Simplified Method to Derive the Kalman Filter Covariance Matrices to Predict Wind Speeds From a NWP Model

Authors

Conor Lynch, Process, Energy and Transport Engineering Department, Cork Institute of Technology, Bishopstown, Cork, IrelandFollow
Michael J. O'Mahony, Process, Energy and Transport Engineering Department, Cork Institute of Technology, Bishopstown, Cork, IrelandFollow
Ted Scully, Computing Department, Cork Institute of Technology, Bishopstown, Cork, IrelandFollow

Document Type

Article

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.

Disciplines

Engineering | Power and Energy

Publication Details

Energy Procedia, vol 62, 2014.

Abstract

This paper espouses a simplified approach to predict wind speed 1 hour ahead for a wind turbine located on the Cork Institute of Technology (CIT) college campus by utilising a Kalman Filter to predict the bias between a campus based turbine and the output from a Numerical Weather Prediction (NWP) model for Cork Airport.

Furthermore, this paper investigates the optimum number of samples required (n) in a fixed sampling interval process to derive the covariance matrix of the system equation Qt and the covariance matrix of the observation equation Rt . The main contents of this paper include wind speed analysis, state space analysis and Kalman Filtering application to Numerical Weather Prediction (NWP) data for wind speed prediction.

Recommended Citation

Conor Lynch, Michael J. OMahony, Ted Scully, Simplified Method to Derive the Kalman Filter Covariance Matrices to Predict Wind Speeds from a NWP Model, Energy Procedia, Volume 62, 2014, https://doi.org/10.1016/j.egypro.2014.12.431.