Electricity Consumption Prediction Using Machine Learning

The use of electricity has a significant impact on the environment, energy distribution costs, and management since it directly impacts these costs. Long-standing techniques have inherent limits in terms accuracy scalability when comes to predicting power usage. It is now feasible properly anticipate using previous data thanks improvements machine learning techniques. In this paper, we provide learning-based method for forecasting use. study, investigate number techniques, including linear regression, K Nearest Neighbours, XGBOOST, random forest, artificial neural networks(ANN), forecast Using historical received from utility business, trained assessed models. year’s worth hourly that been pre-processed address outliers missing numbers. Various assessment measures, Mean Absolute Error (MAE), Root Squared (RMSE), Coefficient Determination (R2), were used assess performance models [19]. outcomes demonstrate suggested may accurately Neighbours(KNN) model outperformed all others performance, with 90.92% rate agricultural production