42Adelaide: Lachlan
Energy4me is an application that pulls data from the National Energy Market, to better educate users on the most renewable times to use electricity. This would also work to alert users when it would be environmentally friendly to use their electric vehicle to send power to the grid (as vehicle to grid charging becomes more mainstream).
As an application, it becomes a push notification system based on the data from opennem.org, and bom data re prediction of weather patterns and their correlation to the type of electricity generated, thus encouraging users to utiltise electricity when it is most green.
Proposed data used in this project
NEM Data
This data is used in my project to display the current makeup of the energy supply to the grid, that in turn the consumers will be using. Based on this data, consumers are informed of the most renewable and cost effective time to use their electricity. In conjunction with this, as vehicle to grid charging becomes more widely available, it will allow my app to notify people when it would be climate friendly to plug in their vehicle to supply some power back to the grid.
Bureau of Meteorology Climate Data
In conjunction with NEM data, my application would compare previous weather data with respective NEM data from that time period, and based on that, compare the current weather, and make a prediction on the upcoming NEM values.
BOM - Data services for weather information
Using the forecast weather predictions provided through BOM, we can use this data to accurately predict the supply of renewable energy, when compared to similar past weather and NEM data.
ABS - Census Data
Using this data, my product would estimate and predict the future draws on the grid based on user uptake of EV's (based on the vehicle census data).
City of Hobart - Building Footprint Data
This data would be used, to help more business targeted clients with my solution, to use building data, and, along with information provided by the user, accurately predict best times for energy usage, and the costs associated with the energy use at different times.
Population Projections
Population projections are used in Energy4me, in conjunction with NEM (National Electricity Market), and current population census data, to predict the amount of draw the population will be putting on the grid.
Description of Use Using this data, my product would estimate and predict the future draws on the grid based on user uptake of EV's (based on the vehicle census data).
Description of Use This data would be used, to help more business targeted clients with my solution, to use building data, and, along with information provided by the user, accurately predict best times for energy usage, and the costs associated with the energy use at different times.
Description of Use Using the forecast weather predictions provided through BOM, we can use this data to accurately predict the supply of renewable energy, when compared to similar past weather and NEM data.
Description of Use In conjunction with NEM data, my application would compare previous weather data with respective NEM data from that time period, and based on that, compare the current weather, and make a prediction on the upcoming NEM values.
Description of Use This data is used in my project to display the current makeup of the energy supply to the grid, that in turn the consumers will be using. Based on this data, consumers are informed of the most renewable and cost effective time to use their electricity. In conjunction with this, as vehicle to grid charging becomes more widely available, it will allow my app to notify people when it would be climate friendly to plug in their vehicle to supply some power back to the grid.
Description of Use Population projections are used in Energy4me, in conjunction with NEM (National Electricity Market), and current population census data, to predict the amount of draw the population will be putting on the grid.
Go to Challenge | 9 teams have entered this challenge.
Go to Challenge | 3 teams have entered this challenge.
Go to Challenge | 4 teams have entered this challenge.