Electricity Consumption Prediction Using Machine Learning

Abstract

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

Keywords

• knn
• ann
• random forest
• xgboost regressor