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.

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The prediction of solar potential is an important step toward the evaluation of PV plant production for the best energy planning. In this study, the discrete Kalman filter model was implemented for short-term solar resource forecasting one the Dakar site in Senegal. The model input parameters are constituted at a time t of the air temperature, the relative humidity and the global solar radiation. The expected output at time t+T is the global solar radiation. The model performance is evaluated with the square root of the normalized mean squared error (NRMSE), the absolute mean of the normalized error (NMAE), the average bias error (NMBE). The model Validation is carried out by means of the data measured within the Polytechnic Higher School of Dakar for one year. The simulation results following the 20 minute horizon show a good correlation between the prediction and the measurement with an NRMSE of 4.8%, an NMAE of 0.27% and an NMBE of 0.04%. This model could contribute to help photovoltaic based energy providers to better plan the production of solar photovoltaic plants in Sahelian environments.

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