Overview
This project was developed for Kenter to design a scalable energy prediction and monitoring tool. The application visualizes measurement data, reconstructs missing information, and predicts future energy consumption. Our approach consisted of three phases: analyzing the current situation, concept development involving AI algorithms and prototypes, and evaluation with detailed reporting for further development.
Features
- Data Visualization: Displays energy measurement data in an intuitive dashboard.
- Data Reconstruction: Uses algorithms to fill in missing data points.
- Energy Prediction: AI models forecast future energy consumption trends.
- Scalable Architecture: Designed for future expansions and integrations.
Development Process
- Analysis of Current Situation: Researched existing systems and data infrastructure.
- Concept Development: Designed application architecture, selected AI algorithms, and created prototypes.
- Evaluation & Reporting: Summarized findings and designs as a foundation for the realization phase.
Gallery
Kenter Main Dashboard
Kenter Login Page
Technologies Used
- OpenTofu
- GitHub
- GitLab
- CI/CD Pipelines
- AI Algorithms
- Infrastructure as Code
CI/CD Pipeline Features
- Automates the process of building, testing, and deploying the web application.
- Uses OpenTofu to efficiently set up and manage cloud environments.
- Creates reliable and reproducible test environments to ensure consistency.
- Detects potential issues early through automated testing.
- Ensures the application is future-proof and easily transferable to other environments or cloud providers.
Team Members
- Magomed-Ali Dudayev (App Development)
- Myrthe Daniëls (App Development)
- Arne Muijshondt (AI)
- Arne De Bruyn (AI)
- Thomas Deboel (Pipeline & Cloud)
- Enis Haliti (App Development)