Solar PV inverters often have their own web-based monitoring solutions. However some of these do not make it easy to view current generation or consumption due to refresh delays. Out of the box monitoring is usually good for looking at long-term time periods however lacks the granularity to see consumption of appliances over the short term.
Realtime monitoring of Solar generation and net export helps to maximise self-consumption. For example coordinating appliances to make best use of solar PV.
Existing inverter monitoring does not show granular data over recent history – for example, to be able to tell when a dishwasher has finished its heating cycle and whether another high-consumption appliance should be turned on:
This sample android application allows realtime monitoring whilst charting consumption, generation and net export:
The chart shows recent data over time and is configurable for SMA and Enphase inverters. In both cases the local interface of each inverter is used to pull raw data:
For this demo, home solar PV generation data has been obtained from United Energy’s Energy Easy portal in CSV format. For the sake of convenience in dealing with the raw data which usually comes in half-hourly intervals, this data has been loaded in to a Pentaho data warehouse instance (more details in a later post, perhaps!) and converted to day-by-day figures.
Analysis can help explore whether solar panels are getting less efficient over time, or even determine what a “good” day of production is like in summer vs winter (by looking at the relevant frequency of each in the histogram).
Drag and scroll date region which affects histogram above:
Changeable histogram buckets:
Snap-shotting of one selected date range for visual comparison with another (e.g. summer vs winter comparison):
With the benefit of smart electricity meters it’s possible to obtain hourly data showing household consumption in KWh. I downloaded this dataset for my own house in CSV format from United Energy’s EnergyEasy portal.
With some massaging, the data can be formatted to a structure which which makes aggregation easier. The excellent tool OpenRefine made this task easier, effectively unpivoting half-hourly measures which were in many columns into a single column, so that the data looks like this:
During which hours of the day is the highest average energy consumption? Is this different in summer vs winter? Has this changed from 2012 to 2013?
Has the minimum energy consumption overnight changed? Is the new (and slightly annoying) energy saving power board purchased in mid 2013 doing its job to reduce standby power use?
During which hours of the day is power usage the most variable?
Selectable date range – e.g. to compare a rolling 12 month period. This uses a “context” graphics section in D3.js with brush functionality to trigger realtime recalculation of data in the “focus” section when a user selects a range using their mouse. The live update of the hourly consumption profile means it’s easy to see trends over time in the “focus” area of the screen (shown in the following point):
Plotting of max / min / mean / standard deviation of KWh consumption per hour of the day:
“Snapshotting” of date range – e.g. to compare two consecutive years in an interactive way: