Medium and Long-Term Parametric Temperature Forecasting using Real Meteorological Data

Temperature forecasting based on meteorological data is the key stage for an accurate estimation of PV power production and demand-side management leading to better grid stability. Typically, weather forecasting is the prediction of weather parameters for seconds until months ahead based on the historical weather database. Thus, researchers create several approaches to maximize the accuracy of these predictions and increase the period of estimation. This paper proposes a new medium and long-term temperature forecasting approach based on Multi Inputs Single Output (MISO) model base on empirical equations. The parameters of the proposed model are computed using a Recursive Least Squares (RLS) method. Using a set of real meteorological data, simulation results are presented to show the high accuracy of the proposed temperature forecasting approach.