Options to Improve the Quality of Wind Generation Output Forecasting with the Use of Available Information as Explanatory Variables
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Abstract
Development of wind generation, besides its positive aspects related to the use of renewable energy, is a challenge from the point of view of power systems’ operational security and economy. The uncertain and variable nature of wind generation sources entails the need for the for the TSO to provide adequate reserves of power, necessary to maintain the grid’s stable operation, and the actors involved in the trading of energy from these sources incur additional of balancing unplanned output deviations. The paper presents the results of analyses concerning the options to forecast a selected wind farm’s output exercised by means of different methods of prediction, using a different range of measurement and forecasting data available on the farm and its surroundings. The analyses focused on the evaluation of forecast errors, and selection of input data for forecasting models and assessment of their impact on prediction quality improvement.
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References
Sweeney C., Lynch P., Nolan P., Reducting
errors of wind speed forecasts by an
optimal combination of post-processing
methods, Department of Meteorology
and Climate Centre, Dublin, 2011.
Prondziński Z., Rubanowicz T.,
Zryczałtowana usługa operatora
handlowo-technicznego na potrzeby
rozwoju energetyki wiatrowej w Polsce,
Acta Energetica 2004, nr 19.
Hernandez L., Artificial Neural Network
for Short-Term Load Forecasting
in Distribution Systems, Energies
, 7 1576-1598, ISSN1996-1073,
marzec 2014, praca zbiorowa.
Perez-Llera C., Fernandez-Baizan
M.C., Gonzalez del Valle V., Local
Short-Term Prediction of Wind Speed:
A Neural Network Analysis, Universidad
Politecnica de Madrid, Spain.
Moghaddas-Tafreshi, S.M., Panahi D.,
One-hour-ahead forecasting of wind
turbine power generation using artificial
neural networks, University of
Technology, Teheran, Iran.
Mao J., Zhang X., Li J., Wind power forecasting
based on the BP neural network,
Beifang University of Nationalities,
Yinchuan, China.
Sweeney C. i in., Post-processing
COSMO output for improved wind forecast,
Meteorology and Climate Centre,
Universtity College Dublin, Ireland,
April 2012.
Selcuk Nogay H., Akinci T.C.,
Eidukeviciute M., Application of artificial
neural networks for short term wind speed
forecasting in Mardin, Turkey, Journal
of Energy in Southern Africa, November
, Vol. 23, No. 4.