The current document contains the communication and dissemination plan conducted and organized by WP6 leader: SEMIDE. The objective of this document is to organize the communication and dissemination activities that will be carried out for the entire duration of SUPROMED project by all partners to attract and inform the targeted audience/stakeholders and general public on project services, activities, progress and results.
This is achieved through identifying the following points: objectives (why), target audience (who), key messages (what), media supports and information distribution channels (how), materials (with what), timing (when) and resources (both financial and human resources). Furthermore, the plan contains the methodology of the dissemination and communication management and the implementation plan. Also, it has set some achievement indicators to evaluate the effectiveness and the success of these activities. To facilitate communication between partners, some tools and rules have been set up and are detailed in chapter 3 ‘Internal communication’.
Abstract: Global energy consumption and costs have increased exponentially in recent years, accelerating the search for viable, profitable, and sustainable alternatives. Renewable energy is currently one of the most suitable alternatives. The high variability of meteorological conditions (irradiance, ambient temperature, and wind speed) requires the development of complex and accurate management models for the optimal performance of photovoltaic systems. The simplification of photovoltaic models can be useful in the sizing of photovoltaic systems, but not for their management in real time. To solve this problem, we developed the I-Solar model, which considers all the elements that comprise the photovoltaic system, the meteorologic conditions, and the energy demand. We have validated it on a solar pumping system, but it can be applied to any other system. The I-Solar model was compared with a simplified model and a machine learning model calibrated in a high-power and complex photovoltaic pumping system located in Albacete, Spain. The results show that the I-Solar model estimates the generated power with a relative error of 7.5%, while the relative error of machine learning models was 5.8%. However, models based on machine learning are specific to the system evaluated, while the I-Solar model can be applied to any system. Keywords: photovoltaic energy; irrigation; solar pumping; real-time management