Quantum mechanics

Algorithm

World Wide Web

Engineering

Coordinate descent

Biology

Vector autoregression

Statistics

Multivariate statistics

Time series

Wind power forecasting

Economics

Power (physics)

Computer science

Mathematical optimization

STAR model

Paleontology

Estimation

Physics

Mathematics

Estimator

Management

Autoregressive model

Electric power system

Overfitting

Econometrics

Autoregressive integrated moving average

Artificial intelligence

Series (stratigraphy)

Lasso (programming language)

Wind power

Artificial neural network

Machine learning

Univariate

Electrical engineering

International Journal of Forecasting

Wind power forecasts with lead times of up to a few hours are essential to the optimal and economical operation of power systems and markets. Vector autoregression (VAR) is a framework that has been shown to be well suited to predicting for several wind farms simultaneously by considering the spatio-temporal dependencies in their time series. Lasso penalisation yields sparse models and can avoid overfitting the large numbers of coefficients in higher dimensional settings. However, estimation in VAR models usually does not account for changes in the spatio-temporal wind power dynamics that are related to factors such as seasons or wind farm setup changes, for example. This paper tackles this problem by proposing a time-adaptive lasso estimator and an efficient coordinate descent algorithm for updating the VAR model parameters recursively online. The approach shows good abilities to track changes in the multivariate time series dynamics on simulated data. Furthermore, in two case studies it shows clearly better predictive performances than the non-adaptive lasso VAR and univariate autoregression.

Online adaptive lasso estimation in vector autoregressive models for high dimensional wind power forecasting