Arkos

Inspiration

We were particularly moved by how energy inefficiency disproportionately impacts lower-income households and small businesses that lack access to sophisticated energy optimization tools. The growing Green Technology and Sustainability Market further convinced us that AI-driven energy solutions could create both environmental and economic benefits.

What it does

Arkos implements a comprehensive energy analysis and advisory platform that helps businesses and households reduce their energy costs through dual AI intelligence. Rather than functioning as a management system that directly controls energy usage, Arkos serves as a sophisticated analytical and advisory tool that empowers users to make informed decisions about their energy consumption.

How we built it

Frontend: React.js with TailwindCSS - For building a responsive, intuitive user interface. Chart.js - For creating interactive energy usage visualizations. Backend: Flask API - For creating RESTful endpoints that connect all components LSTM model - For timeseries forecasting of energy demand. Google's Gemini API - For embedding generation and natural language processing. ChromaDB - For vector storage of document embeddings. PyMuPDF - For extracting content from PDF documents. Python - For data processing and model implementation. Data Processing: Pandas and NumPy - For data manipulation and numerical computations. TensorFlow - For implementing neural network components

Challenges we ran into

One of our biggest hurdles was integrating both machine learning models with our frontend. The LSTM energy prediction model and the RAG-based document analysis system each had different output formats and processing requirements, making it challenging to create a unified API that could serve both to the React frontend. We had to restructure our API endpoints and implement consistent data formatting to ensure the models' outputs could be properly visualized and interacted with through our user interface. This integration required significant coordination between our frontend and backend to align on data and visualization requirements while maintaining the unique capabilities of our dual AI approach.

Accomplishments that we're proud of

We're most proud of our successful implementation of dual intelligence that combines predictive analytics with document understanding. Our system can forecast energy demand with great accuracy while also extracting contextual information from complex documents to provide more comprehensive recommendations. We've created an intuitive dashboard that makes sophisticated energy analytics accessible to non-technical users. The RAG based chatbot can answer nuanced questions about energy usage patterns and provide specific, actionable recommendations based on both historical data and document context.

What we learned

We learned how to implement and optimize LSTM models for time-series forecasting, how to build effective RAG systems for document intelligence, and how to create seamless integrations between different AI components. We also discovered the importance of user-centered design in making complex data actionable for everyday users. Additionally, we gained deep insights into energy consumption patterns and the factors that drive energy costs in different contexts.

What's next for Arkos

We'll focus on improving our LSTM model accuracy by implementing attention mechanisms and optimized hyperparameters through systematic tuning. We plan to enhance our model architecture with increased hidden units and more sophisticated data preprocessing pipelines to better capture the complex patterns in energy consumption data, increasing our prediction accuracy.

Built With

• ai
• chart.js
• chromadb
• lstm
• ml
• numpy
• pandas
• pymupdf
• python
• react
• tailwindcss