Short-term probabilistic building load forecasting based on feature integrated artificial intelligent approach-Reference-Cited by-同舟云学术

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Short-term probabilistic building load forecasting based on feature integrated artificial intelligent approach

Published:2022-05 Issue: Volume:206 Page:107802
ISSN:0378-7796
Container-title:Electric Power Systems Research
language:en
Short-container-title:Electric Power Systems Research

Author:

Liu Ronghui,Chen Teng,Sun Gaiping,Muyeen S.M.,Lin Shunfu,Mi Yang

Funder

National Natural Science Foundation of China

Shanghai Municipality Science and Technology Commission

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology

Reference41 articles.

1. Novel deep supervised ML models with feature selection approach for large-scale utilities and buildings short and medium-term load requirement forecasts;Ahmad;Energy,2020

2. A comprehensive overview on the data driven and large scale based approaches for forecasting of building energy demand: a review;Ahmad;Energy Build.,2018

3. Review of smart meter data analytics: applications, methodologies, and challenges;Wang;IEEE Trans. Smart Grid,2019

4. Load demand forecasting of residential buildings using a deep learning model;Wen;Electr. Pow. Syst. Res.,2020

5. Challenges of feature selection for big data analytics;Li;IEEE. Intell. Syst.,2017

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