Heat Loss Coefficient Estimation Applied to Existing Buildings through Machine Learning Models

Martínez-Comesaña, Miguel; Garrido, Lara Febrero; Granada, Enrique; Martínez-Torres, Javier; Martínez-Mariño, Sandra

doi:10.3390/app10248968

Cited by 20 publications

(10 citation statements)

References 63 publications

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“…In several studies, vector-based analyses has been used to resolve many problems related to the technical sciences. For example, one can find methods based on vector analysis in acoustics [20], building planning [21], and other technical topics [22]. However, during research using a large international database, we did not find any usage of vector analysis in the industrial management field.…”

Section: Introductionmentioning

confidence: 79%

Systematic Assessment of Product Quality

Drozd

Wolniak

2021

Journal of Open Innovation: Technology, Market, and Complexity

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The article describes an innovative metrizable idea for systemic assessments of product quality within the baking industry. Complex product quality analysis requires the employment of metrizability criteria for factors that impact the quality of the product, and these are called determinants. Therefore, such analysis is only possible with the use of systems engineering. A system represents the potential of a manufacturing process, of major impact on quality. Composites of the manufacturing process make up the determinants of bread quality, grouped into three sets: raw materials, manufacturing technology, and manufacturing organization and technique. This paper also contains methodological implications for the construction of algorithms for manufacturing process potential determinants. Metrizable product quality assessment is a very important issue in the context of its implementation in manufacturing companies. Its use allows for obtaining comprehensive data on the quality status of a product. It is an important tool for analyzing and forecasting modern quality trends. The method presented in the article is new, innovative, and practical; and its vector representation may prove useful in Quality 4.0. The method could be an important point of reference for managers, directors, and decision makers who must determine the best metrizability criteria for systemic product quality assessments, and could prove useful in Industry 4.0 in the bakery industry. The main value of the paper is the presentation of a new, extensive method for systemic assessments of product quality based on vector analysis in industrial organization. We trialed the method in the baking industry. We concluded that the method is a contribution to management science, especially in the field of quality management, because this approach is not used in business and is not described in relevant international literature.

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Section: Introductionmentioning

confidence: 79%

Systematic Assessment of Product Quality

Drozd

Wolniak

2021

Journal of Open Innovation: Technology, Market, and Complexity

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“…These datasets are necessary to obtain an efficient hyperplane that defines the boundary line for effective learning. MLP and SVR models are considered black box models with difficulty understanding internal mechanisms (Martínez-Comesaña et al, 2020;Stollfuss and Bacher, 2022), although research into model explainability is gaining attention (Molnar et al, 2020). The reduced performance observed in MLP and SVR models could be related to the complexity of their model parameters.…”

Section: Resultsmentioning

confidence: 99%

Prediction Of Chlorophyll-a As an Index of Harmful Algal Blooms Using Machine Learning Models

Busari,

Sahoo,

Harmel

et al. 2024

Journal of Natural Resources and Agricultural Ecosystems

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Highlights The monitoring of HABs can be improved using ML models for chlorophyll-a prediction. ML model selection for HABs monitoring depends on target objectives. Random forest model predicts chlorophyll-a better when the temporal dimension is not considered. The LSTM model is essential for making time-dependent chlorophyll-a predictions for HABs monitoring. Abstract. The complex dynamics of freshwater harmful algal blooms (HABs) necessitate proactive monitoring approaches to mitigate their impacts. The rapid breakthrough in computing prowess and statistical advances is triggering the development of data-driven techniques such as machine learning (ML) models, which have been shown in different fields to be instrumental in finding patterns for explaining relationships in observed data. This study assesses the ability of ML models for HABs monitoring in a lake using chlorophyll-a concentration as the index. The selected models for this study were regression tree, random forest (RF), multilayer perceptron (MLP), support vector regression (SVR), long short-term memory (LSTM), and gated recurrent unit (GRU) models, with the last two models able to consider the temporal sequence of obtained water quality datasets. The results showed that the RF model with R2, mean absolute error (MAE), and root mean square error (RMSE) of 0.87 µgL-1, 0.97 µgL-1, and 3.53 µgL-1, respectively, outperformed the SVR, MLP, and regression tree models. LSTM model with MAE and RMSE of 2.39 µgL-1 and 3.29 µgL-1, respectively, predicted temporal dynamics of chlorophyll-a better than GRU, although with more runtime, and showed the potential for developing real-time HAB monitoring and early warning systems. The findings reveal the robustness of the chosen ML models, thereby shedding light on crucial factors that necessitate careful deliberation by researchers and policymakers in determining the most suitable approaches for monitoring HABs. Keywords: Cyanobacteria, Early warning systems, Freshwater, HABs, Machine learning models.

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“…Tools and methods are needed that take into account the time, probability of events, threats, and the need for functional flexibility of buildings. This involves access to integrated information, interdisciplinary observation of phenomena and the structuring of large amounts of data (on the basis of D'Amico; A., Bergonzoni; G., Pini; A.; Currà, E, 2020; Marques, G.;Pitarma, R. A, 2019;Martínez-Comesaña, M. et al, 2020). Among the recent studies observed is, for example, the analysis of current limitations in residential buildings in relation to the phenomenon of the current epidemic.…”

Section: Introduction Health Aspects In Architectural Design On the Background Of Sustainability Goals Methodology Of Research In Case Stmentioning

confidence: 99%

The Need of Itegration of Health Aspects in Sustainability by Urban-Architectural Multicriteria Assessment Tools

Horn¹

2021

piF

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Current situation of worsening of the health of individuals and societies requires response in architectural design. The article discusses possibilities for integration of health aspects and well-being factors in investment process in pursue of sustainability, by utilizing the advantages of the green building rating systems and evaluation tools which are already present in international and national practice. The aim of this article is to indicate the need for an integral approach in creating a built environment that allows for a healthy life in accordance with the location and current social and other problems. The author believes that the recognition of architectural objects as an integral part of complex urban structures is a necessary approach that determines the integration of the discussed aspects in design. The process should also include raising public awareness, focusing on better planning and design tools, and systems for collecting data and measuring health impacts. Integrating health parameters with already existing sustainable design procedures and standards is crucial. The core of the study was the observation and analysis of the already built housing environment, planned and designed according to the principles of sustainable development, in order to determine the degree of taking into account environmental elements and aspects of health on the object and urban scale.

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Heat Loss Coefficient Estimation Applied to Existing Buildings through Machine Learning Models

Cited by 20 publications

References 63 publications

Systematic Assessment of Product Quality

Systematic Assessment of Product Quality

Prediction Of Chlorophyll-a As an Index of Harmful Algal Blooms Using Machine Learning Models

The Need of Itegration of Health Aspects in Sustainability by Urban-Architectural Multicriteria Assessment Tools

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