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Tesis sobre el tema "Wind power prediction"

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Publicado: 4 de junio de 2021
Última modificación: 26 de julio de 2025

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1

Vlasova, Julija. "Spatio-temporal analysis of wind power prediction errors." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2007. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2007~D_20070816_142259-79654.

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Nowadays there is no need to convince anyone about the necessity of renewable energy. One of the most promising ways to obtain it is the wind power. Countries like Denmark, Germany or Spain proved that, while professionally managed, it can cover a substantial part of the overall energy demand. One of the main and specific problems related to the wind power management — development of the accurate power prediction models. Nowadays State-Of-Art systems provide predictions for a single wind turbine, wind farm or a group of them. However, the spatio-temporal propagation of the errors is not adequa
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2

Cutler, Nicholas Jeffrey Electrical Engineering &amp Telecommunications Faculty of Engineering UNSW. "Characterising the uncertainty in potential large rapid changes in wind power generation." Publisher:University of New South Wales. Electrical Engineering & Telecommunications, 2009. http://handle.unsw.edu.au/1959.4/43570.

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Wind energy forecasting can facilitate wind energy integration into a power system. In particular, the management of power system security would benefit from forecast information on plausible large, rapid change in wind power generation. Numerical Weather Prediction (NWP) systems are presently the best available tools for wind energy forecasting for projection times between 3 and 48 hours. In this thesis, the types of weather phenomena that cause large, rapid changes in wind power in southeast Australia are classified using observations from three wind farms. The results show that the majority
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3

Clemow, Philip R. "Smoothing wind farm output power through co-ordinated control and short term wind speed prediction." Thesis, Imperial College London, 2012. http://hdl.handle.net/10044/1/9504.

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In recent years the energy sector has looked to renewables as a means to reduce emissions. Wind power is able to provide large amounts of energy at a reasonable cost from presently available products. Thus the amount of wind generation has risen steeply in recent years, notably in the countries of northern Europe. However, this rise in wind power has lead to issues regarding the variability of the wind power output. Wind power is related to the wind speed, which varies greatly. This variability can cause issues with wind operators' ability to participate in electricity markets and can also lea
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4

Sakthi, Gireesh. "WIND POWER PREDICTION MODEL BASED ON PUBLICLY AVAILABLE DATA: SENSITIVITY ANALYSIS ON ROUGHNESS AND PRODUCTION TREND." Thesis, Uppsala universitet, Institutionen för geovetenskaper, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-400462.

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The wind power prediction plays a vital role in a wind power project both during the planning and operational phase of a project. A time series based wind power prediction model is introduced and the simulations are run for different case studies. The prediction model works based on the input from 1) nearby representative wind measuring station 2) Global average wind speed value from Meteorological Institute Uppsala University mesoscale model (MIUU) 3) Power curve of the wind turbine. The measured wind data is normalized to minimize the variation in the wind speed and multiplied with the MIUU
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5

Werngren, Simon. "Comparison of different machine learning models for wind turbine power predictions." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-362332.

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The goal of this project is to compare different machine learning algorithms ability to predict wind power output 48 hours in advance from earlier power data and meteorological wind speed predictions. Three different models were tested, two autoregressive integrated moving average (ARIMA) models one with exogenous regressors one without and one simple LSTM neural net model. It was found that the ARIMA model with exogenous regressors was the most accurate while also beingrelatively easy to interpret and at 1h 45min 32s had a comparatively short training time. The LSTM was less accurate, harder
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6

Heinermann, Justin Philipp [Verfasser], Oliver [Akademischer Betreuer] Kramer, and Jörg [Akademischer Betreuer] Lässig. "Wind Power Prediction with Machine Learning Ensembles / Justin Philipp Heinermann ; Oliver Kramer, Jörg Lässig." Oldenburg : BIS der Universität Oldenburg, 2016. http://d-nb.info/1122481861/34.

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7

Heinermann, Justin Philipp Verfasser], Oliver [Akademischer Betreuer] [Kramer, and Jörg [Akademischer Betreuer] Lässig. "Wind Power Prediction with Machine Learning Ensembles / Justin Philipp Heinermann ; Oliver Kramer, Jörg Lässig." Oldenburg : BIS der Universität Oldenburg, 2016. http://d-nb.info/1122481861/34.

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8

Åkerberg, Ludvig. "Using Unsupervised Machine Learning for Outlier Detection in Data to Improve Wind Power Production Prediction." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-200336.

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The expansion of wind power for electrical energy production has increased in recent years and shows no signs of slowing down. This unpredictable source of energy has contributed to destabilization of the electrical grid causing the energy market prices to vary significantly on a daily basis. For energy producers and consumers to make good investments, methods have been developed to make predictions of wind power production. These methods are often based on machine learning were historical weather prognosis and wind power production data is used. However, the data often contain outliers, causi
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9

Pfeifer, Mark B. "A hybrid approach to forecasting wind power using Artificial Neural Networks and Numeric Weather Prediction." Thesis, Wichita State University, 2011. http://hdl.handle.net/10057/5031.

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A methodology to forecast wind power production 24 hours ahead is developed using a hybrid approach of an artificial neural network (ANN) and numerical weather prediction (NWP). The methodology is simple and designed to be applicable to any wind farm on the globe, using publicly available NWP data and basic historical power production data from wind farm. Notably, no historical wind data from on-farm sensors is required as the 0 hour forecast data is used to train the ANN. The results are encouraging, with a root-mean-square-error of 0.2267 for a 24 hour ahead forecast, corresponding to a fore
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10

Sugathan, Aromal, and Sean Gregory. "Analysis of AEP prediction against production data of commercial wind turbines in Sweden." Thesis, Högskolan i Halmstad, Akademin för företagande, innovation och hållbarhet, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-44527.

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Based on data from 2083 wind turbines installed in Sweden since 1988, the annual energy production (AEP) predictions considered at the project planning phases of the wind turbines in Sweden have been compared to the wind-index-corrected production data. The production data and the predicted AEP data are taken from Vindstat, a database that collects information directly from wind turbine owners in Sweden. The mean error for all analyzed wind turbines was 11.9%,which means that, overall, the predicted AEP has been overestimated. There has been improved accuracy with time and error in prediction
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11

Li, Wenyan. "Predictive engineering in wind energy: a data-mining approach." Thesis, University of Iowa, 2009. https://ir.uiowa.edu/etd/399.

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The large-scale wind energy industry is relatively new and is rapidly expanding. The ability of a wind turbine to extract power from the wind is a function of three main factors: the measured wind speed, the power curve of the turbine, and the ability of the machine to handle wind fluctuations. The key parameter determining wind turbine performance is wind speed and it is normally measured with an anemometer placed at the nacelle of a turbine. The dynamic nature of wind speed, however, is a barrier for applying predictive engineering in wind energy. Traditional approaches based on physical sci
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12

Bolouri, Afshar Banafsheh. "Wind power forecasting using artificial neural networks with numerical prediction : a case study for mountainous Canada." Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/58978.

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Wind is a free and easily available source of energy. Several countries, including Canada, have already incorporated wind power into their electricity supply system. Forecasting wind power production is quite challenging because the wind is variable and depends on weather conditions, terrain factors and turbine height. In addition to traditional physical and statistical methods, some advanced methods based on artificial intelligence have been investigated in recent years to achieve more reliable wind-power forecasts. The aim of this work is to exploit the ability of artificial neural network (
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13

Martínez-Arellano, G. "Forecasting wind power for the day-ahead market using numerical weather prediction models and computational intelligence techniques." Thesis, Nottingham Trent University, 2015. http://irep.ntu.ac.uk/id/eprint/322/.

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Wind power forecasting is essential for the integration of large amounts of wind power into the electric grid, especially during large rapid changes of wind generation. These changes, known as ramp events, may cause instability in the power grid. Therefore, detailed information of future ramp events could potentially improve the backup allocation process during the Day Ahead (DA) market (12 to 36 hours before the actual operation), allowing the reduction of resources needed, costs and environmental impact. It is well established in the literature that meteorological models are necessary when f
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14

Stamp, Alexander. "The relationship between weather forecasts and observations for predicting electricity output from wind turbines." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-215651.

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Wind power production is of growing importance to many countries around the world. To improve reliability and power grid stability related to wind power, forecasting of wind power is becoming an important commercial and research area. Machine learning methods are considered to be highly valuable when making predictions on time series data and as such have become prominent within wind forecasting as well. This thesis extends an existing neural network prediction system with new input data series, in particular the observed wind speed from the wind farm itself. The goal was to investigate the ef
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15

Mohammadi, Mohammad Mehdi. "PREDICTION OF WIND TURBINE BLADE FATIGUE LOADS USING FEED-FORWARD NEURAL NETWORKS." Thesis, Uppsala universitet, Institutionen för geovetenskaper, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-444115.

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In recent years, machine learning applications have gained great attention in the wind power industry. Among these, artificial neural networks have been utilized to predict the fatigue loads of wind turbine components such as rotor blades. However, the limited number of contributions and differences in the used databases give rise to several questions which this study has aimed to answer. Therefore, in this study, 5-min SCADA data from the Lillgrund wind farm has been used to train two feed-forward neural networks to predict the fatigue loads at the blade root in flapwise and edgewise directio
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16

YILDIRIM, NURSEDA. "TIME SERIES MODELLING FOR WIND POWER PREDICTION AND CONTROL : CLUSTERING AND ASSOCIATION RULES OF DATA MINING FOR CFD AND TIME SERIES DATA OF POWER RAMPS." Thesis, Uppsala universitet, Institutionen för geovetenskaper, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-245304.

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17

Diniz, Marco Antonio Bezerra. "Desempenho AerodinÃmico de uma Turbina EÃlica em Escala, Perfil NREL S809, com Diferentes Velocidades EspecÃficas de Projeto." Universidade Federal do CearÃ, 2014. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=11527.

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CoordenaÃÃo de AperfeiÃoamento de NÃvel Superior<br>A constÃncia dos ventos brasileiros, e a necessidade de amenizar a demanda das grandes cidades encontraram na energia eÃlica uma forte parceira. Como uma alternativa na produÃÃo de energia elÃtrica, condomÃnios e prÃdios modernos, alÃm de algumas aplicaÃÃes rurais, tÃm recorrido Ãs turbinas eÃlicas de pequeno porte como uma alternativa para sanar suas necessidades. Contudo, a maioria da tecnologia encontrada no mercado à importada e nÃo foi desenvolvida exclusivamente para aplicaÃÃes no Brasil. A ferramenta mais importante na aerodinÃmica exp
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18

Brka, Adel. "Optimisation of stand-alone hydrogen-based renewable energy systems using intelligent techniques." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2015. https://ro.ecu.edu.au/theses/1756.

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Wind and solar irradiance are promising renewable alternatives to fossil fuels due to their availability and topological advantages for local power generation. However, their intermittent and unpredictable nature limits their integration into energy markets. Fortunately, these disadvantages can be partially overcome by using them in combination with energy storage and back-up units. However, the increased complexity of such systems relative to single energy systems makes an optimal sizing method and appropriate Power Management Strategy (PMS) research priorities. This thesis contributes to the
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19

Ak, Ronay. "Neural Network Modeling for Prediction under Uncertainty in Energy System Applications." Thesis, Supélec, 2014. http://www.theses.fr/2014SUPL0015/document.

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Cette thèse s’intéresse à la problématique de la prédiction dans le cadre du design de systèmes énergétiques et des problèmes d’opération, et en particulier, à l’évaluation de l’adéquation de systèmes de production d’énergie renouvelables. L’objectif général est de développer une approche empirique pour générer des prédictions avec les incertitudes associées. En ce qui concerne cette direction de la recherche, une approche non paramétrique et empirique pour estimer les intervalles de prédiction (PIs) basés sur les réseaux de neurones (NNs) a été développée, quantifiant l’incertitude dans les p
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20

Lange, Matthias. "Analysis of the uncertainty of wind power predictions." [S.l. : s.n.], 2003. http://deposit.ddb.de/cgi-bin/dokserv?idn=969985789.

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21

Li, Wenyan Kusiak Andrew. "Predictive engineering in wind energy a data-mining approach /." [Iowa City, Iowa] : University of Iowa, 2009. http://ir.uiowa.edu/etd/399.

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22

Wang, Ye. "Evaluation de la Performance des Réglages de Fréquence des Eoliennes à l’Echelle du Système Electrique : Application à un Cas Insulaire." Thesis, Ecole centrale de Lille, 2012. http://www.theses.fr/2012ECLI0027/document.

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L’intégration croissante de la production éolienne ne participant pas au réglage de fréquence induit de nouvelles difficultés de gestion des systèmes électriques. Ces problèmes sont d’autant plus significatifs que le réseau est faible. La présente thèse vise à évaluer la performance et la fiabilité du réglage de fréquence des éoliennes à l’échelle du système électrique. Les études sont appliquées sur un réseau insulaire.D’abord, l’impact d’un fort taux de pénétration de la production éolienne sur l’allocation de la réserve primaire et sur le comportement dynamique du réseau est caractérisé. Il
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23

Robb, David MacKenzie. "Model based predictive control with application to renewable energy systems." Thesis, University of Strathclyde, 2000. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=20379.

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In the promotion and development of renewable energy systems, control engineering is one area which can directly affect the overall system performance and economics and thus help to make renewable energies more attractive and popular. For cost effectiveness, ideally the renewable energy industry requires a control design technique which is very effective yet simple with methods that are transparent enough to allow implementation by non-control engineers. The objective of this thesis is to determine if Model Based Predictive Control (MBPC) is a suitable control technique for use by the renewabl
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24

Badran, Ahmad. "Integrating Wind Power into The Electric Grid : Predictive Current Control Implementation." Thesis, Linnéuniversitetet, Institutionen för fysik och elektroteknik (IFE), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-99640.

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The increasing penetration of wind power into the power system dominated by variable-speed wind turbines among the installed wind turbines will require further development of control methods. Power electronic converters are widely used to improve power quality in conjunction with the integration of variable speed wind turbines into the grid. In this thesis, a detailed model of the Predictive Current Control (PCC) method will be descripts for the purpose of control of the grid-connected converter. The injected active and reactive power to the grid will be controlled to track their reference val
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25

LABELLA, ALESSANDRO GIUSEPPE. "Advanced Primary Controllers for Inverter Based Power Sources: Microgrids and Wind Power Plants." Doctoral thesis, Università degli studi di Genova, 2020. http://hdl.handle.net/11567/1009797.

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The aim of this doctoral thesis is to present the research activity fulfilled during the Ph.D. studies. The research project of the candidate was focused on two main cores. The first core is centred in the microgrid area; in particular in islanded microgrid modelling and control. Firstly, the model was compared with experimental results collected in some facilities available at University of Genoa. Then traditional controllers for islanded microgrid are analysed and explored, proposing a new stability estimation procedure for droop controlled microgrid. Finally, a new control strategy based
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26

Zastrau, David [Verfasser]. "Estimation of Uncertainty of Wind Energy Predictions : With Application to Weather Routing and Wind Power Generation / David Zastrau." Frankfurt a.M. : Peter Lang GmbH, Internationaler Verlag der Wissenschaften, 2017. http://d-nb.info/1127484524/34.

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27

Dione, Mamadou. "Prévision court terme de la production éolienne par Machine learning." Electronic Thesis or Diss., Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAG004.

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La loi de transition énergétique votée par l’Etat français a des implications précises sur les énergies renouvelables, en particulier sur leur mécanisme de rémunération. Jusqu’en 2015, un contrat d’obligation d’achat permettait de vendre l’électricité d’origine éolienne à un tarif fixe. A partir de 2015 certains parcs éoliens ont commencé à sortir de l’obligation d’achat. En effet, l’énergie éolienne commence à être directement vendue sur le marché par les producteurs à cause de la rupture des contrats d’obligation d’achat. Les gestionnaires de réseaux de distribution et les gestionnaires de r
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28

Keller, William. "Wind farm noise impact in France: A proposition of acoustic model improvements for predicting energy production." Thesis, KTH, Energiteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-152229.

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Despite all environmental and economic advantages of wind power, noise emission remains an issue for population acceptance. In France, the current noise emission regulation defines noise emergence level thresholds, leading to wind turbine curtailment. Great energy generation losses and thus lost revenues are at stake. This master thesis presents current acoustic campaigns conducted for the development of a wind power project in France and proposes acoustic model improvements to predict curtailment losses before the construction of the wind farm. It first gives insights about the French wind po
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29

Lledó, Ponsatí Llorenç. "Climate variability predictions for the wind energy industry: a climate services perspective." Doctoral thesis, Universitat de Barcelona, 2020. http://hdl.handle.net/10803/670882.

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In order to mitigate the climate change effects, the world is undergoing an energy transition from polluting sources towards renewable energies. This transition is turning the electricity system more dependent on atmospheric conditions and more prone to suffer the effects of climate variability. The atmospheric circulation is changing in certain aspects due to increasing concentrations of greenhouse gases in the atmosphere, but it also varies from year to year due to natural variability processes occurring in the Earth system at timescales of weeks, months and years. The atmosphere intera
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30

Evans, Martin A. "Multiplicative robust and stochastic MPC with application to wind turbine control." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:0ad9b878-00f3-4cfa-a683-148765e3ae39.

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A robust model predictive control algorithm is presented that explicitly handles multiplicative, or parametric, uncertainty in linear discrete models over a finite horizon. The uncertainty in the predicted future states and inputs is bounded by polytopes. The computational cost of running the controller is reduced by calculating matrices offline that provide a means to construct outer approximations to robust constraints to be applied online. The robust algorithm is extended to problems of uncertain models with an allowed probability of violation of constraints. The probabilistic degrees of sa
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31

Siniscalchi, Minna Sara. "Advanced wind farm control strategies for enhancing grid support." Doctoral thesis, TDX (Tesis Doctorals en Xarxa), 2019. http://hdl.handle.net/10803/669244.

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Nowadays, there is rising concern among Transmission System Operators about the declining of system inertia due to the increasing penetration of wind energy, and other renewable energy systems, and the retirements of conventional power plants. On the other hand, by properly operating wind farms, wind generation may be capable of enhancing grid stability and ensuring continued security of power supply. In this doctoral thesis, new control approaches for designing wind farm optimization-based control strategies are proposed to improve the participation of wind farms in grid support, specially in
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32

Saunders, Daniel Curtis. "Wind Turbine Wake Interactions - Characterization of Unsteady Blade Forces and the Role of Wake Interactions in Power Variability Control." ScholarWorks @ UVM, 2017. http://scholarworks.uvm.edu/graddis/745.

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Growing concerns about the environmental impact of fossil fuel energy and improvements in both the cost and performance of wind turbine technologies has spurred a sharp expansion in wind energy generation. However, both the increasing size of wind farms and the increased contribution of wind energy to the overall electricity generation market has created new challenges. As wind farms grow in size and power density, the aerodynamic wake interactions that occur between neighboring turbines become increasingly important in characterizing the unsteady turbine loads and power output of the farm. Tu
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33

Heller, Ondřej. "Akumulace energie z OZE." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2010. http://www.nusl.cz/ntk/nusl-218539.

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The objective of the first part of master’s thesis is mapping the potential of various types of renewable sources in Europe and Czech Republic, especially solar energy, wind energy, water energy and biomass. There are described principals and ways of energy generation from these sources, brief overview of current technologies, and also their advantages and limitations. An important part is electric supply continuity from renewable sources, there are large differences and the resulting to restrictions on construction and connecting the units to the power system. In this work there are mentioned
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34

Nami, Alireza. "A new multilevel converter configuration for high power and high quality applications." Thesis, Queensland University of Technology, 2010. https://eprints.qut.edu.au/33216/1/Alireza_Nami_Thesis.pdf.

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The Queensland University of Technology (QUT) allows the presentation of theses for the Degree of Doctor of Philosophy in the format of published or submitted papers, where such papers have been published, accepted or submitted during the period of candidature. This thesis is composed of ten published /submitted papers and book chapters of which nine have been published and one is under review. This project is financially supported by an Australian Research Council (ARC) Discovery Grant with the aim of investigating multilevel topologies for high quality and high power applications, with speci
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35

Riachy, Léa. "Contribution à la commande d'un onduleur multiniveaux, destinée aux énergies renouvelables, en vue de réduire le déséquilibre dans les réseaux électriques." Thesis, Normandie, 2017. http://www.theses.fr/2017NORMR076/document.

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Le travail de cette thèse apporte une contribution aux méthodes de réglage de la tension dans les réseaux électriques. Il s’agit de fournir au réseau la puissance active et surtout la puissance réactive nécessaire pour réguler la tension et aboutir à un système équilibré vue du côté source. Ces puissances sont extraites d’une source d’energie renouvelable : une attention particulière a été portée à l’énergie éolienne raccordée au réseau à travers la Machine Asynchrone à Double Alimentation (MADA) pilotée par des convertisseurs statiques. Le système de contrôle le plus répandu des éoliennes est
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36

Lee, Ke-Chu, and 李可掬. "A Wake Model for Wind Turbine Power Prediction." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/8w5evy.

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碩士<br>國立臺灣大學<br>工程科學及海洋工程學研究所<br>106<br>This thesis describes a wake model which incorporates an actuator disk with a body force and turbulence source approach. The force source simulates the flow deceleration effect based on the wake flow of the wind turbine, whereas the turbulence source models the turbulence generation resulting from the rotational motion of the turbine rotor. The investigated three-dimensional flow field is described by the steady continuity and momentum equations together with a modified k-ε turbulence model, which are solved by an in-house, parallelized, RANS-based Fortr
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37

Chiang, Yen-Cheng, and 江衍成. "Wind Farm Power Prediction via Actuator Disk Model." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/8spdhv.

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碩士<br>國立臺灣大學<br>工程科學及海洋工程學研究所<br>107<br>This study predicts the power characteristics of three onshore Taiwanese wind farms based on the wind direction distribution and wind data from long-term meteorological measurements in those wind farms, where typical wind condition is then defined from the statistical result of measurement. To calculate the power output under typical wind condition in the wind farm, a three-dimensional flow field is described by the steady continuity and momentum equation coupled with k-ε turbulence model, which is solved by an actuator disk model code, WIFA3D. The power
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38

Potter, Cameron Wallace. "Development of a very short-term prediction system for windpower generation." Thesis, 2006. https://eprints.utas.edu.au/21251/1/whole_PotterCameronWallace2006_thesis.pdf.

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Accurate forecasts are vital to efficient power system operation. This makes power systems forecasting research an important task. Windpower is presently the fastest growing power generation sector in the world; however, windpower is intermittent. Accurate forecasts are required to make the best use of this rapidly emerging technology. In fact, before large-scale penetration of windpower into a power system can be successfully managed it is important to be able to predict the rapid fluctuations of wind on a short time scale. This thesis concentrates upon wind parameter prediction on the "ver
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39

Chang, Chia-Yuan, and 張家源. "Wind Power Generation Prediction Using Data Mining Technique:The Case of Chang Kung Wind Farm." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/7z77eq.

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碩士<br>國立中興大學<br>資訊管理學系所<br>106<br>Given that wind power generators require high establishment costs and their power storage demands high maintenance, predicting wind power generation capacity based on a certain location’s weather conditions could help interested parties on the electric power market evaluate whether the establishment is worthwhile and make decisions concerning electrical power. With such in mind, this study’s experiment results provide interested parties with a different index for consideration when it comes to wind power application and establishment. This study takes wind po
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Chen, Chang-Jie, and 陳昌杰. "The Structure Discussion and Application of RSCMAC Based Wind Power Prediction." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/27331777846250747056.

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碩士<br>健行科技大學<br>電機工程系碩士班<br>102<br>In recent years, the development of renewable energy is in international attention. In Taiwan, renewable energy is also developed aggressively after passing Renewable Energy Law and under the policy of Feed-in Tariff. Wind Power is one of the important development projects. Wind Power is intermittent energy; its power generation depends on climate changes. If wind power can be precisely predicted, the use and dispatch of electricity can be responded as earlier as possible, thus wind power is very possible to become part of the electricity base load. This rese
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"Short-Term Wind Power Forecasts using Doppler Lidar." Master's thesis, 2014. http://hdl.handle.net/2286/R.I.27540.

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abstract: With a ground-based Doppler lidar on the upwind side of a wind farm in the Tehachapi Pass of California, radial wind velocity measurements were collected for repeating sector sweeps, scanning up to 10 kilometers away. This region consisted of complex terrain, with the scans made between mountains. The dataset was utilized for techniques being studied for short-term forecasting of wind power by correlating changes in energy content and of turbulence intensity by tracking spatial variance, in the wind ahead of a wind farm. A ramp event was also captured and its propagation was tracked.
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Ou, Jing, and 歐靖. "Study of Power Generation Prediction of Taiwan Wind Farm Based on Machine Learning." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/xbbdar.

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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>Because of the shortage of non-renewable energy, wind energy, which an environmentally friendly energy, has become an important alternative to fossil fuels. But the wind has the characteristics of intermittence, volatility and randomness. Wind power has brought some challenges to the stability of power system. The prediction of wind power is an important way to solve this problem. Wind speed forecasting is an important part of wind power prediction. Under this background, this paper focuses on the following aspects of the short-term wind power prediction: Use
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He, Yan-Lou, and 何彥樓. "A study of wind speed and power generation prediction based on particle swarm opt." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/47826392659434340806.

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碩士<br>中原大學<br>電機工程研究所<br>100<br>The output power generation of a wind turbine will fluctuate according to the unstable wind speed. If we can predict the wind power generation and its speed precisely, the economic benefits and the cost of a wind turbine can be evaluated. Therefore, to improve the prediction accuracy, this paper employs three prediction methods, including linear time-series based model, double exponential smoothing and general regression neural network and their combinations with K-means clustering, nearest neighbor clustering and correlation clustering individually to predict t
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Chang, Ming-Jen, and 張銘仁. "Frequency Nadir Prediction of Power System with High Wind Penetration after Generators Tripping Using an Artificial Neural Networks." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/gp76we.

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碩士<br>國立臺北科技大學<br>電機工程系研究所<br>101<br>Maintaining frequency stability is dynamic security requirements in power system operations. With the increase in operation of variable-speed wind turbine, the traditional generator will be replaced gradually. Due to the intermittent characteristic of wind power generation, if the wind turbine generators (WTGs) provide neither inertial response nor frequency control, the rate of system frequency variation will be obvious. When generators trip in a hybrid system with the connection of WTGs and diesel generators, the drop in system frequency is apparent; this
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"Vorhersage der Leistungsabgabe netzeinspeisender Windkraftanlagen zur Unterstuetzung der Kraftwerkseinsatzplanung." 2000. http://docserver.bis.uni-oldenburg.de/publikationen/dissertation/2001/moevor00/moevor00.html.

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Black, Jonathan D. "Load Hindcasting: A Retrospective Regional Load Prediction Method Using Reanalysis Weather Data." 2011. https://scholarworks.umass.edu/theses/666.

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The capacity value (CV) of a power generation unit indicates the extent to which it contributes to the generation system adequacy of a region’s bulk power system. Given the capricious nature of the wind resource, determining wind generation’s CV is nontrivial, but can be understood simply as how well its power output temporally correlates with a region’s electricity load during times of system need. Both wind generation and load are governed by weather phenomena that exhibit variability across all timescales, including low frequency weather cycles that span decades. Thus, a data-driven determi
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Chiang, Chih-Yi, and 江致頤. "On Attributes Selection for Predicting Wind Power of Wind Turbine." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/07915234575925912260.

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碩士<br>國立勤益科技大學<br>工業工程與管理系<br>102<br>The oil supply has gradually decreased since the 80’s oil glut and the price of traditional energy sources continue to rise. If people continue to depend on crude oil, it is not surprising that it will cause an economic crisis in a near future. This, on the other hand, has showed the importance of renewable energy. Compared with other renewable energy, electricity produced by wind power does not create gas waste or water pollution. Many advanced countries have started to develop wind energy technology. However, if the instability of wind power could be pred
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Lange, Matthias [Verfasser]. "Analysis of the uncertainty of wind power predictions / Matthias Lange." 2003. http://d-nb.info/969985789/34.

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SRIVASTAVA, TUSHAR. "PREDICTIVE ANALYSIS OF SHORT-TERM WIND POWER FORECASTING USING VARIOUS NEURAL NETWORKS MODEL." Thesis, 2019. http://dspace.dtu.ac.in:8080/jspui/handle/repository/17051.

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This study proposes a intelligent method for short-term wind power forecasting and uncertainty analysis. In practice, the power output of a wind turbine is a direct function of wind speed. Owing to the intermittent and irregular nature of wind, the wind power generation is not easily dispatched and the prediction of wind power is highly uncertain. To allow a procedure for more accurate forecasting, few wind power prediction models like: NARX (Non-Linear Exogenous Inputs), NLIO (non-Linear Input& Output), RNN (Recurrent Neural Network), LSTM (Long Short Term Memory), GBM(Gradient Boosting Mach
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Antonishen, Michael P. "Use of a model predictive control framework for optimal control of grid scale electrical energy storage in conjunction with a wind farm." Thesis, 2012. http://hdl.handle.net/1957/29854.

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Over the last decade, wind penetration in the Pacific Northwest has increased rapidly. The variable nature of this massive new resource has increased stress on the hydropower resource to the point where system limits are currently being reached. In order to cultivate continued growth of the wind energy industry both in the Pacific Northwest and the rest of the world, something must be added to help mitigate the effects of the variability of wind power. This research aims to show what can be done by adding energy storage to a wind farm. A novel model predictive control structure has been cr
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