Residential Electricity Consumption Level Impact Factor Analysis Based on Wrapper Feature Selection and Multinomial Logistic Regression-Reference-Cited by-同舟云学术

Residential Electricity Consumption Level Impact Factor Analysis Based on Wrapper Feature Selection and Multinomial Logistic Regression

Published:2018-05-08 Issue:5 Volume:11 Page:1180
ISSN:1996-1073
Container-title:Energies
language:en
Short-container-title:Energies

Author:

Wang Fei,Yu Yili,Wang Xinkang,Ren Hui,Shafie-Khah Miadreza,Catalão João

Publisher

MDPI AG

Subject

Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous)

Link

http://www.mdpi.com/1996-1073/11/5/1180/pdf

Reference50 articles.

1. The Relationship between Residential Electricity Consumption and Income: A Piecewise Linear Model with Panel Data

2. Informatics Solution for Energy Efficiency Improvement and Consumption Management of Householders

3. Electricity Information 2011

4. Life Cycle Energy Consumption and Greenhouse Gas Emissions Analysis of Natural Gas-Based Distributed Generation Projects in China

5. Building operation and energy performance: Monitoring, analysis and optimisation toolkit

Cited by 7 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Parametric Time-series Modelling of London Smart Meter Data for Short-term Demand Forecasting;2023 IEEE 3rd International Conference on Sustainable Energy and Future Electric Transportation (SEFET);2023-08-09

2. Application of Machine Learning to Child Mode Choice with a Novel Technique to Optimize Hyperparameters;International Journal of Environmental Research and Public Health;2022-12-15

3. International Natural Gas Price Trends Prediction with Historical Prices and Related News;Energies;2022-05-13

4. A data-driven approach for multi-scale GIS-based building energy modeling for analysis, planning and support decision making;Applied Energy;2020-12

5. A data-driven approach to optimize urban scale energy retrofit decisions for residential buildings;Applied Energy;2020-06