Predictive analytics for building power demand: Day-ahead forecasting and anomaly prediction-Reference-Cited by-同舟云学术

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Predictive analytics for building power demand: Day-ahead forecasting and anomaly prediction

Published:2022-01 Issue: Volume:255 Page:111670
ISSN:0378-7788
Container-title:Energy and Buildings
language:en
Short-container-title:Energy and Buildings

Author:

Lin Jing,Fernández Julián A.,Rayhana Rakiba,Zaji Amirhossein,Zhang Ran,Herrera Omar E.,Liu Zheng,Mérida Walter

Funder

Natural Resources Canada

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Mechanical Engineering,Building and Construction,Civil and Structural Engineering

Reference49 articles.

1. Data Mining and Big Data: Myths, Misconceptions and Methods. Business in the Digital Economy;Finlay,2014

2. Integrating artificial intelligent techniques and continuous time simulation modelling. Practical predictive analytics for energy efficiency and failure detection;Crespo Márquez;Comput. Ind.,2020

3. Data-driven building load profiling and energy management;Zhu;Sustain. Cities Soc.,2019

4. A hybrid data mining approach for anomaly detection and evaluation in residential buildings energy data;Xu;Energy Build.,2020

5. Neural network model for short-term and very-short-term load forecasting in district buildings;Dagdougui;Energy Build.,2019

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1. Electricity Behavior Modeling and Anomaly Detection Services Based on a Deep Variational Autoencoder Network;Energies;2024-08-07

2. Short-Term Energy Forecasting to Improve the Estimation of Demand Response Baselines in Residential Neighborhoods: Deep Learning vs. Machine Learning;Buildings;2024-07-21

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

4. Energy Forecasting: A Comprehensive Review of Techniques and Technologies;Energies;2024-03-30

5. Multi-task deep learning for large-scale buildings energy management;Energy and Buildings;2024-03

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