Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders

Jui-Sheng Chou and Duc-Son Tran

Energy, 2018, vol. 165, issue PB, 709-726

Abstract: Energy consumption in buildings is increasing because of social development and urbanization. Forecasting the energy consumption in buildings is essential for improving energy efficiency and sustainable development, and thereby reducing energy costs and environmental impact. This investigation presents a comprehensive review of machine learning (ML) techniques for forecasting energy consumption time series using actual data. Real-time data were collected from a smart grid that was installed in an experimental building and used to evaluate the efficacy and effectiveness of statistical and ML techniques. Well-known artificial intelligence techniques were used to analyze energy consumption in single and ensemble scenarios. An in-depth review and analysis of the ‘hybrid model’ that combines forecasting and optimization techniques is presented. The comprehensive comparison demonstrates that the hybrid model is more accurate than the single and ensemble models. Both the accuracy of prediction and the suitability for use of these models are considered to support users in planning energy management.

Keywords: Energy consumption; Residential buildings; Time-series forecasting; Data mining; Artificial intelligence; Machine learning (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (39)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544218319145
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:165:y:2018:i:pb:p:709-726

DOI: 10.1016/j.energy.2018.09.144

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().