Energy Consumption Prediction Using Data Reduction and Ensemble Learning Techniques

Abstract

Building energy problems have various kinds of aspects, one of which is the difficulty of measuring energy efficiency. With current data development, energy efficiency measurements can be made by developing predictive models to estimate future building needs. However, with the massive amount of data, several problems arise regarding data quality and the lack of scalability in terms of computation memory and time in modeling. In this study, we used data reduction and ensemble learning techniques to overcome these problems. We used numerosity reduction, dimension reduction, and a LightGBM model based on boosting added with a bagging technique, which we compared with incremental learning. Our experimental results showed that the numerosity reduction and dimension reduction techniques could speed up the training process and model prediction without reducing the accuracy. Testing the ensemble learning model also revealed that bagging had the best performance in terms of RMSE and speed, with an RMSE of 262.304 and 1.67 times faster than the model with incremental learning.