Autoregressive Modeling of Time Series in Renewable Energy Systems

Autoregressive (AR) models have been increasingly used to forecast solar irradiance and estimate state-of-charge (SOC) in energy storage systems (ESSs). This work aims to apply AR models to real SOC and solar irradiance measurements. We determine the AR model coefficients using the modified covariance method while we estimate the model order using three standard metrics: final prediction error (FPE), Akaike information criterion (AIC), and criterion autoregressive transfer function (CAT). Additionally, we also evaluate the model order that minimizes the spectral Kullback-Leibler divergence between the data periodogram and the AR power spectral density estimates corresponding to the three metrics. We assess the model accuracy using root mean squared error (RMSE) and demonstrate that the lowest RMSE is generally achieved with higher model orders. The comparative analysis of the data collected under different weather conditions reveals that partly cloudy conditions are the most challenging.