Relevant bibliographies by topics / Wind power prediction

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Wind power prediction'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Wind power prediction.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Wind power prediction"

1

Wu, Xinhua, Nan Chen, Qianyun Du, Shuangshuang Mao, and Xiaoming Ju. "Short-term wind power prediction model based on ARMA-GRU-QPSO and error correction." Journal of Physics: Conference Series 2427, no. 1 (2023): 012028. http://dx.doi.org/10.1088/1742-6596/2427/1/012028.

Full text
Abstract:
Abstract Power system dispatch benefits from accurate wind power predictions. To increase the prediction precision for wind power, this paper proposes a combined model for predicting short-term wind power based on the autoregressive moving average-gated recurrent unit (ARMA-GRU). Firstly, we build the ARMA model and GRU model respectively to predict wind power. Then we optimize the combined model’s weights by quantum particle swarm algorithm (QPSO). Finally, we build an error correction model for the prediction errors to acquire the final results for the wind power predictions. Our experimenta
APA, Harvard, Vancouver, ISO, and other styles
2

Bao-Wei Zhang, Bao-Wei Zhang, Hong-Bo Cui Bao-Wei Zhang, and Jiu-Xiang Song Hong-Bo Cui. "Wind Power Prediction Based on Difference Method." 電腦學刊 33, no. 4 (2022): 195–204. http://dx.doi.org/10.53106/199115992022083304016.

Full text
Abstract:
<p> The renewable wind power sources are difficult to be predicted in view of the fluctuating factors such as wind bearing, pressure, wind speed, and humidity of the surrounding atmosphere. An attempt is made in this paper to propose a difference method to build a neural network and a long short term memory (LSTM) model for wind power prediction. First, the correlation of each data is analyzed and then per-forming difference processing on the original data to solve the problem that the original data cannot be analyzed by probability distribution. The prediction is made by building the ne
APA, Harvard, Vancouver, ISO, and other styles
3

Hu, Hongda, Zhiyong Hu, Kaiwen Zhong, et al. "Long-term offshore wind power prediction using spatiotemporal kriging: A case study in China’s Guangdong Province." Energy Exploration & Exploitation 38, no. 3 (2019): 703–22. http://dx.doi.org/10.1177/0144598719889368.

Full text
Abstract:
The predicted wind power in coastal waters is an important factor when planning and developing offshore wind farms. The stochastic wind field challenges the accuracy of these predictions. Using single-point wind measurements, most previous studies have focused on the prediction of short-term wind power, ranging from minutes to several days. Longer-term wind power predictions would better support decision-making related to offshore wind power balance management and reserve capacities. In addition, larger-scale wind power predictions, based on gridded wind field data, would provide a more compre
APA, Harvard, Vancouver, ISO, and other styles
4

Guo, Wei, Li Xu, Danyang Zhao, Dianqiang Zhou, Tian Wang, and Xujing Tang. "A Wind Power Combination Forecasting Method Based on GASF Image Representation and UniFormer." Journal of Marine Science and Engineering 12, no. 7 (2024): 1173. http://dx.doi.org/10.3390/jmse12071173.

Full text
Abstract:
In the field of wind power prediction, traditional methods typically rely on one-dimensional time-series data for feature extraction and prediction. In this study, we propose an innovative short-term wind power forecasting approach using a “visual” 2D image prediction method that effectively utilizes spatial pattern information in time-series data by combining wind power series and related environmental features into a 2D GASF image. Firstly, the wind power data are decomposed using the ICEEMDAN algorithm optimized by the BWO (Beluga Whale Optimization) algorithm, extracting the submodal IMF (
APA, Harvard, Vancouver, ISO, and other styles
5

Liu, Yi, Jun He, Yu Wang, Zong Liu, Lixun He, and Yanyang Wang. "Short-Term Wind Power Prediction Based on CEEMDAN-SE and Bidirectional LSTM Neural Network with Markov Chain." Energies 16, no. 14 (2023): 5476. http://dx.doi.org/10.3390/en16145476.

Full text
Abstract:
Accurate wind power data prediction is crucial to increase wind energy usage since wind power data are characterized by uncertainty and randomness, which present significant obstacles to the scheduling of power grids. This paper proposes a hybrid model for wind power prediction based on complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN), sample entropy (SE), bidirectional long short-term memory network (BiLSTM), and Markov chain (MC). First, CEEMDAN is used to decompose the wind power series into a series of subsequences at various frequencies, and then SE is emp
APA, Harvard, Vancouver, ISO, and other styles
6

Liu, Yuanlong, Yuanbiao Zhang, and Ziyue Chen. "Wind Power Prediction Investigation." Research Journal of Applied Sciences, Engineering and Technology 5, no. 5 (2013): 1762–68. http://dx.doi.org/10.19026/rjaset.5.4935.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Liu, Hai Ke, Jiang Xia Feng, Shen Quan Yang, and Tao Jia. "Wind Power Prediction Model Based on ARMA and Improved BP-ANN." Advanced Materials Research 1008-1009 (August 2014): 183–87. http://dx.doi.org/10.4028/www.scientific.net/amr.1008-1009.183.

Full text
Abstract:
In order to improve the prediction accuracy of wind power, this research is based on time series and improved BP-ANN algorithm. The basic idea is described as follows: wind speed forecasting model is established by using time series method; wind speed-wind power model is built by utilising improved BP-ANN algorithm; wind speed data from time series forecasting is used as input of neural network model, and the prediction results for wind power are obtained. In order to analyse the availability of wind power prediction model, the mean absolute error and correlation coefficient are compared to an
APA, Harvard, Vancouver, ISO, and other styles
8

Li, Wei, Hong Tu Zhang, and Ting Ting An. "Study on Short-Term Wind Power Prediction Model Based on ARMA Theory." Applied Mechanics and Materials 448-453 (October 2013): 1875–78. http://dx.doi.org/10.4028/www.scientific.net/amm.448-453.1875.

Full text
Abstract:
At present, the difficulty of wind power integration has resulted in a large number of wind curtailment phenomena and wasted a lot of renewable energy. Due to the significant instability, anti-peak-regulation and intermittency of wind power, wind power integration needs an accurate prediction technique to be a basis. ARMA model has the advantage of high prediction accuracy in predicting short-term wind power. This paper puts forward the method for short-term wind power prediction using ARMA model and carries out empirical analysis using the data from a wind farm of Jilin province, which shows
APA, Harvard, Vancouver, ISO, and other styles
9

Solovev, Bogdan, and Giorgi Gamisonia. "WIND POWER PREDICTION METHODS FOR SHELF WIND POWER PLANTS." Electrical and data processing facilities and systems 18, no. 3-4 (2022): 108–20. http://dx.doi.org/10.17122/1999-5458-2022-18-3-4-108-120.

Full text
Abstract:
Relevance Wind energy forecasting is an opportunity to evaluate the production possibilities of a wind farm in the short term. Production often refers to the available capacity of the wind farm in question. For example, to date, the installed wind power in Russia has reached 20 GW. Direct transmission operators use existing tools to forecast wind production up to 48 hours. Forecasting tools help optimize power system management. This article discusses the abundance of relevant forecasting methods in the field of wind energy, evaluates their effectiveness and value for the most effective contro
APA, Harvard, Vancouver, ISO, and other styles
10

Ramadevi, Bhukya, Venkata Ramana Kasi, and Kishore Bingi. "Hybrid LSTM-Based Fractional-Order Neural Network for Jeju Island’s Wind Farm Power Forecasting." Fractal and Fractional 8, no. 3 (2024): 149. http://dx.doi.org/10.3390/fractalfract8030149.

Full text
Abstract:
Efficient integration of wind energy requires accurate wind power forecasting. This prediction is critical in optimising grid operation, energy trading, and effectively harnessing renewable resources. However, the wind’s complex and variable nature poses considerable challenges to achieving accurate forecasts. In this context, the accuracy of wind parameter forecasts, including wind speed and direction, is essential to enhancing the precision of wind power predictions. The presence of missing data in these parameters further complicates the forecasting process. These missing values could resul
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Wind power prediction"

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.

Full text
Abstract:
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
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
Abstract:
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
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
Abstract:
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
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
Abstract:
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
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
Abstract:
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
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
Abstract:
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
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
Abstract:
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
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
Abstract:
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
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Wind power prediction"

1

G, Sigari, Costi T, Michigan State University. Division of Engineering Research., and United States. National Aeronautics and Space Administration., eds. Effect of accuracy of wind power prediction on power system operator: Final report. College of Engineering, Michigan State University, 1985.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
2

National Renewable Energy Laboratory (U.S.) and IEEE Energy Conversion Congress and Exposition (2012 : Raleigh, N.C.), eds. Wind power plant prediction by using neural networks: Preprint. National Renewable Energy Laboratory, 2012.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
3

P, Shepherd Kevin, and Langley Research Center, eds. Wind turbine acoustics. National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Division, 1990.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
4

Effect of accuracy of wind power prediction on power system operator: Final report. College of Engineering, Michigan State University, 1985.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
5

Lange, Matthias, and Ulrich Focken. Physical Approach to Short-Term Wind Power Prediction. Springer London, Limited, 2006.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
6

Lange, Matthias, and Ulrich Focken. Physical Approach to Short-Term Wind Power Prediction. Springer, 2009.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
7

Lange, Matthias, and Ulrich Focken. Physical Approach to Short-Term Wind Power Prediction. Springer Berlin / Heidelberg, 2010.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
8

Physical Approach to Short-Term Wind Power Prediction. Springer-Verlag, 2006. http://dx.doi.org/10.1007/3-540-31106-8.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Physical Approach to Short-Term Wind Power Prediction. Springer, 2005.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
10

Zastrau, David. Estimation of Uncertainty of Wind Energy Predictions: With Application to Weather Routing and Wind Power Generation. Lang GmbH, Internationaler Verlag der Wissenschaften, Peter, 2017.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Book chapters on the topic "Wind power prediction"

1

Ernst, Bernhard. "Wind Power Prediction." In Wind Power in Power Systems. John Wiley & Sons, Ltd, 2012. http://dx.doi.org/10.1002/9781119941842.ch33.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Li, Kai, Kaiming Shi, Ruiming Ma, Shengpeng Sang, Shitong Cao, and Yuliang Gou. "Wind Power Correction Prediction Considering Similar Wind Power Climbing Events." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-7146-2_68.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Treiber, Nils André, Justin Heinermann, and Oliver Kramer. "Wind Power Prediction with Machine Learning." In Computational Sustainability. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31858-5_2.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Dvorský, Jiří, Stanislav Mišák, Lukáš Prokop, and Tadeusz Sikora. "On Wind Power Station Production Prediction." In Networked Digital Technologies. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14306-9_65.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Şen, Zekâi. "Innovative Wind Energy Models and Prediction Methodologies." In Handbook of Wind Power Systems. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41080-2_4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Guo, Chengke, Qijun Wang, Bo Peng, and Ning Mei. "A Review of Wind Power Cluster Forecasting Techniques." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-4856-6_20.

Full text
Abstract:
Abstract As the global energy crisis and environmental protection issues intensify, wind energy shows great potential as a clean energy source. However, the intermittency and uncertainty of wind energy resources pose challenges to the stable operation of equipment and power system dispatch, and accurate prediction of wind farm-related parameters has become a key issue. The article systematically summarizes the domestic and international research progress of wind power cluster prediction technology and gives an outlook. Firstly, it analyzes the necessary steps in the pre-processing of wind powe
APA, Harvard, Vancouver, ISO, and other styles
7

Ambach, Daniel, and Carsten Croonenbroeck. "Obtaining Superior Wind Power Predictions from a Periodic and Heteroscedastic Wind Power Prediction Tool." In Springer Proceedings in Mathematics & Statistics. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-13881-7_25.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Lydia, M., S. Suresh Kumar, A. Immanuel Selvakumar, and G. Edwin Prem Kumar. "Wind Farm Power Prediction Based on Wind Speed and Power Curve Models." In Lecture Notes in Electrical Engineering. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4852-4_2.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Kolumbán, Sándor, Stella Kapodistria, and Nazanin Nooraee. "Short Term Wind Turbine Power Output Prediction." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-31234-2_7.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Wu, Wenjie, Heping Jin, Gan Wang, et al. "Research on Wind Power Peak Prediction Method." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1068-3_66.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Wind power prediction"

1

Ghazaei, Elman, Omid Feizi, Amir A. Ghavifekr, Mina Salim, and Armin Hassanzadeh. "Wind Farm Power Prediction with Transformer Encoder." In 2024 9th International Conference on Technology and Energy Management (ICTEM). IEEE, 2024. http://dx.doi.org/10.1109/ictem60690.2024.10631900.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Arati, Devi C., Parvathy S. Menon, Jithin Velayudhan, Prabaharan Poornachandran, Arun K. Raj, and Sikha O. K. "Enhancing Wind Power Prediction through Machine Learning." In 2024 4th International Conference on Artificial Intelligence and Signal Processing (AISP). IEEE, 2024. https://doi.org/10.1109/aisp61711.2024.10870705.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Hu, Mengying, Xiang Gao, Jiandong Duan, Yifei He, Junpeng Ji, and Lei Yang. "Wind Power Prediction Based on SSA-SVM." In 2025 9th International Conference on Green Energy and Applications (ICGEA). IEEE, 2025. https://doi.org/10.1109/icgea64602.2025.11009957.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Ye, Hongzhi. "Wind speed prediction based on improved Informer." In 2024 4th International Conference on Intelligent Power and Systems (ICIPS). IEEE, 2024. https://doi.org/10.1109/icips64173.2024.10900083.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Zhao, Qianhao, Jiazheng Zhang, and Yudong Meng. "Research on Power Prediction of Offshore Wind Power Based on BiSTM." In 2024 6th International Conference on Energy Systems and Electrical Power (ICESEP). IEEE, 2024. http://dx.doi.org/10.1109/icesep62218.2024.10651659.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Ren, Zhe, Chengshuai Huang, and Meng Li. "Research on Wind Power Prediction." In 2019 IEEE 3rd Conference on Energy Internet and Energy System Integration (EI2). IEEE, 2019. http://dx.doi.org/10.1109/ei247390.2019.9061851.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Kramer, Oliver, and Jill Baumann. "Wind Power Prediction with ETSformer." In ESANN 2023 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Ciaco - i6doc.com, 2023. http://dx.doi.org/10.14428/esann/2023.es2023-173.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Zhang, Peng, Chunyan Li, and Qian Zhang. "Wind power accommodation considering the prediction error of wind power." In 2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS). IEEE, 2016. http://dx.doi.org/10.1109/pmaps.2016.7764079.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Renani, E. T., M. F. M. Elias, and N. Abd Rahim. "Wind power prediction using enhanced parametric wind power curve modeling." In 4th IET Clean Energy and Technology Conference (CEAT 2016). Institution of Engineering and Technology, 2016. http://dx.doi.org/10.1049/cp.2016.1359.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Choudhary, A. K., K. G. Upadhyay, and M. M. Tripathi. "Soft computing applications in wind speed and power prediction for wind energy." In 2012 IEEE Fifth Power India Conference. IEEE, 2012. http://dx.doi.org/10.1109/poweri.2012.6479588.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Wind power prediction"

1

Pilla, Ernani, Sean Casto, Julia Willmott, et al. Bird and Bat Collision Risks & Wind Energy Facilities. Inter-American Development Bank, 2012. http://dx.doi.org/10.18235/0006988.

Full text
Abstract:
The Inter-American Development Bank (IDB) has seen its portfolio of wind power projects increase substantially, a trend which is expected to continue. This report is intended to provide expert guidance regarding wind wildlife risk issues, and to ensure that environmental impact considerations are sufficiently incorporated into the IDB's wind energy projects. Guidance is provided in 3 specific areas corresponding to the 3 chapters of this report as follows: Efficacy of bird and bat impact minimization/mitigation measures (Chapter 1); Efficacy of preconstruction collision risk prediction models
APA, Harvard, Vancouver, ISO, and other styles
2

Sadegh, Mojtaba, Seyd Seydi, John Abatzoglou, Amir AghaKouchak, Mir Matin, and Kaveh Madani. January 2025 Los Angeles Wildfires: Once-in-a-Generation Events Now Happen Frequently. United Nations University Institute for Water, Environment and Health (UNU INWEH), 2025. https://doi.org/10.53328/inr25mos003.

Full text
Abstract:
1. On January 7, 2025, Palisades and Eaton fires started and burned through urban areas of Los Angeles County, California. They collectively destroyed nearly 16,250 structures, and directly exposed ~41,000 people, ranking them 2nd and 3rd most destructive wildfires in California’s history1. 2. Started during drought conditions coincident with the Santa Ana winds with wind gusts exceeding 100 miles per hour, the fires rapidly spread into densely populated urban areas, resulting in 29 fatalities and widespread population displacement. 3. The January 2025 Los Angeles wildfires underscore the incr
APA, Harvard, Vancouver, ISO, and other styles
3

Martin Wilde, Principal Investigator. The use of real-time off-site observations as a methodology for increasing forecast skill in prediction of large wind power ramps one or more hours ahead of their impact on a wind plant. Office of Scientific and Technical Information (OSTI), 2012. http://dx.doi.org/10.2172/1062998.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Wind power prediction' for particular source types:
Journal articles Dissertations / Theses Books
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography