It is often the case that companies that operate solar farms are not the owners of those farms, and thus, don`t have a straightforward access to the detailed features of the plants in their portfolios. A company may operate a portfolio of solar farms located in various regions and countries. All of those farms are, however, owned by different owners and can be different in mechanical set up.
It is therefore useful to be able to onboard new farms to a portfolio from different locations and different owners in a straightforward way without knowing the specific details of those assets. Unlike typical traditional PV_Yield formulas which requires to know the detailed set up of a PV installation, our approach is data-driven. TIM™ is able to build predictive models fast and automatic without the need to know the explicit physical configuration of the PV plant.
TIM™ can build the predictive models from just the GPS coordinates of the PV plant and historical the production or output of the PV plant in the form of time series.
Explanatory variable candidates ( meteorological data ) are automatically included by TIM™based on the provided GPS location.
TIM™ unifies the whole process by allowing for a fully automatic model building process, requiring limited information from the operator and adding the required meteo data in one simple API call or through a click in our TIM™Studio 365.