Overview of the use of artificial neural networks for energy‐related applications in the building sector

Guyot, Dimitri; Giraud, Florine; Simon, Florian; Corgier, David; Marvillet, Christophe; Tréméac, Brice

doi:10.1002/er.4706

Cited by 18 publications

(11 citation statements)

References 100 publications

(160 reference statements)

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“…Sha et al (2019) [9] summarized how computational intelligence could be used to improve building energy system design. Guyot et al (2019) [10] reviewed how artificial neural networks (ANNs) could be used for energy-related applications in the building sector. However, those review studies only focused on a specific stage of building life cycle (e.g., building design, building operation, and control) or on a specific application (e.g., occupant sensing, load prediction), or were limited to the use of a specific machine learning algorithm (e.g., ANN).…”

Section: Introductionmentioning

confidence: 99%

State-of-the-art on research and applications of machine learning in the building life cycle

Hong

Wang

Luo

et al. 2020

Energy and Buildings

207

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Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science.

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Section: Introductionmentioning

confidence: 99%

State-of-the-art on research and applications of machine learning in the building life cycle

Hong

Wang

Luo

et al. 2020

Energy and Buildings

207

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“…This makes it possible to use neural networks widely, not only in research on brain functions but further to analyze data in areas as diverse as economics [4][5][6], automation [7], the energy industry [8][9][10][11], the natural sciences [12], and medicine [13,14]. ANNs are a tool used in machine learning.…”

Section: Introductionmentioning

confidence: 99%

An ANN Model Trained on Regional Data in the Prediction of Particular Weather Conditions

et al. 2021

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Artificial Neural Networks (ANNs) have proven to be a powerful tool for solving a wide variety of real-life problems. The possibility of using them for forecasting phenomena occurring in nature, especially weather indicators, has been widely discussed. However, the various areas of the world differ in terms of their difficulty and ability in preparing accurate weather forecasts. Poland lies in a zone with a moderate transition climate, which is characterized by seasonality and the inflow of many types of air masses from different directions, which, combined with the compound terrain, causes climate variability and makes it difficult to accurately predict the weather. For this reason, it is necessary to adapt the model to the prediction of weather conditions and verify its effectiveness on real data. The principal aim of this study is to present the use of a regressive model based on a unidirectional multilayer neural network, also called a Multilayer Perceptron (MLP), to predict selected weather indicators for the city of Szczecin in Poland. The forecast of the model we implemented was effective in determining the daily parameters at 96% compliance with the actual measurements for the prediction of the minimum and maximum temperature for the next day and 83.27% for the prediction of atmospheric pressure.

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“…As a commonly used prediction method, machine learning has good nonlinear characteristics [ 25 , 26 ], convergence and a certain generalization ability. Therefore, machine learning technology has been widely used in the agricultural field in recent years [ 27 , 28 ], such as the study of material characteristics [ 29 , 30 ], the control of compression mechanisms [ 31 , 32 ] and the inspection of work quality [ 33 , 34 ].…”

Section: Introductionmentioning

confidence: 99%

Experimental study on creep properties prediction of reed bales based on SVR and MLP

Zhang

Huang³

et al. 2021

Plant Methods

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Background Reed has high lignin content, wide distribution and low cost. It is an ideal raw material for replacing wood in the paper industry. Reeds are rich in resources, but the density of reeds is low, leading to high transportation and storage costs. This paper aims to study the compression process of reeds and the creep behaviour of compressed reeds, and provide theoretical guidance for the reed compressor management, bundling equipment and the stability of compressed reed bales. Results We have established a multi-layer perceptron network prediction model for the creep characteristics of reeds, and the prediction rate R2 of this model is greater than 0.997. The constitutive equation, constitutive coefficient and creep quaternary model of the reed creep process were established by using the prediction model. The creep behaviour of the reed bale is positively correlated with the initial maximum compressive stress (σ0). During the creep of the reed, the elastic power and the viscous resistance restrict each other. The results show that the proportion of elastic strain in the initial stage is the largest, and gradually decreases to 99.19% over time. The viscoelastic strain increases rapidly with time, then slowly increases, and finally stabilizes to 0.69%, while the plastic strain accounts for the proportion of the total strain. The specific gravity of the reed increases linearly with the increase of creep time, and finally accounts for 0.39%, indicating that as time increases, the damage of the reed's own structure gradually increases. Conclusions We studied the relationship between the strain and time of the reed and the strain and creep behaviour of the reed bag under different holding forces under constant force. It is proved that the multi-layer perceptron network is better than the support vector machine regression in predicting the characteristics of reed materials. The three stages of elasticity, viscoelasticity and plasticity in the process of reed creep are analysed in detail. This article opens up a new way for using machine learning methods to predict the mechanical properties of materials. The proposed prediction model provides new ideas for the characterization of material characteristics.

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Overview of the use of artificial neural networks for energy‐related applications in the building sector

Cited by 18 publications

References 100 publications

State-of-the-art on research and applications of machine learning in the building life cycle

State-of-the-art on research and applications of machine learning in the building life cycle

An ANN Model Trained on Regional Data in the Prediction of Particular Weather Conditions

Experimental study on creep properties prediction of reed bales based on SVR and MLP

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