Hackathon Project: Solution for Energy Usage Modeling

Introduction

This repository contains the project submission for the hackathon where we developed a solution for energy usage modeling. Our solution aims to address a crucial need in the energy sector by providing accurate predictions and insights into energy consumption patterns.

Problem Statement

The lack of efficient energy usage modeling tools can lead to inefficiencies in energy management and planning. Our project aims to tackle this issue by leveraging data science and machine learning techniques to create a predictive model for energy usage.

Solution Overview

Our solution comprises several components, including data preprocessing, model development, and deployment. Here's a brief overview of each:

1. Data Preprocessing: We preprocess the raw energy usage data to clean, normalize, and transform it into a format suitable for model training.

2. Model Development: We employ machine learning algorithms, specifically deep learning techniques using TensorFlow, to develop a predictive model. The model is trained on historical energy usage data to learn patterns and make accurate predictions.

3. Evaluation and Tuning: We evaluate the performance of the model using appropriate metrics and fine-tune its parameters to enhance accuracy and reliability.

4. Deployment: Once the model is trained and validated, we deploy it in a production environment, making it accessible for real-time energy usage predictions.

Repository Structure

• Frontend: Contains files related to the frontend development, if applicable.
• README.md: Overview of the project and repository structure.
• best.h5, bestest3.h5, bestest5.h5, bestest75.h5: Trained model files with varying configurations.
• gen_train.csv, gen_train_5kwh.csv, gen_train_75kwh.csv: Training datasets with different granularities.
• gen_test.csv, gen_test_5kwh.csv, gen_test_75kwh.csv: Testing datasets corresponding to different training datasets.
• weather_train.csv, weather_test.csv: Additional weather data used in modeling.
• gen-temp.csv, weather_with_solar_energy.csv: Miscellaneous datasets used or generated during the project.
• main.ipynb: Main Jupyter Notebook containing the primary project workflow.
• pvlib_test.ipynb, weather_train.ipynb, weather_test.ipynb: Jupyter Notebooks for testing and analyzing weather data.

Acknowledgments

We would like to express our gratitude to Hackathon Organizers for providing this platform and opportunity to showcase our skills and innovative solutions.