Predicting multivariate time series in real time with confidence intervals: Applications to renewable energy

摘要

We extend our earlier work on predicting a univariate time series in real time with confidence intervals (Hirata, et al., Renew. Energy 67, 35 (2014)) to a multivariate time series. We realize this extension by using the “p-norm” where p is smaller than 1. We compare the performance when p is 0.5 with that when p is 2 using solar irradiation data and wind data measured all over Japan.