Bootstrapped Multi-Model Neural-Network Super-Ensembles for Wind Speed and Power Forecasting

Zhongxian Men,Yongqian Liu,Hua Ji,Eugene Yee,Fue-Sang Lien

doi:10.4236/epe.2014.611029

Abstract

The bootstrap resampling method is applied to an ensemble artificial neural network (ANN) approach (which combines machine learning with physical data obtained from a numerical weather prediction model) to provide a multi-ANN model super-ensemble for application to multi-step-ahead forecasting of wind speed and of the associated power generated from a wind turbine. A statistical combination of the individual forecasts from the various ANNs of the super-ensemble is used to construct the best deterministic forecast, as well as the prediction uncertainty interval associated with this forecast. The bootstrapped neural-network methodology is validated using measured wind speed and power data acquired from a wind turbine in an operational wind farm located in northern China.

Highlights

IntroductionThere has been an increasing emphasis towards a greater use of renewable energy (e.g., solar, wind, geothermal) as a strategy to reduce greenhouse gas emissions and to mitigate climate change
There has been an increasing emphasis towards a greater use of renewable energy as a strategy to reduce greenhouse gas emissions and to mitigate climate change
In addition to these measured time series, wind speed data at turbine hub height obtained from a numerical weather prediction model was available and this information was used as the exogenous input for artificial neural network training

Summary

Introduction

There has been an increasing emphasis towards a greater use of renewable energy (e.g., solar, wind, geothermal) as a strategy to reduce greenhouse gas emissions and to mitigate climate change. The latter has significant implications for unit commitment and determination of scheduling and dispatch decisions (economic dispatch) needed for the optimal utilization of wind energy within a mixed power system. Numerical weather prediction models have a number of limitations, including limited spatial resolution resulting in a coarse representation of the local terrain [9] To overcome the latter problem, Liu et al [10] considered the possibility of coupling a synoptic scale flow model to a large-eddy simulation model for wind energy applications and Li et al [11] recently introduced a short-term wind forecasting methodology based on the use of CFD pre-calculated flow fields. The confidence bands in these predictions can be determined, which can be used to provide a more rigorous uncertainty assessment in wind speed and power forecasting

Bootstrapping Ensembles of Artificial Neural Networks

Data Preparation

Results

Summary and Conclusions