An intelligent HVAC prediction and analysis project utilizing machine learning to optimize the performance, efficiency, and reliability of Heating, Ventilation, and Air Conditioning (HVAC) systems. Python script that loads IoT sensor CSV files, trains a neural‑network model (MLPRegressor) to predict temperature and humidity, and visualises actual vs. predicted values.
- Python ≥ 3.14
pip install -r requirements.txt
# Run with the default data folder (./data)
python app.py
# Or specify a custom folder
python app.py /path/to/your/csvs
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## Table of Contents
- [Project Overview](#project-overview)
- [Features](#features)
- [Project Structure](#project-structure)
- [Getting Started](#getting-started)
- [Usage](#usage)
- [Results](#results)
- [Contributing](#contributing)
- [License](#license)
- [Acknowledgements](#acknowledgements)
---
## Project Overview
This repository contains machine learning models and analysis for optimizing HVAC systems. The project tackles:
1. **Energy Optimization & Consumption Prediction:**
Reducing energy usage while maintaining comfort.
2. **Thermal Comfort Prediction & Optimization:**
Predicting and optimizing indoor comfort levels.
3. **Predictive Maintenance & Anomaly Detection:**
Anticipating faults and scheduling maintenance.
4. **HVAC System Optimization & Control:**
Intelligent control strategies for improved system efficiency.
---
## Features
- Data-driven HVAC energy consumption prediction
- Comfort level analysis and forecasting
- Anomaly detection for predictive maintenance
- Model explainability and performance visualization
- Modular and extensible codebase using Jupyter Notebooks
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## Project Structure
. ├── data/ # Datasets (raw & processed) ├── notebooks/ # Jupyter Notebooks with analysis & ML models ├── models/ # Saved machine learning models ├── results/ # Generated results and plots ├── requirements.txt # Python dependencies ├── README.md # Project documentation └── LICENSE # MIT License
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## Getting Started
### Prerequisites
- Python 3.8+
- Jupyter Notebook
- Recommended: [Anaconda](https://www.anaconda.com/products/distribution) distribution
### Installation
1. **Clone the repository:**
```bash
git clone https://github.com/Amar03ete/Smart-HVAC-Predictive-Model.git
cd Smart-HVAC-Predictive-Model
-
Install dependencies:
pip install -r requirements.txt
-
Launch Jupyter Notebook:
jupyter notebook
- Place your data in the
data/directory. - Open and run the Jupyter notebooks in the
notebooks/directory. - Follow the step-by-step instructions within the notebooks to preprocess data, train models, and visualize results.
-
Energy Prediction Accuracy:
-
Thermal Comfort Analysis:
Actual results and figures depend on datasets and model versions. See the results/ directory for outputs generated by the latest run.
Contributions are welcome! Please open issues or pull requests for improvements, bug fixes, or new features.
- Fork the repository
- Create a new branch (
git checkout -b feature/your-feature) - Commit your changes (
git commit -am 'Add new feature') - Push to the branch (
git push origin feature/your-feature) - Open a pull request
This project is licensed under the MIT License.
Author: Amar03ete