Solar energy is a clean, available and renewable source of energy. Deployment of photovoltaic panels is trending to take advantage of solar energy for electrical power generation. Intermittent nature of solar energy results in variable generated power. This uncertainty could be alleviated by taking advantage of energy storage systems and accurate solar power forecast. This thesis aims to implement a solar forecast module to take part in an optimized Energy Management System (EMS). Different solar power prediction methods are studied including statistical methods, sky imagers, satellite imaging and Numerical Weather Prediction (NWP). Artificial neural networks (ANNs) which are a subset of statistical methods are chosen as prediction method to satisfy requirements imposed by EMS.