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

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.

Disciplines

Engineering | Power and Energy

DOI

10.1016/j.egypro.2014.12.431

Publication Details

Energy Procedia, vol 62, 2014.

Publisher

Elsevier

License Condition

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

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.