Energy Load Forecasting in Great Britain
Aryan Kushwah
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Posted Aug 2, 2025
Machine learning project to forecast energy load in Great Britain using weather and time-series data.
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Energy Forecasting in Great Britain
This repository contains a machine learning project to forecast energy load in Great Britain. The project utilizes a range of weather and time-series features to develop a robust predictive model, providing valuable insights for energy management and grid optimization.
Project Overview
The primary objective of this project is to build a highly accurate model for forecasting energy consumption. The forecast is based on historical energy load data, combined with a comprehensive set of external features, including weather conditions and time-series components (e.g., day of the week, hour of the day).
Key Features
Data-Driven Forecasting: Uses machine learning models to identify complex patterns and relationships between energy load and various influencing factors.
Time-Series Analysis: Incorporates seasonal and temporal trends to improve prediction accuracy.
Weather Integration: Leverages weather data (temperature, humidity, etc.) as a key feature for enhanced forecasting.
Performance Evaluation: Includes a robust evaluation framework to assess model performance and ensure reliability.
Repository Structure
The project is organized into the following directories:
data/: Contains the raw and preprocessed datasets used for training and testing the model.eda_outputs/: Stores the results and visualizations from the Exploratory Data Analysis (EDA) phase.models/: Holds the trained machine learning models, including the final predictive model.results/: Contains the final forecast outputs and performance metrics.scripts/: All the Python scripts for data preprocessing, modeling, and evaluation are stored here.visuals/: Stores key visualizations generated throughout the project, such as feature importance plots and forecast vs. actual charts.README.md: This file, providing an overview of the project.requirements.txt: Lists all the necessary Python libraries and their versions for replicating the project environment.Technologies Used
Python
Pandas for data manipulation
Scikit-learn for machine learning models
Matplotlib and Seaborn for data visualization
Jupyter Notebooks (if used for EDA)
Author
Aryan Kushwah