Groupe2_Prediction

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Model Details

Model Description

Nous avons voulu créer un modèle pour étudier le comportement de la consommation d'énergie renouvelable de la France. Pour cela nous avons commencer par imaginer quels sont les facteurs qui influencent la consommation d'energie renouvelable. Nous avons estimé que cette consommation pouvait être correlée à la consommation de charbon, au PIB par habitant et au temps écoulé.

Hypothèse 1 : Nous avons choisi le charbon car on suppose qu'on utilise de moins en moins de charbon en France et que cette diminution est compensé par la consommation d'énergie renouvelable.

Hypothèse 2 : Nous avons choisi le PIB par habitant parce qu'on suppose que plus un pays est developpé, plus il va investir donc utiliser d'energie renouvelable.

Hypothèse 3 : Nous avons choisi la variable du temps parce qu'on suppose que plus on avance dans le temps, plus en utilise des energie renouvelable en France car la potitique du pays tend à favoriser le développement d'energie verte.

Nous sommes parti sur la base d'un modèle linéaire avec ces trois variables.

Le modèle nous a permis d'obtenir le niveau d'explicativité de la consommation et des variables corrélées à cette consommation.

• Developed by: [Pauline, Steve, Ahmed, Leonnard, Michaela]
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Model Sources [optional]

https://ourworldindata.org/renewable-energy Nous avons retrouvé les données sur le site Our World in Data ayant comme titre Renewable Energy.

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Uses

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Bias, Risks, and Limitations

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Recommendations

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Training Details

Training Data

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Preprocessing [optional]

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Training Hyperparameters

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Evaluation

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Testing Data, Factors & Metrics

Testing Data

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Factors

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Summary

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Environmental Impact

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

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Technical Specifications [optional]

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