Most influential parametrical and data needs for realistic wind speed prediction


Artificial Neural Network based Yearly Auto-Regressive (ANNYAR) model proposed for wind speed forecasting.

Use of Levenberg–Marquardt back propagation algorithm for modelling and analysis purpose.

Parametrical dependence of wind speed increases for higher time horizons.

Data needs for wind speed prediction depends on daily/hourly trends of weather parameters.

Proposed model has been compared with persistence and 1st order wind speed prediction model.


Depleting fossil fuel reserves and increasing global weather concerns has diverted mankind to look out for clean and green reserves of energy ever since the beginning of last decade. Wind holds a major role in satisfying our energy needs, however, its use as an alternate power source accounts for various issues such as deregulation of supply, frequency instability, etc. In order to nullify such effects, power engineers need to have an idea of futuristic weather conditions, especially the wind speed trend. Numerical Weather Prediction (NWP) tools such as Yearly Auto-Regressive (YAR) models when deployed for medium-term wind speed forecasting have proved their effectiveness. In this paper Artificial Neural Network based Yearly Auto-Regressive (ANNYAR) model have been used to figure out the most influential parameter’s affecting wind prediction and corresponding range of yearly data set required for Time Horizon (TH) extending from 6 to 96 h. Data from area in and around ‘VABB airfield Mumbai’ has been incorporated for modelling and analysis purpose.


  • Artificial neural network;
  • Auto regressive;
  • Multi-layer perceptron neural network;
  • Numerical weather prediction;
  • Parametrical combination;
  • Time horizon

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