⚡ Electricity Demand Forecasting Project

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📌 Project Description

End-to-end electricity demand forecasting project using Python. Covers data preprocessing, exploratory analysis, feature engineering, and machine learning models to predict hourly energy consumption from temperature, humidity, and temporal patterns.

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📁 Project Structure

Electricity-Demand-Forecasting/
│
├── data/
│   └── Electricity_Demand_Dataset.csv
│
├── notebook/
│   └── Electricity_demand_ipynb.ipynb
│
├── plots/
│   ├── demand_trend.png
│   ├── correlation_heatmap.png
│   └── prediction_vs_actual.png
│
├── README.md
└── requirements.txt

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📊 Project Overview

Electricity demand forecasting helps utilities and industries plan energy supply, reduce blackouts, and optimize cost distribution. This project applies machine learning techniques to predict hourly demand using:

• Temperature
• Humidity
• Time features (hour, day, month, year)

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🔍 Key Features

• ✔️ Full end-to-end data science workflow
• ✔️ Clean and well-structured dataset
• ✔️ Exploratory data analysis (EDA)
• ✔️ Machine learning for demand prediction
• ✔️ Time-based and weather-based feature engineering
• ✔️ Visualized insights and model performance plots

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📥 Dataset Summary

Feature	Description
Timestamp	Date and time of record
hour	Hour of the day
dayofweek	Day of the week
month	Month number
year	Year
dayofyear	Numeric day of the year
Temperature	Temperature in Celsius
Humidity	Humidity percentage
Demand	Electricity demand (target variable)

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🔬 Exploratory Data Analysis (EDA)

Key insights explored:

• Trend patterns in electricity demand

• Correlation between temperature, humidity, and demand

• Daily & seasonal demand cycles

• Distribution of weather features

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🧠 Modeling Approach

The project uses multiple ML regression models, including:

• Linear Regression
• Random Forest Regressor
• Gradient Boosting Regressor

Models were evaluated using:

• RMSE (Root Mean Squared Error)
• MAE (Mean Absolute Error)

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🧰 Tools & Technologies

• Python
• Pandas, NumPy
• Matplotlib, Seaborn
• Scikit-learn
• Jupyter Notebook

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🚀 How to Run the Project

1. Clone the repository

git clone https://github.com/JosephHinga/electricity-demand-forecasting

2. Install dependencies

pip install -r requirements.txt

3. Run the notebook

jupyter notebook notebook/Electricity_demand_ipynb.ipynb

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📌 Future Enhancements

• Deploy the model using FastAPI/Flask
• Build a live dashboard in Power BI/Tableau
• Integrate real-time weather API for dynamic forecasting
• Try advanced time-series models (Prophet, LSTM)

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👤 Author

Joseph Hinga Mwangi Data & Risk Analyst | Data Scientist 📧 hingamwangijoseph@gmail.com

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