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Machine Learning for Electricity Forecasting by KIRAN is a document available to read on EtoBox.

This document discusses electricity consumption prediction using machine learning techniques. It reviews literature on applying methods like linear regression, K-Nearest Neighbors (KNN), XGBoost, random forest, and artificial neural networks to forecast electricity usage based on historical hourly consumption data. The KNN model achieved the highest accuracy of 90.92% according to performance metrics like Mean Absolute Error, Root Mean Squared Error, and Coefficient of Determination. The document also outli

Author

KIRAN

Language

EN