We released a range of software tools to help consumers monitor, visualise and understand their energy consumption.
Demand for electricity in the UK varies throughout the day, and thus, the mix of generators supplying this electricity continually changes. As a result, the carbon intensity of the electricity - the quantity of CO2 produced for 1 kWh of electricity consumed - also varies continually. Deferring your use of electricity to off-peak times, when the carbon intensity is low, can help reduce your carbon footprint.
GridCarbon uses up-to-date generation mix data made available by ELEXON. GridCarbon presents a summary of the generation mix data broken down into Gas (CCGT + OCGT), Coal (COAL), Nuclear (NUCLEAR), Wind (WIND) and Hydro (NPSHYD). This data is converted into a carbon intensity value by weighting the proportions from each generation type using the values below.
|Symbol||Fuel Type||Carbon Intensity
|CCGT||Closed cylce gas turbine||360|
|OCGT||Open cylce gas turbine||480|
|NPSHYD||Non-pumped storage hydro||0|
Finally, the resulting figure is multiplied by 1.07 in order to reflect the losses in the transmission and distribution networks.
We can also calculate and predict carbin intensity for individual energy suppliers who mix their own generation with grid supplied electricity.
Building Energy Monitoring
We have implemented a number of live energy displays using energy data feeds from our own office building (live versions are optimised for 1280x1024 display screens and require an HTML5 compliant browser):
We also log the energy consumption of individual desktop computers within the building, and here is a live graphic showing this data:
Finally, no discussion of energy feedback would be complete with an ambient orb (or cube in our case). Our own version is a USB powered ambient feedback device based on an ATtiny45 microcontroller.
The data to drive these displays and devices comes from the building management system and a network of Zigbee Ploggs that measure the individual energy consumption of a number of devices within our lab.
To test the algorithms developed within the project we have implemented a thermostat with a time proportional intergral controller (interfacing with Zigbee temperature sensors and power switches) that learns the thermal properties of the system that it is controlling. These properties can then be used to optimise heating start times, or to perform demand side management of heating loads: