Tuning machine learning models for prediction of building energy loads-Reference-Cited by-同舟云学术

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Tuning machine learning models for prediction of building energy loads

Published:2019-05 Issue: Volume:47 Page:101484
ISSN:2210-6707
Container-title:Sustainable Cities and Society
language:en
Short-container-title:Sustainable Cities and Society

Author:

Seyedzadeh Saleh,Pour Rahimian Farzad,Rastogi Parag,Glesk Ivan

Funder

Data Lab (Edinburgh, UK) and arbnco Ltd

Publisher

Elsevier BV

Subject

Transportation,Renewable Energy, Sustainability and the Environment,Civil and Structural Engineering,Geography, Planning and Development

Reference61 articles.

1. Artificial neural networks to predict energy performance and retrofit scenarios for any member of a building category: A novel approach;Ascione;Energy,2017

2. Classification and regression trees;Breiman,2017

3. Forecasting the evolution of nonlinear and nonstationary systems using recurrence-based local Gaussian process models;Bukkapatnam;Physical Review E - Statistical, Nonlinear, and Soft Matter Physics,2010

4. Measurement and verification of building systems under uncertain data: A Gaussian process modeling approach;Burkhart;Energy and Buildings,2014

5. XGBoost: A Scalable Tree Boosting System;Chen;Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining,2016

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4. Intrinsically interpretable machine learning-based building energy load prediction method with high accuracy and strong interpretability;Energy and Built Environment;2024-08

5. Predictive Analysis of a Building’s Power Consumption Based on Digital Twin Platforms;Energies;2024-07-26

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