期刊
2019 IEEE INTERNATIONAL CONFERENCE ON BIG DATA AND SMART COMPUTING (BIGCOMP)
卷 -, 期 -, 页码 571-578出版社
IEEE
DOI: 10.1109/bigcomp.2019.8679188
关键词
Short-term load forecasting; feature selection; feature engineering; load clustering; smart meter data
Identifying an appropriate set of predictors is important for making efficient and accurate forecasting models. In this paper, we study the application of some feature selection methods for prediction of household energy consumption. This study follows a two-stage framework. First, it identifies candidate features based on literature study and data characteristics of a load profile and then it selects a subset of relevant features using four different feature selection methods; F-regression, Mutual Information, Recursive Feature Elimination and Elastic Net. We evaluate the effectiveness of these methods, in conjunction with an ensemble-based prediction algorithm (Gradient Boosted Regression Tree), using smart meter data of 23 houses in Norway. To study the performance of these methods for different load profiles, we grouped households into clusters of similar consumption behaviour and computed average performance of each mechanism over clusters' members. Test results show that all feature selection methods could identify a custom-made subset of highly relevant features for each household. Across all clusters, building predictive models utilizing feature selection techniques led to considerable improvements in training speed and simplicity, as well as comparable prediction accuracy with models without feature engineering.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据