hilaryol/Renewable_Groupe5_2024

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Model Details

Model Description

• Developed by: Hilary, Evan, Matthis, Valère, Doline, Christian, Sorel
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• Model type: Linear
• Language(s) (NLP): Python
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Model Sources [optional]

• Repository: Model factory
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Uses

Direct Use

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

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Training Details

We have a dataset containing information on energy consumption in France, in particular fossil fuels and renewable energies per year, as well as information on the number of inhabitants (rural and urban areas), GDP per capita and life expectancy.

Training Data

We used the variables Temps, population and all fossil.

Training Procedure

The model was trained with a linear model called OLS (Ordinary Least Squares), from the statsmodels package.

Preprocessing [optional]

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Training Hyperparameters

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

We have an R-squared score of 0.597, i.e. the variables we have selected explain almost 60% of the target variable. The variables Temps and Population have p-values of 0.7 and 0.3, which are greater than 0.1, i.e. they do not have a great influence on renewable energy consumption. On the other hand, the All fossil variable has a p-value of 0.03, which has a strong influence on renewable energy consumption.

Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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