What does the SmartgridOne Controller predict?
The SmartgridOne Controller uses machine learning to predict the grid power of the near future (12h to 36h ahead of time, based on the available weather and price data). The prediction is based on historical data, for a given time, weekday and solar irradiation from weather data.
Tip
Why predict the grid power, and not the PV power and consumption power? Firstly, most control objectives care about what the final power at the grid is - because you get billed based on the energy you get from the grid - so this is also one of the most important parameters in the control algorithm. Secondly, the SmartgridOne Controller almost always has real, measured values from the grid energy meter. This is not always true for PV (not all inverters might be read out) and very often not for the base load consumption (this is usually a calculated value from the grid power, after subtracting all the values measured from the devices the SmartgridOne Controller communicates with). Predictions based on directly measured values are often more accurate.
Frequently asked questions
How much time does the EMS have to gather data for before predictions can be made?
Typically, within one to two days, the EMS can already detect patterns in your grid power. After one to two weeks, the predictions should be reasonably accurate.
Does the prediction algorithm take seasonal effects in account?
Yes, seasonal variations are accounted for.
Does the prediction algorithm take the orientation of PV panels into account?
Yes. Because the predicted grid power is affected by how much energy your PV installation actually produces (which is a function of both the solar irradiation and the orientation of the panels), the effect of the orientation of the panels is inherently included in the data the prediction is based on. As such, the orientation is accounted for.