📊 Fair and Sustainable Energy Usage Predictions: An AI and Ethics Case Study

📝 Project Overview

This project focuses on developing a time-series machine learning model to predict household energy consumption using historical data. In addition to the technical modeling aspect, the project explores ethical considerations surrounding AI in energy systems — including privacy, equity, transparency, and sustainability.

The dataset used is household_power_consumption.csv, which contains real-world energy usage metrics over time.

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📂 Dataset

• Name: household_power_consumption.csv
• Source: Available on Blackboard
• Description: Includes historical energy usage data with timestamps and contextual variables like voltage, intensity, and sub-metering data.

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⚙️ Technical Tasks

This is a time-series regression task. The notebook includes:

• Data cleaning and preprocessing
• Feature engineering (e.g., time of day, date-related features)
• Model development (regression using algorithms such as Linear Regression, Random Forest, or LSTM)
• Forecasting future energy consumption (e.g., hourly or daily)
• Visualization of predictions and model performance

Optional: Classification of usage patterns into high, medium, or low categories based on thresholds

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🧠 Ethical Analysis

The notebook also investigates and discusses the following ethical questions:

1. Bias in Access

   • How might energy forecasting models favor certain socio-economic groups or geographic areas?
   • Considerations of energy equity in access to predictive technologies.

2. Privacy Concerns

   • Risks associated with using granular, real-time energy data (e.g., user profiling or surveillance).
   • Balancing utility and personal privacy.

3. Transparency and Accountability

   • Methods to make AI decisions interpretable (e.g., SHAP values, model explanations).
   • Ensuring accountability in prediction errors or unfair recommendations.

4. Sustainability

   • Can the model suggest energy-efficient behavior?
   • Preventing penalization of vulnerable groups (e.g., low-income households) while promoting sustainable habits.

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🛠 Requirements

Make sure to install the following Python libraries before running the notebook:

pip install pandas numpy matplotlib seaborn scikit-learn xgboost shap

Optional for advanced modeling:

pip install keras tensorflow

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📁 File Structure

📦Project Folder
 ┣ 📜 powerconsumption_aivancity.ipynb     # Main Jupyter Notebook
 ┣ 📜 household_power_consumption.csv      # Dataset (to be downloaded from Blackboard)
 ┗ 📄 README.md                            # This file

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📌 Instructions

1. Download the dataset from Blackboard.
2. Place it in the same directory as the notebook.
3. Open powerconsumption_aivancity.ipynb and run cells sequentially.
4. Review both the model performance and ethical discussion sections.

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👩‍🏫 Authors

• [Your Name]
• [Your Group Members]
• Institution: Aivancity

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📬 License

This project is for educational purposes only. Redistribution or reuse of the dataset must follow the terms defined on Blackboard.