Research and Academic Projects.

Employer: DECOM/FEEC/UNICAMP

Function details/Program: We are objectively destined to develop the photovoltaic solar generation prediction system on two fronts:
(a) Communication and sensing infrastructure (i.e. wireless sensor network and controls systems for signal processing).

• The definition of the use of a wireless sensor network (WSN) technology initially requires two initial investigations. First is the applicability analysis of sensors, sensor node and sensing methods for global solar irradiance (analysis of aspects like Lux information, A/D converter, resolution, sensitivity, uncertainties, calibration, purchase specification, etc.). Second is the applicability analysis of wireless sensor network topology (analysis of aspects like type of net-work (i.e Wi-Fi, Zigbee), transmission range, access points, network layout, the connection between the sensor nodes, message protocol, energy consumption, purchase specification, etc.).

• After these two initial investigations, it would be possible to study the integration and optimization solutions for WSN infrastructure considering probabilities forecasting approaches. From this point, considering that a prototype can be easily implemented a profound investigation into energy management schemes and hardware solutions in energy harvesting wireless sensor networks can be performed with the objective of providing an energy efficiency condition for the WSN.

(b) Predictive analysis of the use of existing publically available solar databases.

• Initially, a profound investigation into sources of solar (panel and irradiance) data, Market data, Simulation data, Tools to manage big data (i.e. MongoDB, Cassandra, CouchDB), NoSQL and SQL for solar database (possible conclusion of this report is the lack and/or lost data) and forecasting’s CI methods, should be developed with the objective of consolidating the predictive analytics model, that can be used to identify useful correlated patterns (considering the lack and/or lost data) to predict the global solar irradiance, or, photovoltaic solar generation based on past data.

• The virtual work environment will be very important, it should allow the manipulation of analog signals, apply discretization techniques, and signal to process. It is believed that applying forecasting’s CI methods may have better efficacy on already identified and processed signal patterns.

• After that, the integration of these two fronts (a and b), can be accomplished by a cloud platform, preferably of an industrial and control level (i.e. PROFICLOUD Technology). One of the ways to add value to a piece of prediction information, thus increasing its reliability, is to use environments that can generate and communicate this prediction information through virtual devices. Virtual devices have no physical counterpart – they only exist within the cloud, where they function exactly like a real IO (Input/Output) device. Unlike real devices, their IO dates do nor only originate from sensors and control actuators; instead, it can represent information from the specific cloud service. Given this system design, it is possible, for example, to obtain weather information from the internet and make it available to the control system via the virtual device. This function can generate added value in photovoltaic solar generation, because it also allows for precise yield forecasts, among other things.