A robust forecasting framework based on the Kalman filtering approach with a twofold parameter tuning procedure: Application to solar and photovoltaic prediction. (June 2016) - Soubdhan, Ted; Ndong, Joseph; Ould-Baba, Hanany; Do, Minh-Thang
Highlights: We have presented in this work a robust forecasting method based on the Kalman filter combined with a probabilistic initialization, Expectation Maximisation (EM) or Auto Regressive (AR) based. The model is built to be performed with both univariate or multivariate data. We test here it's ability to forecast solar radiation from 1 min to one hour ahead, and Photovoltaic power production for one hour ahead. The influence of exogenous inputs on the forecasting error is also evaluated. The model shows good forecasting skills on time scale of 30 min to one hour, but also on short time scale such as 1, 5 and 10 min for both GHI and PV. Abstract: This paper presents a framework which relies on the linear dynamical Kalman filter to perform a reliable prediction for solar and photovoltaic production. The method is convenient for real-time forecasting and we describe its use to perform these predictions for different time horizons, between one minute and one hour ahead. The dataset used is a set of measurements of solar irradiance and PV power production measured in a sub-tropical zone: Guadeloupe. In this zone, fluctuating meteorological conditions can occur, with highly variable atmospheric events having severe impact in the solar irradiance and the PV power. In such conditions, heterogeneous ramp events are observed making difficult to control and manage these sources of energy. The present work hopes to build a suitable statistical method, based on bayesian inference and state-space modeling, able to predict the evolution of solar radiation and PV production. We develop a forecast method based on the Kalman filter combined with a robust parameter estimation procedure built with an Auto Regressive model or with an Expectation–Maximisation algorithm. The model is built to run with univariate or multivariate data according to their availability. The model is used here to forecast the univariate solar and PV data and also PV with exogenous data such as cloud cover and air temperature. The accuracy of this technique is studied with a set of performance criterion including the root mean square error and the mean bias error. We compare the results for the different tests performed, from one minute to one hour ahead, to the simple persistence model. The performance of our technique exceeds by far the traditional persistence model with a skill score improvement around 39% and 31%, respectively for PV production and GHI, for one hour ahead forecast.