Beat The Heat

Team Name

Crisp4Good

Team Members

Project Description

Our project Beat The Heat aims to communicate the urban heat challenges for the residents in the Parramatta LGA during summer.

Visualising the data to provide meaningful insights, Beat The Heat conveys the correlation between local temperature and energy consumption for sites in Parramatta, and suggests the need to be forward-thinking to ensure adequate energy resources to meet future needs.

We visualize this data in the form of an interactive Power BI visualization. Our visualisations convey the amount of power required by air conditioners in the local government area of Parramatta.

Using our visualization users can view historical data on energy consumption and temperature data based on the selected time period or location.

We used the Parramatta temperature dataset to estimate the energy consumption of running an air conditioner to cool Paramatta households on a hot summers day. Also, we used census data to determine the total energy expenditure by air conditioners per hour for all households in Parramatta.

To determine how much power would be required, the energy company could calculate the amount of power used on a day where air conditioners would not be used as much (on a relatively cool day) and then use our data to predict how much power exactly they would need to supply on a sweltering day, thus reducing the risk of a power outage.

Future Additions:
If we were to develop this product in the future, we would look at data with more specific daily results such as average amount of hours spent running the air conditioner per day rather than per year. This would allow us to cater our results to various parts of the year where temperatures can vary a lot.

We would also combine data from the Bureau of Meteorology to expand our predictions to various locations around the country, especially rural areas as they would be unlikely to have air conditioners.

Data Story

Our visualisations predict the amount of power required by air conditioners in the local government area of Parramatta.

We used the Parramatta temperature data set to determine how the maximum temperature changed everyday and combined this with the energy cost of running the air conditioner at 35C to calculate the energy cost when running at the maximum temperature per day. We then used the census data to determine how many households there are in each of suburbs where the data was reported from to calculate total energy expenditure by air conditioners per hour for all suburbs.

Another dataset that was used showed the amount of hours spent by each household per year either cooling or heating the house. Since the data we received was from January, we only looked at the cooling hours to determine how long a household would spend cooling their house per day. This led to much better energy prediction results.

One use case of this data would be to determine how much extra power would need to be inserted into the grid on days where the temperature is very high.

To determine how much extra power would be required to be inserted, the energy company could calculate the amount of power used on a day where air conditioners would not be used as much (on a relatively cool day) and then use our data to predict how much power exactly they would need to supply on a hot day. Then they would be able to predict how much more power they would need to inject in the grid on hot days.

Another use case would be for power companies to reduce the price per unit of power supplied when it is in demand which would convince more people to leave the air conditioning on for longer. This could potentially lead to better revenues.