🔋 Energy Forecasting Dashboard

An end-to-end MLOps pipeline for predicting household energy consumption using time-based features, built with FastAPI, Streamlit, MLflow, DVC, and Prefect.
This project is fully containerized and reproducible via Docker.

🚀 Current setup: Local deployment via Docker on 0.0.0.0.

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📌 Project Overview

This project forecasts household hourly electricity usage based on:

• Hour of the day
• Day of the week
• Month

It features:

• XGBoost regression model
• A real-time FastAPI inference service
• A user-friendly Streamlit dashboard
• End-to-end experiment tracking with MLflow
• DVC for data/model versioning
• Prefect for pipeline automation
• Packaged & deployable with Docker

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🧠 Model

• Model type: XGBoostRegressor
• Features used:
  • hour: Hour of the day (0–23)
  • dayofweek: Day of the week (0=Monday)
  • month: Month number (1–12)
• Target: Global_active_power (kW)

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🗂️ Project Structure

energy-forecasting/
├── api/                    # FastAPI inference server
│   └── main.py
├── dashboard/              # Streamlit dashboard
│   └── app.py
├── data/
│   ├── raw/                # Original dataset (from UCI)
│   └── processed/          # Cleaned + resampled data
├── models/                 # Trained model files (via DVC)
│   └── latest_model_path.txt
├── mlops/
│   ├── mlflow_config.yaml
│   └── register_model.py
├── pipelines/              # Prefect automation
│   └── prefect_flow.py
├── src/
│   ├── data_loader.py      # Preprocessing script
│   └── train_model.py      # Model training & logging
├── Dockerfile              # Container setup
├── docker-compose.yaml     # Service orchestration
├── dvc.yaml                # Pipeline stages
├── dvc.lock
├── requirements.txt
└── README.md

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⚙️ MLOps Pipeline Overview

Stage	Tool	Description
Data versioning	DVC	Tracks data and models (e.g. energy_clean.csv)
Training	XGBoost	Model trained on 3 time features
Experiment tracking	MLflow	Logs parameters, metrics, model artifacts
Automation	Prefect	Defines retraining pipeline (data → train)
Serving	FastAPI	Real-time prediction API on /predict
Monitoring UI	Streamlit	Frontend to submit inputs & visualize results
Packaging	Docker	Full stack in one container

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📦 How to Run Locally (Docker)

# 1. Build and start
docker-compose up --build

# 2. Access:
FastAPI     → http://localhost:8000
Streamlit   → http://localhost:8502
MLflow UI   → http://localhost:5050

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🚀 Prediction API (FastAPI)

Endpoint: POST /predict
Input JSON:

{
  "hour": 13,
  "dayofweek": 2,
  "month": 4
}

Response:

{
  "predicted_energy_in_kW": 3.123
}

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🖥️ Dashboard (Streamlit)

Access: http://localhost:8502

• Input desired time (hour, weekday, month)
• View predicted energy in kW
• Visualize prediction history
• Check real historical energy usage

🔄 Reproducible Training (DVC)

# Run full pipeline
dvc repro

# Push data + model versions to remote (optional)
dvc push

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🔁 Model Retraining (Prefect)

python pipelines/prefect_flow.py

Runs:

• data_loader.py → preprocessing
• train_model.py → model training + MLflow logging

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🧪 Track Experiments (MLflow)

Visit: http://localhost:5050
Browse runs, parameters, metrics, models.

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📄 Dataset Info

• Source: UCI - Individual household electric power consumption
• Resampled to hourly intervals
• Target: Global_active_power

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🛠️ Tech Stack

• Python 3.10
• FastAPI, Streamlit
• xgboost, scikit-learn, pandas
• MLflow, Prefect, DVC
• Docker, docker-compose

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🧠 Author

👨‍💻 Taey Kim
📫 GitHub 💡 Passionate about MLOps, system automation, and real-time inference!

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📌 To-Do

• Add CI/CD via GitHub Actions
• Deploy to Heroku / Fly.io
• Batch forecasting + scheduling
• User login for dashboard

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MIT License | 2025