Bridging IoT devices and machine learning for predicting power consumption: case study universitas Widya Dharma Pontianak

Abstract

Multiple methods have been developed and implemented to reduce dependence on fossil fuels and conserve electricity. However, accurately predicting electricity consumption is essential before reducing it. Forecasting building electricity consumption has become increasingly critical, as buildings account for 39% of global electricity consumption. Among these, campus buildings are particularly energy-intensive. In this study, we used Monte Carlo (MC) simulations—trained on each leaf that generated by the regression tree (RT) algorithm—to predict the electricity consumption of Widya Dharma University Pontianak (UWDP)’s campus building. Unlike traditional approaches that rely on the mean of samples within a leaf, our method incorporates their likelihood. Since RT algorithms are prone to overfitting, training each leaf individually is expected to mitigate this issue. The data were collected by measuring hourly electricity consumption on one floor of the UWDP campus building over several months. The proposed MCRT prediction algorithm achieved an accuracy of 91.61%, with a Root Mean Square Error of 3.49 and a Normalized Root Mean Square Error of 0.09.