Forme possible de la variation de msb si on fait varier pNP, dans cette version pNS s'adapte a la variation de pNP, il suffit de dire que la forme exacte de cette courbe dépend comment on décide de faire varier pNS quand on fait varier pNP, ce qui est pas précisé dans le sujet donc pas besoin d'approffondir plus

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Avery HollomanThis study evaluated the performance of K-Nearest Neighbors (KNN) and Linear Regression algorithms in predicting power output in wind power generation. The Linear Regression algorithm demonstrated superior performance with a mean accuracy of 82.15% compared to KNN's accuracy of 79.55%. The results showed statistical significance with a p-value < 0.05, indicating that the Linear Regression algorithm is a robust method for this application. # The study emphasized the importance of selecting appropriate algorithms for specific data characteristics. Introduction In recent times, I have seen growing interest in wind energy as a sustainable and eco-friendly alternative source due to its potential to reduce greenhouse gas emissions and mitigate climate change. However, integrating wind energy into the power grid has posed challenges due to its limited predictability and intermittency. To address these issues, I investigated machine learning algorithms, specifically Linear Regression and KNN, to improve prediction accuracy in wind power generation. My aim was to compare the simplicity and efficiency of Linear Regression with the flexibility of KNN, evaluating their applicability for real-time predictions in wind power systems. I also reviewed existing literature, noting advancements like neural networks and gradient boosting machines achieving higher accuracy but requiring greater computational resources.

Si on remplace rremb par une fonction qui renvoie juste x0, on retrouve les mêmes valeurs qu'avec un eta très grand

In this project, I explored hybrid regression approaches to calibrate low-cost SO2 electrochemical sensors. Using data from Pahala and Hilo AQ stations, I trained and validated models for predicting SO2 levels under varying environmental conditions. The analysis combined linear regression and kNN regression to address dynamic changes in pollutant levels and environmental variability. My findings highlighted the limitations of linear models in capturing non-linearities and kNN's inability to extrapolate beyond the training range. The hybrid model achieved robust predictive power, with RMSE as low as 6.9 ppb for relocated sensors.