudados e avaliados de modo a se prever quanto de energia elétrica é produzida com a mesma antecedência dada nos produtos de previsão do recurso solar. A influência da gaze de queimada foi um fenômeno particular, comum na Cingapura de hoje, que afeta o desempenho de sistemas fotovoltaicos e que foi investigado em detalhe. Todo o trabalho foi validado por redes detalhadas de estações meteorológicas em solo e também através de monitoramento de sistemas fotovoltaicos por toda Cingapura.

Abstract : Humanity has used and continues to consume in great proportion non-renewable energy resources of the planet such as oil, natural gas and coal in order to fulfil its energy needs. It was only during the past two decades that other sources of renewable energy such as solar photovoltaics (PV) and wind energy became somewhat relevant towards electricity generation in the world. PV installations worldwide have reached a compound annual growth rate of ~40% for the last fifteen years. However, the great majority of these systems (over 90% of them) are located where the solar energy resource is not the most abundant - outside of the tropical regions of the planet. While trying to incorporate solar energy PV into electrical power grids, one common question which arises is related to the variable aspect of this form of energy generation - with alternating production during the day due to cloud motion, and total absence during night time. Nonetheless, in some countries, contribution ratios of 5 to 10% of electrical energy from solar PV have been achieved. It becomes then challenging to integrate this source of energy into grids in a professional way, in parallel with existing resources (mostly still fossil-fuel-based). In this thesis, short-term forecasting (for time horizons such as 15-min, 30-min and 1-hour) of the solar resource was investigated in a tropical region of the world - in Singapore, 1° North of the Equator, in Southeast Asia. This thesis focuses on existing methods for irradiance forecasting, but also explores a novel Hybrid proposal, tailored to the tropical environment at hand. Beyond the forecast of the solar energy irradiance ahead of time, PV system simulation and performance assessment were studied and evaluated with the goal of predicting how much electricity is produced in the same time frame given by the solar irradiance forecasting products. The influence of haze was a particular phenomenon, common in today?s Singapore, which affects PV system performance and which was investigated in detail. All work has been validated by a comprehensive network of ground-based meteorological stations, as well as by various PV system monitoring sites throughout Singapore.
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Nobre, André Maia. „Short-term solar irradiance forecasting and photovoltaic systems performance in a tropical climate in Singapore“. reponame:Repositório Institucional da UFSC, 2015. https://repositorio.ufsc.br/xmlui/handle/123456789/169480.

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Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia Civil, Florianópolis, 2015.
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A humanidade usou e continua consumindo em grande quantidade os recursos não-renováveis do planeta como petróleo, gás natural e carvão mineral para suprir suas necessidades energéticas. Somente nas últimas duas décadas que outras fontes de energia renováveis, como a solar fotovoltaica e a eólica, passaram a se tornar relevantes na geração de energia elétrica em nível mundial. Instalações de sistemas fotovoltaicos ao redor do mundo atingiram crescimento da ordem de 40% durante os últimos quinze anos. Entretanto, a grande maioria destes sistemas, (acima de 90%), estão localizados em regiões onde o recurso solar não é tão abundante, ou seja, fora da região dos trópicos do planeta. Devido a este fato, ao tentar incorporar a energia solar fotovoltaica às redes elétricas, uma pergunta que sempre surge está relacionada a variação desta forma de geração de energia elétrica com a produção alternante durante o dia devido ao movimento das nuvens e total ausência no período noturno. Mesmo assim, em alguns países, já se atinge percentuais em torno de 5 a 10% de contribuição da energia elétrica proveniente de energia solar fotovoltaica. Passa a ser desafiador a inserção dessa fonte de energia à rede, de maneira intensiva, em paralelo com os recursos já existentes (em sua maioria ainda de origem fóssil). Nesta tese, foi avaliada a previsão do recurso solar em curtíssimo prazo (como 15-min, 30-min e uma hora) para uma região tropical do planeta, neste caso em Cingapura, ilha que se localiza próxima à linha do equador, no Sudeste Asiático. Esta tese foca em métodos existentes de previsão de irradiância, mas também explora uma nova proposta híbrida, adaptada a uma localidade tropical. Além das previsões de irradiação solar, simulações de sistemas fotovoltaicos e o cálculo de seu desempenho foram estudados e avaliados de modo a se prever quanto de energia elétrica é produzida com a mesma antecedência dada nos produtos de previsão do recurso solar. A influência da gaze de queimada foi um fenômeno particular, comum na Cingapura de hoje, que afeta o desempenho de sistemas fotovoltaicos e que foi investigado em detalhe. Todo o trabalho foi validado por redes detalhadas de estações meteorológicas em solo e também através de monitoramento de sistemas fotovoltaicos por toda Cingapura.

Abstract : Humanity has used and continues to consume in great proportion non-renewable energy resources of the planet such as oil, natural gas and coal in order to fulfil its energy needs. It was only during the past two decades that other sources of renewable energy such as solar photovoltaics (PV) and wind energy became somewhat relevant towards electricity generation in the world. PV installations worldwide have reached a compound annual growth rate of ~40% for the last fifteen years. However, the great majority of these systems (over 90% of them) are located where the solar energy resource is not the most abundant - outside of the tropical regions of the planet. While trying to incorporate solar energy PV into electrical power grids, one common question which arises is related to the variable aspect of this form of energy generation - with alternating production during the day due to cloud motion, and total absence during night time. Nonetheless, in some countries, contribution ratios of 5 to 10% of electrical energy from solar PV have been achieved. It becomes then challenging to integrate this source of energy into grids in a professional way, in parallel with existing resources (mostly still fossil-fuel-based). In this thesis, short-term forecasting (for time horizons such as 15-min, 30-min and 1-hour) of the solar resource was investigated in a tropical region of the world - in Singapore, 1° North of the Equator, in Southeast Asia. This thesis focuses on existing methods for irradiance forecasting, but also explores a novel Hybrid proposal, tailored to the tropical environment at hand. Beyond the forecast of the solar energy irradiance ahead of time, PV system simulation and performance assessment were studied and evaluated with the goal of predicting how much electricity is produced in the same time frame given by the solar irradiance forecasting products. The influence of haze was a particular phenomenon, common in today?s Singapore, which affects PV system performance and which was investigated in detail. All work has been validated by a comprehensive network of ground-based meteorological stations, as well as by various PV system monitoring sites throughout Singapore.
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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
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 caso base (sin el sistema de control), la operación en deload de los parques implica una gran cantidad de energía que no se está aprovechando, lo que no se consideró en la metodología. Con tal de mejorar esto, el siguiente trabajo propone un control de mitigación de efecto nube en parques fotovoltaicos de gran escala basado en una herramienta de pronóstico de radiación. Esto último permite disminuir las pérdidas de energía junto con mitigar los efectos de la nubosidad parcial, mediante la determinación de un nivel de deload en los parques fotovoltaicos usando dicho pronóstico. En primer lugar, esta tesis presenta una revisión bibliográfica y discusión del estado del arte de las técnicas de pronóstico en parques fotovoltaicos. Se muestra que la selección de la técnica de pronóstico depende en la información disponible y la ventana de tiempo del pronóstico, es decir, dependerá del caso de estudio. Dicho esto, se propone el uso de una técnica de pronóstico basada en redes neuronales en el Sistema Interconectado del Norte Grande (SING) de Chile. El pronóstico sirve para determinar el nivel de deload en el parque fotovoltaico para los siguientes 10 minutos, en función de una rampa de radiación. Los resultados muestran que la implementación de la técnica de pronóstico no solo mejora la respuesta en frecuencia del sistema, sino que también disminuye las pérdidas energéticas de forma significativa.
Este trabajo fue parcialmente financiado por el Proyecto CONICYT/FONDAP/15110019 "Solar Energy Research Center" SERC-Chile y el Instituto de Sistemas Complejos de Ingeniería (ISCI)
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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 smart buildings in a Demand-Response framework, and estimation of photovoltaic generation forecasting models. In the first part of this thesis a novel Model Predictive Control approach for integrated management of HVAC, electrical and thermal storage, and photovoltaic generation in building is presented. The proposed methodology also considers participation of the building in a Demand-Response program, which allows the consumer to become an active player in the electricity system. The related optimization problems turn out to be computationally appealing, even uncertainty sources is also addressed by means of a two-step procedure. The second part deals with the problem of estimating photovoltaic generation forecasting models in scenarios where measurements of meteorological variables (i.e., solar irradiance and temperature) at the plant site are not available. This scenario is relevant to electricity network operation, when a large number of photovoltaic plants are deployed in the grid. In particular, two methods have been developed. The first approach makes use of raw cloud cover data provided by a weather service combined with power generation measurements to estimate the parameters of a novel class of models. The second approach is based on a set of tests performed on the generated power time series aimed at detecting data portions that were generated under clear sky conditions. These data are then used for fit the parameters of the PVUSA model to the theoretical clear sky irradiance. All the methods covered in this thesis have been extensively validated either using industry-standard simulation frameworks or via experiments performed on real data.
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Almquist, Isabelle, Ellen Lindblom und 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 irradiance are used to simulate the expected PV power generation. Data on driving distances are used to simulate hourly electricity demands of the EVs at the charging stations. A sensitivity analysis, based on PV irradiance that deviates from the forecast, is carried out. The results show what power the grid needs to have installed capacity for if no PV power system is installed. Furthermore, appropriate PV power installation sizes are suggested. The suggestions depend on whether the aim is to achieve 100 percent self-consumption of PV generated power or full PV power coverage of charging demands. For different scenarios, PV power installations appropriate for reducing peak powers on the grid are suggested. The sensitivity analysis highlights deviations caused by interference in solar irradiance.
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Bücher zum Thema "Photovoltaic forecasting"

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Nelson, Brent P. Potential of Photovoltaics. Washington, D.C: National Renewable Energy Laboratory, 2008.

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

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

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

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Ray, George, Bush Brian, National Renewable Energy Laboratory (U.S.) und Colorado Renewable Energy Conference (2009), Hrsg. Estimating solar PV output using modern space/time geostatistics. Golden, Colo.]: National Renewable Energy Laboratory, 2009.

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National Renewable Energy Laboratory (U.S.), Hrsg. Future of grid-tied PV business models: What will happen when PV penetration on the distribution grid is significant? : preprint. Golden, CO: National Renewable Energy Laboratory, 2008.

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

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Computational Intelligence for Modeling, Control, Optimization, Forecasting and Diagnostics in Photovoltaic Applications. MDPI, 2020. http://dx.doi.org/10.3390/books978-3-03943-201-1.

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Aghaei, Mohammadreza. Solar Radiation: Measurement, Modeling and Forecasting Techniques for Photovoltaic Solar Energy Applications. IntechOpen, 2022.

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Solar Radiation - Measurements, Modeling and Forecasting for Photovoltaic Solar Energy Applications [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.87671.

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Buchteile zum Thema "Photovoltaic forecasting"

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Khurana, Agrim, Ankit Dabas, Vaibhav Dhand, Rahul Kumar, Bhavnesh Kumar und Arjun Tyagi. „Solar Power Forecasting“. In Artificial Intelligence for Solar Photovoltaic Systems, 23–41. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003222286-2.

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Yang, Dazhi, und Jan Kleissl. „Data for Solar Forecasting“. In Solar Irradiance and Photovoltaic Power Forecasting, 169–220. Boca Raton: CRC Press, 2024. http://dx.doi.org/10.1201/9781003203971-6.

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Lakshmi, K., G. Sophia Jasmine und D. Magdalin Mary. „Optimization Modeling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants“. In Photovoltaic Systems, 105–21. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003202288-6.

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Yang, Dazhi, und Jan Kleissl. „Why We Do Solar Forecasting“. In Solar Irradiance and Photovoltaic Power Forecasting, 1–25. Boca Raton: CRC Press, 2024. http://dx.doi.org/10.1201/9781003203971-1.

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Yang, Dazhi, und Jan Kleissl. „Hierarchical Forecasting and Firm Power Delivery“. In Solar Irradiance and Photovoltaic Power Forecasting, 516–60. Boca Raton: CRC Press, 2024. http://dx.doi.org/10.1201/9781003203971-12.

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Yang, Dazhi, und Jan Kleissl. „Philosophical Thinking Tools“. In Solar Irradiance and Photovoltaic Power Forecasting, 26–49. Boca Raton: CRC Press, 2024. http://dx.doi.org/10.1201/9781003203971-2.

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Yang, Dazhi, und Jan Kleissl. „Solar Forecasting: The New Member of the Band“. In Solar Irradiance and Photovoltaic Power Forecasting, 83–128. Boca Raton: CRC Press, 2024. http://dx.doi.org/10.1201/9781003203971-4.

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Yang, Dazhi, und Jan Kleissl. „A Guide to Good Housekeeping“. In Solar Irradiance and Photovoltaic Power Forecasting, 129–68. Boca Raton: CRC Press, 2024. http://dx.doi.org/10.1201/9781003203971-5.

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Yang, Dazhi, und Jan Kleissl. „Probabilistic Forecast Verification“. In Solar Irradiance and Photovoltaic Power Forecasting, 399–438. Boca Raton: CRC Press, 2024. http://dx.doi.org/10.1201/9781003203971-10.

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Yang, Dazhi, und Jan Kleissl. „Deterministic Forecast Verification“. In Solar Irradiance and Photovoltaic Power Forecasting, 363–98. Boca Raton: CRC Press, 2024. http://dx.doi.org/10.1201/9781003203971-9.

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Konferenzberichte zum Thema "Photovoltaic forecasting"

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Wu, Linhan, Jizhou Yu, Yuxin Dai, Tianlu Gao und Jun Zhang. „Photovoltaic Power Generation Forecasting Based on TCN-Transformer Model“. In 2024 5th International Conference on Artificial Intelligence and Electromechanical Automation (AIEA), 620–26. IEEE, 2024. http://dx.doi.org/10.1109/aiea62095.2024.10692906.

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Córtez, Juan Carlos, Jose A. Cumbicos, Lucas Zenichi Terada, Juan Camilo Lopez, Mateus Giesbrecht, Gustavo Fraidenraich und Marcos J. Rider. „Fuzzy Ensemble Algorithm for Day-ahead Photovoltaic Power Forecasting“. In 2024 International Conference on Smart Energy Systems and Technologies (SEST), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/sest61601.2024.10694514.

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Hu, Hongtao, und Meng Yang. „Photovoltaic Power Load Forecasting Method Based on DBO-DELM“. In 2024 9th International Conference on Intelligent Computing and Signal Processing (ICSP), 1582–85. IEEE, 2024. http://dx.doi.org/10.1109/icsp62122.2024.10743625.

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Gao, Shenhong, Yaqi Wang, Wenxuan Wei und Tianci Ning. „Photovoltaic Forecasting with a Connected Multi-Structure Neural Network“. In 2024 4th Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS), 550–56. IEEE, 2024. http://dx.doi.org/10.1109/acctcs61748.2024.00103.

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Elsherbiny, Lamiaa, Ali Al-Alili und Saeed Alhassan. „Short Term Photovoltaic Power Forecasting“. In ASME 2021 15th International Conference on Energy Sustainability collocated with the ASME 2021 Heat Transfer Summer Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/es2021-63850.

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Abstract Due to the rapid increase of energy demand and the continuous decrease of renewable energy cost, photovoltaic (PV) installed capacity has increased significantly. The PV power output depends on the available solar irradiance and other meteorological data such as air temperature, wind speed, and relative humidity. The performance of PV panels also depends on the cleaning frequency and maintenance of these panels. Soiling is considered to be a key factor on PV performance in desert areas. The Middle East has one of the highest dust intensity in the world which results in dramatic PV power losses. Therefore, forecasting the power output of PV panels is essential for the development of smart grids and smart metering techniques. In this study, a hybrid Artificial Neural Network (ANN) is developed to forecast the performance of a PV panel. The hybrid ANN is trained on the local weather and solar data as well as different cleaning frequencies. Then, the performance of the hybrid-ANN is compared to that of a conventional ANN. The results are presented in terms of different statistical indices such as the root mean square error (RMSE) and the mean bias error (MBE). The results are used to find the optimal cleaning frequency required for the optimal PV performance.
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Caro, Eduardo, Francisco Javier Cara und Jesus Juan. „Forecasting photovoltaic energy using MEWMA models“. In 2015 12th International Conference on the European Energy Market (EEM). IEEE, 2015. http://dx.doi.org/10.1109/eem.2015.7216655.

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Li, Pengtao, Kaile Zhou und Shanlin Yang. „Photovoltaic Power Forecasting: Models and Methods“. In 2018 2nd IEEE Conference on Energy Internet and Energy System Integration (EI2). IEEE, 2018. http://dx.doi.org/10.1109/ei2.2018.8582674.

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Li, Jiaming, und John K. Ward. „Irradiance forecasting for the photovoltaic systems“. In 2014 6th International Conference on Modelling, Identification and Control (ICMIC). IEEE, 2014. http://dx.doi.org/10.1109/icmic.2014.7020778.

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„Photovoltaic Electrical Forecasting in South Algeria“. In International Conference on Artificial Intelligence, Energy and Manufacturing Engineering. International Institute of Engineers, 2014. http://dx.doi.org/10.15242/iie.e0614008.

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Sadowska, Gabriela. „Forecasting energy yield from photovoltaic installations“. In 2nd International PhD Student’s Conference at the University of Life Sciences in Lublin, Poland: ENVIRONMENT – PLANT – ANIMAL – PRODUCT. Publishing House of The University of Life Sciences in Lublin, 2023. http://dx.doi.org/10.24326/icdsupl2.e030.

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Berichte der Organisationen zum Thema "Photovoltaic forecasting"

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Prasanna, Ashreeta, und Sean Esterly. USAID Colombia Young Leaders Workforce Training Program Action Plans: Forecasting Distributed Photovoltaic Adoption in Barranquilla, Colombia. Office of Scientific and Technical Information (OSTI), Februar 2023. http://dx.doi.org/10.2172/1958614.

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