Analysis and Selection of Multiple Machine Learning Methodologies in PyCaret for Monthly Electricity Consumption Demand Forecasting

Abstract

This study investigates the application of several machine learning models using PyCaret to forecast the monthly demand for electricity consumption; we analyze historical data of monthly consumption readings for the Cuajone Mining Unit of the company Minera Southern Peru Copper Corporation, recorded in the electricity yearbooks from the decentralized office of the Ministry of Energy and Mines in the Moquegua region between 2008 and 2018. We evaluated the performance of 27 machine learning models available in PyCaret for the forecast of monthly electricity consumption, selecting the three most effective models: Exponential Smoothing, AdaBoost with Conditional Deseasonalize and Detrending and ETS (Error-Trend-Seasonality). We evaluated the performance of these models using eight metrics: MASE, RMSSE, MAE, RMSE, MAPE, SMAPE, R2, and calculation time. Among the analyzed models, Exponential Smoothing demonstrated the best performance with a MASE of 0.8359, an MAE of 4012.24 and an RMSE of 5922.63; among the analyzed models, Exponential Smoothing demonstrated the best performance with a MASE of 0.8359, an MAE of 4012.24 and a RMSE of 5922.63, followed by AdaBoost with Conditional Deseasonalize and Detrending, while ETS also provided competitive results. Forecasts for 2018 were compared with actual data, confirming the high accuracy of these models. These findings provide a robust energy management and planning framework, highlighting the potential of machine learning methodologies to optimize electricity consumption forecasting.

Keywords

• machine learning
• PyCaret
• electricity consumption forecasting
• exponential smoothing
• AdaBoost
• ETS