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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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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Ensemble approach based on bagging, boosting and stacking for short-term prediction in agribusiness time series

Applied Soft Computing, Journal Year: 2019, Volume and Issue: 86, P. 105837 - 105837

Published: Oct. 11, 2019

Language: Английский

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Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives

Applied Energy, Journal Year: 2021, Volume and Issue: 287, P. 116601 - 116601

Published: Feb. 9, 2021

Enormous amounts of data are being produced everyday by sub-meters and smart sensors installed in residential buildings. If leveraged properly, that could assist end-users, energy producers utility companies detecting anomalous power consumption understanding the causes each anomaly. Therefore, anomaly detection stop a minor problem becoming overwhelming. Moreover, it will aid better decision-making to reduce wasted promote sustainable efficient behavior. In this regard, paper is an in-depth review existing frameworks for building based on artificial intelligence. Specifically, extensive survey presented, which comprehensive taxonomy introduced classify algorithms different modules parameters adopted, such as machine learning algorithms, feature extraction approaches, levels, computing platforms application scenarios. To best authors' knowledge, first article discusses consumption. Moving forward, important findings along with domain-specific problems, difficulties challenges remain unresolved thoroughly discussed, including absence of: (i) precise definitions consumption, (ii) annotated datasets, (iii) unified metrics assess performance solutions, (iv) reproducibility (v) privacy-preservation. Following, insights about current research trends discussed widen applications effectiveness technology before deriving future directions attracting significant attention. This serves reference understand technological progress

Language: Английский

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A deep learning framework for building energy consumption forecast

Renewable and Sustainable Energy Reviews, Journal Year: 2020, Volume and Issue: 137, P. 110591 - 110591

Published: Dec. 15, 2020

Language: Английский

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Slope stability prediction for circular mode failure using gradient boosting machine approach based on an updated database of case histories

Safety Science, Journal Year: 2019, Volume and Issue: 118, P. 505 - 518

Published: June 5, 2019

Language: Английский

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A comprehensive method for improvement of water quality index (WQI) models for coastal water quality assessment

Water Research, Journal Year: 2022, Volume and Issue: 219, P. 118532 - 118532

Published: May 1, 2022

Here, we present an improved water quality index (WQI) model for assessment of coastal using Cork Harbour, Ireland, as the case study. The involves usual four WQI components - selection indicators inclusion, sub-indexing indicator values, sub-index weighting and aggregation with improvements to make approach more objective data-driven less susceptible eclipsing ambiguity errors. uses machine learning algorithm, XGBoost, rank select inclusion based on relative importance overall status. Of ten which data were available, transparency, dissolved inorganic nitrogen, ammoniacal BOD5, chlorophyll, temperature orthophosphate selected summer, while total organic pH, transparency oxygen winter. Linear interpolation functions developed national recommended guideline values are used XGBoost rankings in combination order centroid method determine weight values. Eight tested five from existing models three proposed by authors. computed indices compared those obtained a multiple linear regression (MLR) R2 RMSE function performance. weighted quadratic mean (R2 = 0.91, 4.4 summer; 0.97, 3.1 winter) unweighted arithmetic 0.92, 3.2 authors identified best showed reduced problems others.

Language: Английский

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Machine learning applications in urban building energy performance forecasting: A systematic review

Renewable and Sustainable Energy Reviews, Journal Year: 2020, Volume and Issue: 133, P. 110287 - 110287

Published: Sept. 2, 2020

Language: Английский

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Machine learning in geo- and environmental sciences: From small to large scale

Advances in Water Resources, Journal Year: 2020, Volume and Issue: 142, P. 103619 - 103619

Published: May 26, 2020

Language: Английский

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Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review

Energies, Journal Year: 2019, Volume and Issue: 12(17), P. 3254 - 3254

Published: Aug. 23, 2019

During the past century, energy consumption and associated greenhouse gas emissions have increased drastically due to a wide variety of factors including both technological population-based. Therefore, increasing our efficiency is great importance in order achieve overall sustainability. Forecasting building important for applications planning, management, optimization, conservation. Data-driven models forecasting grown significantly within few decades their performance, robustness ease deployment. Amongst many different types models, artificial neural networks rank among most popular data-driven approaches applied date. This paper offers review studies published since year 2000 which use demand, with particular focus on reviewing applications, data, performance metrics used model evaluations. Based this review, existing research gaps are identified presented. Finally, future directions area highlighted.

Language: Английский

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