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Saiful Akbar
University Center of Excellence in Artificial Intelligence for Vision, Natural Language Processing, and Big Data Analytics (U-CoE AI VLB), Institut Teknologi Bandung, Jalan Ganesa No. 10 Bandung 40132, Indonesia
Computer Science & IT

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Energy Consumption Prediction Using Data Reduction and Ensemble Learning Techniques Marsa Thoriq Ahmada; Saiful Akbar
Journal of ICT Research and Applications Vol. 16 No. 3 (2022)
Publisher : DRPM - ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2022.16.3.1

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.
Co-Authors Marsa Thoriq Ahmada