Predicting Electricity Prices

Welcome to the Predicting Electricity Prices Machine Learning Challenge!

In this challenge, you will be tasked with developing a machine learning model capable of predicting the daily price variation of electricity futures contracts in England and Scotland based on weather, energy, and commercial data. A multitude of factors influence electricity prices daily, including local weather variations, long-term phenomena such as global warming, geopolitical events, and each country's specific energy mix. Your challenge is to navigate this complexity and create a model that can accurately predict these price variations.

Datasets

The dataset provided for this challenge comprises daily data for England and Scotland, including weather measurements (temperature, rain, wind), energy production information (changes in commodity prices), and electricity usage details (consumption, exchanges between the two countries, import-export data with the rest of Europe). The goal is to use these explanatory variables to estimate the daily price variation of electricity futures contracts, which are financial instruments that provide an expected value on the future price of electricity under current market conditions.

Objectives

Your task is to create a machine learning model that uses the provided explanatory variables to accurately predict the daily price variation of electricity futures contracts in England and Scotland. Please note that this is not a prediction problem, but rather a problem of explaining the electricity price with simultaneous variables.

Challenge Guidelines

Conduct an exploratory data analysis to understand the dataset and identify potentially significant variables.

Develop a machine learning model using the available data that can predict the daily price variation of electricity futures contracts.

Validate and refine your model using appropriate machine learning techniques to ensure its accuracy and reliability.

Provide a clear and comprehensive explanation of your methodology, including the choice of model, the feature selection process, and any preprocessing or transformation techniques applied to the data.

Resources

To get started, you can register at the following link: Kaggle.


You may use any programming language, libraries, and tools that you’re comfortable with. Some popular choices for machine learning tasks include Python with libraries like Pandas, NumPy, Scikit-Learn, and TensorFlow, or R with libraries like dplyr, ggplot2, and caret.

Remember, this challenge is not only about creating a model that performs well but also about understanding the data, making informed decisions, and being able to explain your approach. Good luck!