Complex-valued time series based solar irradiance forecast

Abstract

A new method for short-term probabilistic forecasting of global solar irradiance from complex-valued time series is explored. The measurement defines the real part of the time series while the estimate of the volatility is the imaginary part. A complex autoregressive model (capable to capture quick fluctuations) is then applied with data gathered on the Corsica island (France). Results show that even if this approach is easy to implement and requires very little resource and data, both deterministic and probabilistic forecasts generated by this model are in agreement with experimental data (root mean square error ranging from 0.196 to 0.325 considering all studied horizons). In addition, it exhibits sometimes a better accuracy than classical models such as the Gaussian process, bootstrap methodology, or even more sophisticated models such as quantile regression. Many studies and many fields of physics could benefit from this methodology and from the many models that could result from it.