Roles of artificial intelligence in construction engineering and management: A critical review and future trends

Automation in Construction, Journal Year: 2020, Volume and Issue: 122, P. 103517 - 103517

Published: Dec. 18, 2020

Language: Английский

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Modeling and forecasting building energy consumption: A review of data-driven techniques

Sustainable Cities and Society, Journal Year: 2019, Volume and Issue: 48, P. 101533 - 101533

Published: April 14, 2019

Language: Английский

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Forecasting: theory and practice

International Journal of Forecasting, Journal Year: 2022, Volume and Issue: 38(3), P. 705 - 871

Published: Jan. 20, 2022

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds future is both exciting challenging, with individuals organisations seeking to minimise risks maximise utilities. large number forecasting applications calls for a diverse set methods tackle real-life challenges. This article provides non-systematic review theory practice forecasting. We provide an overview wide range theoretical, state-of-the-art models, methods, principles, approaches prepare, produce, organise, evaluate forecasts. then demonstrate how such theoretical concepts are applied in variety contexts. do not claim this exhaustive list applications. However, we wish our encyclopedic presentation will offer point reference rich work undertaken over last decades, some key insights practice. Given its nature, intended mode reading non-linear. cross-references allow readers navigate through various topics. complement covered by lists free or open-source software implementations publicly-available databases.

Language: Английский

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Predictive modelling for solar thermal energy systems: A comparison of support vector regression, random forest, extra trees and regression trees

Journal of Cleaner Production, Journal Year: 2018, Volume and Issue: 203, P. 810 - 821

Published: Aug. 28, 2018

Predictive analytics play an important role in the management of decentralised energy systems. Prediction models uncontrolled variables (e.g., renewable sources generation, building consumption) are required to optimally manage electrical and thermal grids, making informed decisions for fault detection diagnosis. The paper presents a comprehensive study compare tree-based ensemble machine learning (random forest – RF extra trees ET), decision (DT) support vector regression (SVR) predict useful hourly from solar collector system. developed were compared based on their generalisation ability (stability), accuracy computational cost. It was found that ET have comparable predictive power equally applicable predicting (USTE), with root mean square error (RMSE) values 6.86 7.12 testing dataset, respectively. Amongst studied algorithms, DT is most computationally efficient method as it requires significantly less training time. However, accurate (RMSE = 8.76) than ET. time SVR 1287.80 ms, which approximately three times higher

Language: Английский

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A review of the-state-of-the-art in data-driven approaches for building energy prediction

Energy and Buildings, Journal Year: 2020, Volume and Issue: 221, P. 110022 - 110022

Published: April 30, 2020

Language: Английский

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Building energy consumption prediction for residential buildings using deep learning and other machine learning techniques

Journal of Building Engineering, Journal Year: 2021, Volume and Issue: 45, P. 103406 - 103406

Published: Oct. 12, 2021

Language: Английский

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A hybrid model for building energy consumption forecasting using long short term memory networks

Applied Energy, Journal Year: 2020, Volume and Issue: 261, P. 114131 - 114131

Published: Jan. 6, 2020

Language: Английский

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Assessment of deep recurrent neural network-based strategies for short-term building energy predictions

Applied Energy, Journal Year: 2018, Volume and Issue: 236, P. 700 - 710

Published: Dec. 13, 2018

Language: Английский

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Deep learning models for solar irradiance forecasting: A comprehensive review

Journal of Cleaner Production, Journal Year: 2021, Volume and Issue: 318, P. 128566 - 128566

Published: Aug. 11, 2021

Language: Английский

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Predicting energy consumption in multiple buildings using machine learning for improving energy efficiency and sustainability

Journal of Cleaner Production, Journal Year: 2020, Volume and Issue: 260, P. 121082 - 121082

Published: March 14, 2020

Language: Английский

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