The data points of certain basic information about your PV systems, such as the number of systems and components, are not counted. The frequency of such queries is irrelevant.
Detailed information about the PV system, real-time data (power flows) and the current weather data are counted as one data point per response. The frequency of the queries can be freely chosen depending on the application purpose (e.g. every 30seconds for real-time data, once per hour for current weather data, etc.)
Historical and aggregated energy data (e.g. production and consumption, as well as forecast data) and service messages are counted as data points per value and per timestamp. A single value such as energy fed into the grid per month would be counted as 12 data points for the period of the last 12 months. If total consumption and self-consumption were queried for a period of 10hours (considering the default time logging interval of 5 minutes set on the inverter) the number of data points can be calculated as follows:
60 minutes / 5 minutes log interval = 12 data points per hour.
Multiplying these 12 data points by the query period of 10 hours results in 120 data points per queried value. Since we are querying two values in our example the final sum of the data points would be 240.
Below are some sample calculations for various applications.
Data query for a PV system with a Smart Meter
Power flow data is queried at the PV system level (not at the inverter level). The data is requested every 60 seconds for 24 hours per day.
The amount of data points per month is calculated as follows:
60 queries/hour x 24 hours x 30 days = 43,200 data points per month
Data query for a PV system with a Smart Meter
Aggregated energy data for the three API channels gridfeedin, gridpower and selfconsumption is queried. The data is requested hourly, 24 hours per day for daily, monthly, annual and total values.
The amount of data points per month is calculated as follows:
1 query/hour x 4 time intervals x 3 channels x 24 hours x 30 days = 8,640 data points per month
Data query for a PV system with a Smart Meter
Historical energy data is queried for the three API channels gridfeedin, gridpower and selfconsumption. The data is requested every 5 minutes (corresponds to the standard setting of Fronius inverters), 24 hours per day
The amount of data points per month is calculated as follows:
12 queries/hour x 3 channels x 24 hours x 30 days = 25,920 data points per month
Data query for a PV system with 10 inverters
Historical data is requested for one API channel (produced energy) is queried.
The query provides the data at 5-minute intervals for 14 hours a day (usual operating time of the inverter).
The amount of data points per month is calculated as follows:
12 queries/hour x 10 inverters x 1 channel x 14 hours x 30 days = 50,400 data points per month
Data query for a PV system with smart meter and storage system for a monitoring solution
Since a combination of several different data queries is common in practice, the data query for the provision of a photovoltaic visualization application for end customers is shown in this application example.
Power flow data is queried at the PV system level. The data is requested every 15 seconds for 30 minutes a day.
The amount of data points per month is calculated as follows:
240 queries / hour x 0.5 hours x 30 days = 3,600 data points per month
In addition, historical data are requested every 5 minutes for 24 hours.
The amount of data points per month is calculated as follows:
12 queries / hour x 5 channels x 24 hours x 30 days = 43,200 data points per month
Furthermore, aggregated energy data for the 5 API channels grid feed-in, grid power, battery charging, battery discharging and self-consumption are queried. The query takes place once a day for the daily, monthly, annual and total values.
The amount of data points per month is calculated as follows:
1 query / day x 4 time intervals x 5 channels x 30 days = 600 data points per month
For this use case, the result is a total of 47,400 data points per month. The data points calculated in the application examples can be multiplied depending on the number of PV systems. The calculationof the data points is always linear.