Accurate photovoltaic (PV) production forecasting is an important feature that can assist utilities and plant operators in the direction of energy management and dispatchability planning. Although numerous forecasting models have been reported in literature, the challenge of improved accuracy remains unsolved. In this work, a dayahead PV power model utilising a hybrid approach is derived in order to feed to an Artificial Neural Network (ANN) and a linear regression model trained for PV power forecasting. The study focuses on improving the forecasting accuracy by employing machine learning and linear regression models that could record the behaviour of the PV system. The performance of the hybrid model was assessed against a historical test set exhibiting normalised mean square error (nRMSE) of 6.15%.