Water and Electricity Consumption Forecasting at an Educational Institution using Machine Learning models with Metaheuristic Optimization

TL;DR

This study compares machine learning models RF and SVR, optimized with genetic algorithms, for water and electricity consumption forecasting at an educational institution, finding RF generally performs better and climatic variables may reduce accuracy.

Contribution

It introduces a comparative analysis of RF and SVR models with hyperparameter optimization for consumption forecasting in an educational setting, including the impact of climatic variables.

Findings

RF outperforms SVR in forecasting accuracy.

Climatic variables often decrease model performance.

Both models struggle with water consumption prediction.

Abstract

Educational institutions are essential for economic and social development. Budget cuts in Brazil in recent years have made it difficult to carry out their activities and projects. In the case of expenses with water and electricity, unexpected situations can occur, such as leaks and equipment failures, which make their management challenging. This study proposes a comparison between two machine learning models, Random Forest (RF) and Support Vector Regression (SVR), for water and electricity consumption forecasting at the Federal Institute of Paran\'a-Campus Palmas, with a 12-month forecasting horizon, as well as evaluating the influence of the application of climatic variables as exogenous features. The data were collected over the past five years, combining details pertaining to invoices with exogenous and endogenous variables. The two models had their hyperpa-rameters optimized using the Genetic Algorithm (GA) to select the individuals with the best fitness to perform the forecasting with and without climatic variables. The absolute percentage errors and root mean squared error were used as performance measures to evaluate the forecasting accuracy. The results suggest that in forecasting water and electricity consumption over a 12-step horizon, the Random Forest model exhibited the most superior performance. The integration of climatic variables often led to diminished forecasting accuracy, resulting in higher errors. Both models still had certain difficulties in predicting water consumption, indicating that new studies with different models or variables are welcome.