scholarly journals Using quantile regression to extend an existing wind power forecasting system with probabilistic forecasts

Wind Energy ◽  
2006 ◽  
Vol 9 (1-2) ◽  
pp. 95-108 ◽  
Author(s):  
Henrik Aalborg Nielsen ◽  
Henrik Madsen ◽  
Torben Skov Nielsen
Keyword(s):  
Quantile Regression ◽  
Wind Power ◽  
Wind Power Forecasting ◽  
Probabilistic Forecasts ◽  
Forecasting System ◽  
Power Forecasting

Development and Operational Status of Wind Power Forecasting System

2013 ◽  
Vol 133 (4) ◽  
pp. 366-372 ◽  
Author(s):  
Isao Aoki ◽  
Ryoichi Tanikawa ◽  
Nobuyuki Hayasaki ◽  
Mitsuhiro Matsumoto ◽  
Shigero Enomoto
Keyword(s):  
Wind Power ◽  
Wind Power Forecasting ◽  
Forecasting System ◽  
Power Forecasting

Wind Power Forecasting Based on the BP Neural Network

2013 ◽  
Vol 341-342 ◽  
pp. 1303-1307 ◽  
Author(s):  
Jian Dong Mao ◽  
Xiao Jing Zhang ◽  
Juan Li
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Bp Neural Network ◽  
Engineering Application ◽  
Weather Forecast ◽  
Difficult Problem ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Forecasting System ◽  
Power Forecasting

Accurate short-term wind power forecasting has important significance to safety, stability and economy of power system dispatching and also it is a difficult problem in practical engineering application. In this paper, by use of the data of numerical weather forecast, such as wind speed, wind direction, temperature, relative humidity and pressure of atmosphere, a short-term wind power forecasting system based on BP neural network has been developed. For verifying the feasibility of the system, some experiments have been were carried out. The results show that the system is capable of predicting accurately the wind power of future 24 hours and the forecasting accuracy of 85.6% is obtained. The work of this paper has important engineering directive significance to the similar wind power forecasting system.

scholarly journals IEA Wind Task 36 Forecasting

2020 ◽  
Author(s):  
Gregor Giebel ◽  
Will Shaw ◽  
Helmut Frank ◽  
Pierre Pinson ◽  
Caroline Draxl ◽  
...  
Keyword(s):  
Wind Power ◽  
Selection Process ◽  
Meteorological Data ◽  
Weather Prediction ◽  
International Energy Agency ◽  
General Situation ◽  
Energy Agency ◽  
Wind Power Forecasting ◽  
Probabilistic Forecasts ◽  
Power Forecasting

<p>Wind power forecasts have been used operatively for over 20 years. Despite this fact, there are still several possibilities to improve the forecasts, both from the weather prediction side and from the usage of the forecasts. The International Energy Agency (IEA) Wind Task on Wind Power Forecasting organises international collaboration, among national weather centres with an interest and/or large projects on wind forecast improvements (NOAA, DWD, UK MetOffice, ...), forecast vendors and forecast users.<br>Collaboration is open to IEA Wind member states, 12 countries are already therein.</p><p>The Task is divided in three work packages: Firstly, a collaboration on the improvement of the scientific basis for the wind predictions themselves. This includes numerical weather prediction model physics, but also widely distributed information on accessible datasets. Secondly, we will be aiming at an international pre-standard (an IEA Recommended Practice) on benchmarking and comparing wind power forecasts, including probabilistic forecasts. This WP will also organise benchmarks for NWP models. Thirdly, we will be engaging end users aiming at dissemination of the best practice in the usage of wind power predictions.</p><p>The main result is the IEA Recommended Practice for Selecting Renewable Power Forecasting Solutions. This document in three parts (Forecast solution selection process, and Designing and executing forecasting benchmarks and trials, and their Evaluation) takes its outset from the recurrent problem at forecast user companies of how to choose a forecast vendor. The first report describes how to tackle the general situation, while the second report specifically describes how to set up a forecasting trial so that the result is what the client intended. Many of the pitfalls which we have seen over the years, are avoided. <br><br>Other results include a paper on possible uses of uncertainty forecasts, an assessment of the uncertainty chain within the forecasts, and meteorological data on an information portal for wind power forecasting. This meteorological data is used for a benchmark exercise, to be announced at the conference. The poster will present the latest developments from the Task, and announce the next activities.</p>

scholarly journals Continuous and Distribution-free Probabilistic Wind Power Forecasting

2021 ◽  
Author(s):  
honglin wen ◽  
Pierre Pinson ◽  
jinghuan ma ◽  
jie gu ◽  
Zhijiang Jin
Keyword(s):  
Wind Power ◽  
Distribution Free ◽  
Wind Power Forecasting ◽  
Proposed Model ◽  
Probabilistic Forecasts ◽  
Probability Densities ◽  
Data Driven Approach ◽  
Open Datasets ◽  
Approximation Capability ◽  
Power Forecasting

We present a data-driven approach for probabilistic wind power forecasting based on conditional normalizing flow~(CNF). In contrast with the existing, this approach is distribution-free (as for non-parametric and quantile-based approaches) and can directly yield continuous probability densities, hence avoiding quantile crossing. It relies on a base distribution and a set of bijective mappings. Both the shape parameters of the base distribution and the bijective mappings are approximated with neural networks. Spline-based conditional normalizing flow is considered owing to its universal approximation capability. Over the training phase, the model sequentially maps input examples onto samples of base distribution, where parameters are estimated through maximum likelihood. To issue probabilistic forecasts, one eventually map samples of the base distribution into samples of a desired distribution. Case studies based on open datasets validate the effectiveness of the proposed model, and allows us to discuss its advantages and caveats with respect to the state of the art. Code will be released upon publication.

Development and Operational Status of Wind Power Forecasting System

2014 ◽  
Vol 189 (4) ◽  
pp. 22-29 ◽  
Author(s):  
Isao Aoki ◽  
Ryoichi Tanikawa ◽  
Nobuyuki Hayasaki ◽  
Mitsuhiro Matsumoto ◽  
Shigero Enomoto
Keyword(s):  
Wind Power ◽  
Wind Power Forecasting ◽  
Forecasting System ◽  
Power Forecasting

A Wind Power Forecasting System to Optimize Power Integration

2011 ◽  
Author(s):  
Sue Ellen Haupt ◽  
Gerry Wiener ◽  
Yubao Liu ◽  
Bill Myers ◽  
Juanzhen Sun ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Wind Power ◽  
Learning Algorithm ◽  
Wrf Model ◽  
Forecast Accuracy ◽  
The United States ◽  
Processing Technologies ◽  
Wind Power Forecasting ◽  
Forecasting System ◽  
Power Forecasting

The National Center for Atmospheric Research (NCAR) has developed a wind prediction system for Xcel Energy, the power company with the largest wind capacity in the United States. The wind power forecasting system includes advanced modeling capabilities, data assimilation, nowcasting, and statistical post-processing technologies. The system ingests both external model data and observations. NCAR produces a deterministic mesoscale wind forecast of hub height winds on a very fine resolution grid using the Weather Research and Forecasting (WRF) model, run using the Real Time Four Dimensional Data Assimilation (RTFDDA) system. In addition, a 30 member ensemble system is run to both improve forecast accuracy and provide an indication of forecast uncertainty. The deterministic and ensemble model output plus data from various global and regional models are ingested by NCAR’s Dynamic, Integrated, Forecast System (DICast®), a statistical learning algorithm. DICast® produces forecasts of wind speed for each wind turbine. These wind forecasts are then fed into a power conversion algorithm that has been empirically derived for each Xcel power connection node. In addition, a ramp forecasting technology fine-tunes the capability to accurately predict the time, magnitude, and duration of a ramping event. This basic system has consistently improved Xcel’s ability to optimize the economics of incorporating wind energy into their power system.

D110 Wind power forecasting system for the control area of power grid in Japan

2008 ◽  
Vol 2008.13 (0) ◽  
pp. 165-166
Author(s):  
Nobuyuki HAYASAKI ◽  
Isao AOKI ◽  
Ryoichi TANIKAWA ◽  
Hisashi FUKUDA
Keyword(s):  
Wind Power ◽  
Power Grid ◽  
Control Area ◽  
Wind Power Forecasting ◽  
Forecasting System ◽  
Power Forecasting
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