Renewable energy production technologies are indispensable to the development of a clean and energy efficient built environment. Being dependent on climatic parameters, renewable energy production may vary causing a mismatch between energy demand and available production. Therefore, forecasting of renewable energy production allows the design and implementation of management schedules depending on expected production, thus assisting towards a more efficient and secure operation. The application of artificial neural networks (ANN) has been proved effective towards this aim. The present work is investigating the application of ANN for production forecasting of a concentrated solar power (CSP) fresnel system. The fresnel system production is optimum under clear skies and peak direct solar radiation. The system under investigation is connected to a thermal storage and its production is used to cover the heating/cooling loads of a building. The prediction can be used for scheduling the times that the system focalizes and de-focalizes for optimizing energy production and storage. The present work is relevant to researchers, engineers and developers of renewable energy production technologies that work on optimization of energy production and management. This work can be extended to smart grid energy management.