Application of Machine Learning Models for Fast and Accurate Predictions of Building Energy Need

Barbaresi, Alberto; Ceccarelli, Mattia; Menichetti, Giulia; Torreggiani, Daniele; Tassinari, Patrizia; Bovo, Marco

doi:10.3390/en15041266

Cited by 6 publications

(5 citation statements)

References 28 publications

(25 reference statements)

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“…In fact, starting from the outcomes of several energy simulations on a case study building, three ML algorithms-SVR, RF, and XGB-were tested for the assessment of the energy need of the building under several configurations. The works confirm some findings of the previous paper [18]:…”

Section: A Model Selectionsupporting

confidence: 92%

“…The features and models selection procedure has been described in a previous work [18]. In this case, the dataset is divided in predictions for cooling and heating energy needs, which corresponds to hot and cold seasons.…”

Section: B Models Training and Validationmentioning

confidence: 99%

“…Different approaches can be coupled to computer simulations to reduce the human labour, keeping or even improving the reliability of results. In fact, using codes to manage modelling and simulations (created by MatLab [14], Python and more) allows to add automation [15], [16], optimisation algorithms [17] and machine learning [18]. This approach drive to reduce drastically the required labour and even computational time, allowing to investigate and explore a countless number of building configurations and scenarios.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Simulations in agricultural buildings: a machine learning approach to forecast seasonal energy need

Ceccarelli

Barbaresi

Menichetti

et al. 2022

2022 IEEE Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)

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A fast and reliable estimation of building energy need is essential in agricultural building design, nonetheless, a large number of simulations is required to obtain better energy saving solutions. The aim of this work is to understand if machine learning can substitute numerical simulations and speed up the building design process and assess the incidence of specific architectural elements. Supervised regression models has been trained and tested in a data-set of thousands simulations performed on a case-study agricultural building. Among the algorithms, the tree-based Extreme Gradient Boosting showed the best performance. A study on model explainability has been carried out using SHAP and features importance, which is fundamental to help academics and professionals devise better design strategies for both new constructions and retrofitting interventions.

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Section: A Model Selectionsupporting

confidence: 92%

Section: B Models Training and Validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Simulations in agricultural buildings: a machine learning approach to forecast seasonal energy need

Ceccarelli

Barbaresi

Menichetti

et al. 2022

2022 IEEE Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)

Self Cite

View full text Add to dashboard Cite

show abstract

“…where I(x) is the indicator function.The margin function measures the extent to which the number of votes X, Y for the right class exceeds the maximum vote for any other error class-the larger the value, the higher the confidence of the classification. The generalization error is given by Equation ( 5) [30]:…”

Section: Random Forest Regressionmentioning

confidence: 99%

A Study on the Man-Hour Prediction in Structural Steel Fabrication

Wei,

Li,

Niu

et al. 2024

Preprint

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Longitudinal cutting is a most common process in steel structure manufacturing, and the man-hours of the process provide an important basis for enterprises to generate production schedules.However, currently the man-hours in factories are mainly estimated by experts, and the accuracy of this method is relatively low.In this study,we propose a system that predicts man-hours with history data in the manufacturing process and that can be applied in practical structural steel fabrication.The system addresses the data inconsistency problem by one-hot encoding and data normalization techniques,Pearson correlation coefficient for feature selection,and the Random Forest Regression(RFR) for prediction.Compared with the other three Machine Learning(ML) algorithms, the Random Forest algorithm has the best performance.The results demonstrate that the proposed system outperforms the conventional approach and has better forecast accuracy, so that it is suitable for man-hours prediction.

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“…The margin function measures the extent to which the number of votes X, Y for the right class exceeds the maximum vote for any other error class-the larger the value, the higher the confidence of the classification. The generalization error is provided by Equation ( 5) [38]:…”

Section: J) (X) I(h Avmentioning

confidence: 99%

A Study on the Man-Hour Prediction in Structural Steel Fabrication

Wei,

Li,

Niu

et al. 2024

Processes

View full text Add to dashboard Cite

Longitudinal cutting is the most common process in steel structure manufacturing, and the man-hours of the process provide an important basis for enterprises to generate production schedules. However, currently, the man-hours in factories are mainly estimated by experts, and the accuracy of this method is relatively low. In this study, we propose a system that predicts man-hours with history data in the manufacturing process and that can be applied in practical structural steel fabrication. The system addresses the data inconsistency problem by one-hot encoding and data normalization techniques, Pearson correlation coefficient for feature selection, and the Random Forest Regression (RFR) for prediction. Compared with the other three Machine-Learning (ML) algorithms, the Random Forest algorithm has the best performance. The results demonstrate that the proposed system outperforms the conventional approach and has better forecast accuracy so it is suitable for man-hours prediction.

show abstract

Application of Machine Learning Models for Fast and Accurate Predictions of Building Energy Need

Cited by 6 publications

References 28 publications

Simulations in agricultural buildings: a machine learning approach to forecast seasonal energy need

Simulations in agricultural buildings: a machine learning approach to forecast seasonal energy need

A Study on the Man-Hour Prediction in Structural Steel Fabrication

A Study on the Man-Hour Prediction in Structural Steel Fabrication

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