Economics at your fingertips  

A novel data-driven approach for residential electricity consumption prediction based on ensemble learning

Kunlong Chen, Jiuchun Jiang, Fangdan Zheng and Kunjin Chen

Energy, 2018, vol. 150, issue C, 49-60

Abstract: With the development of smart grid as well as the electricity market, it is of increasing significance to predict the household electricity consumption. In this paper, a novel data-driven framework is proposed to predict the annual household electricity consumption using ensemble learning technique. The extreme gradient boosting forest and feedforward deep networks are served as base models. These base models are combined by ridge regression. What is more, the importances of input features are estimated. A subset of features is selected as the important features to feed into the model to increase its accuracy. A comparison of the proposed ensemble framework against classical regression models indicates that the former can reduce by 30% of the prediction error. The results of this study show that ensemble learning method can be a convenient and accurate approach to predict household electricity consumption.

Keywords: Household electricity consumption; Ensemble learning; Neural network; Extreme gradient boosting (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations View citations in EconPapers (1) Track citations by RSS feed

Downloads: (external link)
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:

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 Dana Niculescu ().

Page updated 2019-01-19
Handle: RePEc:eee:energy:v:150:y:2018:i:c:p:49-60