competition link: https://eco.ca/work-integrated-learning/100-hour-net-zero-student-challenge/
Optimize energy usage in buildings using AI-driven data analysis, application that gathers data from building sensors to optimize energy consumption using predictive analysis by TensorFlow.
In recent times, energy usage continues to surge and the challenge of reducing GHG emissions by saving energy in different ways is becoming increasingly important. Implementing solutions in cities and buildings is an easy and efficient way to reduce energy consumption on a large scale.
Current energy management systems in buildings often lack the control or the data insight on what parts of the building cause the most energy and are sometimes lacking in detailed analysis. A system to easily monitor, categorize and assess trends and usage rates is needed for effective energy savings as a whole.
In order to improve the energy monitoring and management of buildings we propose to provide an energy management app that gathers data from IoT sensors placed within buildings in order to monitor energy usage, provide data analysis on the energy data with trends and peak usage times and then provide suggestions for large overall energy savings.
Our proposed solution involves the development of a user-friendly app called Connect where we leverage open-source AI tools for data analysis. The app will serve as a tool for tracking energy usage a data analysis tool for energy breakdown and usage and either connecting directly by wifi or giving suggestions to building. This strategic utilization enhances the app's capabilities, aiming to improve user experience by providing insightful analysis of energy usage through advanced data analysis technology. The app’s software can further be implemented across buildings for large overall energy savings.
