Data integration and prediction models of photovoltaic production from Brazilian northeastern
This work addresses energy management for electric utilities by providing better production estimates, but it appears incremental as it applies existing statistical, probabilistic, and AI methods to a specific regional dataset.
The paper tackled the problem of estimating photovoltaic energy production in Brazil's northeastern region by integrating satellite data with geospatial and meteorological variables, resulting in models that improve production gains and facilitate monitoring of new enterprises.
All productive branches of society need an estimate to be able to control their expenses well. In the energy business, electric utilities use this information to control the power flow in the grid. For better energy production estimation of photovoltaic systems, it is necessary to join multiples geospatial and meteorological variables. This work proposes the creation of a satellite data integration platform, with production estimation models, base stations measurement and actual production capacity. This work presents statistical, probabilistic and artificial intelligence models that generate spatial and temporal production estimates that could improve production gains as well as facilitate the monitoring and supervision of new enterprises are presented.