Impact assessment of varied data granularities from commercial buildings on exploration and learning mechanism-Reference-Cited by-同舟云学术

Impact assessment of varied data granularities from commercial buildings on exploration and learning mechanism

Published:2022-08 Issue: Volume:319 Page:119281
ISSN:0306-2619
Container-title:Applied Energy
language:en
Short-container-title:Applied Energy

Author:

Khan Waqas,Liao Juo Yu,Walker Shalika,Zeiler Wim

Funder

Nederlandse Organisatie voor Wetenschappelijk Onderzoek

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Mechanical Engineering,General Energy,Building and Construction

Reference54 articles.

1. “Energy Efficiency Trends and Policies in the Household and Tertiary Sectors,” ODYSSEE and MURE Databases, Jun. 2015. [Online]. Available: https://www.odyssee-mure.eu/publications/archives/energy-efficiency-in-buildings.html.

2. Energy Load Profile Analysis on Machine Level;Teiwes;Procedia CIRP,2018

3. A systematic literature review on the use of artificial intelligence in energy self-management in smart buildings;Aguilar;Renew Sustain Energy Rev,2021

4. Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks;Rahman;Appl Energy,2018

5. Improved solar photovoltaic energy generation forecast using deep learning-based ensemble stacking approach;Khan;Energy,2022

Cited by 12 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. Impact of dataset sampling period on building thermal models used for flexibility activation;Building and Environment;2024-08

2. Industrial Flexible Load Scheduling Optimization Method Based on Probabilistic Demand Response Potential Evaluation;2024 7th Asia Conference on Energy and Electrical Engineering (ACEEE);2024-07-20

3. Component capacity optimization of a renewable energy system using data-driven two-stage algorithmic approach;Energy Conversion and Management;2024-07

4. Interpretable domain-informed and domain-agnostic features for supervised and unsupervised learning on building energy demand data;Applied Energy;2024-04

5. A novel physical-feature-based approach for stochastic simulation of typical building electricity use profiles;Energy and Buildings;2024-01