Probabilistic Wind Power Forecasting by Using Quantile Regression Analysis

2017-09-22
Ozkan, Mehmet Baris
Guvengir, Umut
Kucuk, Dilek
Secen, Ali Unver
Buhan, Serkan
Demirci, Turhan
Bestil, Abdullah
Er, Ceyda
Karagöz, Pınar
Show full item record
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
197
views
0
downloads
Effective use of renewable energy sources, and in particular wind energy, is of paramount importance. Compared to other renewable energy sources, wind is so fluctuating that it must be integrated to the electricity grid in a planned way. Wind power forecast methods have an important role in this integration. These methods can be broadly classified as point wind power forecasting or probabilistic wind power forecasting methods. The point forecasting methods are more deterministic and they are concerned with the exact forecast for a particular time interval. These forecasts are very important especially for the Wind Power Plant (WPP) owners who attend the energy market with these forecasts from day-ahead. Probabilistic wind power forecasting is more crucial for the operational planning of the electricity grid by grid operators. In this methodology, the uncertainty in the wind power forecast for WPPs are presented within some confidence. This paper presents a probabilistic wind power forecasting method based on local quantile regression with Gaussian distribution. The method is applied to obtain probabilistic wind power forecasts, within the course of the Wind Power Monitoring and Forecast Center for Turkey (RİTM) project, which has been realized by TÜBİTAK MAM. Currently, 132 WPPs are included in the project and they are being monitored in real-time. In this paper, the results for 15 of these WPPs, which are selected from different regions of the country, are presented. The corresponding results are calculated for two different confidence intervals, namely 5–95 and 25–75 quantiles.
Wind Power Forecasting , Quantile Regression , Probabilistic Forecast
https://hdl.handle.net/11511/39888
DARE 2017.
Department of Computer Engineering, Conference / Seminar

Suggestions

Rethinking wind energy in its social context: conflicting perspectives and planning problems – the Karaburun experience
Özçam, Zeynep; Pınarcıoğlu, Mehmet Melih; Department of City and Regional Planning (2016)
As a result of global concerns about energy related environmental problems, the use of renewable energy resources has gained great importance. The renewables are widely recognized as an effective tool for limiting energy related emissions and a fundamental component of sustainable development. Among these, especially wind energy has considerable involvement. Due to this, major national policies intend to support wind energy and wind energy installations have also started to grow with an expanding rate. Like...
Unsteady atmospheric flow solutions with openfoam coupled with the numerical weather prediction software, WRF
Leblebici, Engin; Tuncer, İsmail Hakkı; Department of Aerospace Engineering (2018)
Ways to predict wind energy potential and energy generation accurately are sought in many applications such as the wind turbine siting and the short term wind power production estimation. Current wind energy prediction models are based on the statistical analysis of long term observation data and the reconstruction of average wind fields by means of numerical tools. Even if computational fluid dynamics tools based on Navier-Stokes solutions are employed for sectoral wind fields, the reconstruction of wind f...
Determination of wind power potential and optimal wind power plant locations in Turkey
Çetinay, Hale; Güven, Ali Nezih; Tör, Osman Bülent; Department of Electrical and Electronics Engineering (2014)
Due to increased environmental concerns, utilization of the renewable energy, especially the wind power, gains importance. Like developed countries which have set their renewable energy targets for the long term integration, Turkish government has also set the goal as 20 GW of total wind power installed capacity in 2023. In this thesis, the wind power potential in Turkey is analyzed and different cases are studied to determine optimal locations for wind power plant establishment. In order to determine the w...
Data mining-based power generation forecast at wind power plants
Özkan, Mehmet Barış; Karagöz, Pınar; Department of Computer Engineering (2014)
As a result of rapid depletion of non-renewable energy resources, the importance of the e efficient utilization of renewable energy sources has increased all over the world and in our country in recent years. Wind has an important role in renewable energy sources with its high potential. However, compared to other renewable energy sources, wind has a spatial and temporal discontinuity characteristic so there is a need for estimating and planning of wind power generation. Wind Power Plants (WPPs) inform thei...
Data Mining-Based Upscaling Approach for Regional Wind Power Forecasting: Regional Statistical Hybrid Wind Power Forecast Technique (RegionalSHWIP)
Ozkan, Mehmet Baris; Karagöz, Pınar (Institute of Electrical and Electronics Engineers (IEEE), 2019-01-01)
With the increasing need for the energy, the importance of renewable energy sources has also been increasing. In order to include the power produced by the wind into electricity grid in a controlled manner, power prediction has an important role. To produce a reliable wind power forecast, obtaining Wind Power Plants' (WPP) power generation data in real time and constructing the power forecast model with historical production values is a desirable action plan. However, this situation may not be applicable fo...
Citation Formats
M. B. Ozkan et al., “Probabilistic Wind Power Forecasting by Using Quantile Regression Analysis,” presented at the DARE 2017., 2017, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/39888.
METU IIS - Integrated Information Service open@metu.edu.tr