Comparative study of power forecasting methods for wind farms

Power system includes many types of markets. Such markets are generally cleared at certain times, whereas market participators have to determine their operational plans before meeting the actual conditions. Therefore, forecasting methods can assist market players. Forecasting methods are applied to forecast electricity demand. The unknown conditions in the power system are increased by integration of renewable generation units. Forecasting methods, which are used for the load forecasting, are updated because the output power of renewable generation units such as wind farms and photovoltaic (PV) panels have more deviation than power demand. The pool market can be introduced as other parameter that is forecasted by market players. In this chapter, the authors investigate a mathematical model for forecasting of wind. Then, the forecasting model is proposed. Genetic algorithm is applied as an optimization method to handle delay associated with wind forecasting.

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Analysis of possibilities to improve quality of spatial wind speed forecasts for efficient forecasting of electric energy production in onshore wind farms in Poland

E3S Web of Conferences ◽

10.1051/e3sconf/20198401001 ◽

2019 ◽

Vol 84 ◽

pp. 01001

Author(s):

Paweł Piotrowski ◽

Dariusz Baczyński ◽

Marcin Kopyt ◽

Karolina Szafranek

Keyword(s):

Statistical Analysis ◽

Wind Speed ◽

Energy Production ◽

Meteorological Data ◽

Electric Energy ◽

Wind Farms ◽

Bilinear Method ◽

Explanatory Variables ◽

Forecasting Methods

The most important factor responsible for the quality of energy production forecasts in wind farms is the accurate wind speed forecast. An extensive statistical analysis of meteorological data (NWP) from 16 base nodes of the "300" grid in the "Łódź" area was made. The intention of the statistical analysis was to select potential explanatory variables for models predicting wind speed in the remaining 206 nodes of the grid’s mesh. Next, tests of selected prognostic methods were performed in order to compare their effectiveness with bilinear method which is not computationally complex. It should be emphasized that the main problem in spatial wind speed forecasting is the very large number of nodes for which the forecasts are calculated. As a consequence, more advanced and computationally complex forecasting methods cannot be applied in practice due to too long calculations time and difficulties in huge amounts of data processing. Conclusions with proposals of preferred forecasting methods that could be used in practice were developed.

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A comparative study of forecasting methods for sporadic demand in an auto service station

International Journal of Business Forecasting and Marketing Intelligence ◽

10.1504/ijbfmi.2019.099009 ◽

2019 ◽

Vol 5 (1) ◽

pp. 56 ◽

Cited By ~ 1

Author(s):

Rahul S. Mor ◽

Jitendra Nagar ◽

Arvind Bhardwaj

Keyword(s):

Comparative Study ◽

Service Station ◽

Forecasting Methods

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A comparative study on short-term PV power forecasting using decomposition based optimized extreme learning machine algorithm

Engineering Science and Technology an International Journal ◽

10.1016/j.jestch.2019.03.006 ◽

2020 ◽

Vol 23 (1) ◽

pp. 156-167 ◽

Cited By ~ 8

Author(s):

Manoja Kumar Behera ◽

Niranjan Nayak

Keyword(s):

Comparative Study ◽

Extreme Learning Machine ◽

Short Term ◽

Learning Machine ◽

Power Forecasting

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Study on a Service Mechanism that Wind/Photovoltaic/Energy Storage Power Station Help Wind Farms Tracking Generation Schedule Curve

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.860-863.1909 ◽

2013 ◽

Vol 860-863 ◽

pp. 1909-1913

Author(s):

Hai Xiang Xu ◽

Peng Wang ◽

Xiao Meng Ren

Keyword(s):

Energy Storage ◽

Wind Power ◽

Wind Farm ◽

Wind Farms ◽

Power Station ◽

Forecasting Accuracy ◽

Wind Power Forecasting ◽

Assessment Standard ◽

The Impact ◽

Power Forecasting

At present, the technology of wind power forecasting isn‘t mature enough in china, so some grid-connected wind farms will be assessed when theirs power forecasting accuracy cant reach the assessment standard. In response to the situation, combined with the characteristics of WPSPS and wind farms, this paper designs a service mechanism that WPSPS help wind farms tracking generation schedule curve, namely, encouraging WPSPS to supply output compensation service for wind farm by market means to increase the accuracy of wind power forecasting. By this mechanism, not only WPSPS and wind farms will achieve win-win, but also the impact on the grid caused by fluctuations of wind powers output will reduce.

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