Robust building energy consumption forecasting using an online learning approach with R ranger-Reference-Cited by-同舟云学术

Robust building energy consumption forecasting using an online learning approach with R ranger

Published:2022-04 Issue: Volume:47 Page:103851
ISSN:2352-7102
Container-title:Journal of Building Engineering
language:en
Short-container-title:Journal of Building Engineering

Author:

Moon Jihoon^ORCID,Park Sungwoo,Rho Seungmin,Hwang Eenjun^ORCID

Funder

National Research Foundation of Korea

Ministry of Science, ICT and Future Planning

Publisher

Elsevier BV

Subject

Mechanics of Materials,Safety, Risk, Reliability and Quality,Building and Construction,Architecture,Civil and Structural Engineering

Reference64 articles.

1. Net zero energy buildings in New Zealand: challenges and potentials reviewed against legislative, climatic, technological, and economic factors;Cielo;J. Build. Eng.,2021

2. Building automation system for grid-connected home to optimize energy consumption and electricity bill;Chauhan;J. Build. Eng.,2019

3. Trainingless multi-objective evolutionary computing-based nonintrusive load monitoring: Part of smart-home energy management for demand-side management;Lin;J. Build. Eng.,2021

4. Surveys on the reliability impacts of power system cyber–physical layers;Jimada-Ojuolape;Sust. Cities Soc.,2020

5. Adaptable energy management system for smart buildings;Salerno;J. Build. Eng.,2021

Cited by 36 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. Novel STAttention GraphWaveNet model for residential household appliance prediction and energy structure optimization;Energy;2024-10

2. A novel methodology for day-ahead buildings energy demand forecasting to provide flexibility services in energy markets;International Journal of Electrical Power & Energy Systems;2024-10

3. Optimizing Smart Home Appliance Energy Monitoring using Factorial Hidden Markov Models for Internet of Behavior;Journal of Building Engineering;2024-09

4. Two-Stage Neural Network Optimization for Robust Solar Photovoltaic Forecasting;Electronics;2024-04-25

5. A Future Direction of Machine Learning for Building Energy Management: Interpretable Models;Energies;2024-02-01