A comparative analysis of machine learning and statistical methods for evaluating building performance: A systematic review and future benchmarking framework

Ali, Abdulrahim; Jayaraman, Raja; Azar, Elie; Maalouf, Maher

doi:10.1016/j.buildenv.2024.111268

Cited by 10 publications

(3 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our approach was to apply selected SL algorithms deeming representative for recent applications [5][6][7][8][9][10][11][35][36][37][38][39][40][41] to the data simulated by the UTCI-Fiala model [27] at stage 2 of the UTCI development (Figure 1).…”

Section: Methodsmentioning

confidence: 99%

“…Statistical or machine learning (SL) is central to artificial intelligence (AI) applications [1][2][3] with potential relevance to environmental risk assessment, especially in settings with high dimensional input as for thermal stress indices [4]. There, they may assist or even attempt replacing the biometeorological expert judgement, as indicated by the increasing number of recent application studies in this field [5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Skills of Statistical Learning Algorithms in Thermal Stress Assessment Compared with the Expert Judgement Inherent to the Universal Thermal Climate Index (UTCI)

Bröde,

Fiala,

Kampmann

2024

Preprint

View full text Add to dashboard Cite

The objective of this paper was to verify the applicability of statistical learning (SL) compared to human reasoning with respect to the Universal Thermal Climate Index (UTCI), a complex tool for the assessment of outdoor thermal stress. UTCI is an equivalent temperature index based on the 48-dimensional output of an advanced model of human thermoregulation formed by 12 variables at four consecutive 30-minute intervals, which were calculated for 105642 thermal conditions from extreme cold to extreme heat. Comparing the performance of SL algorithms to the results accomplished by an international endeavor involving more than 40 experts from 23 countries, we found that random forests and k-nearest neighbors closely predicted UTCI values, but that clustering applied after dimension reduction algorithms (principal component analysis and t-distributed stochastic neighbor embedding) were inadequate for risk assessment in relation to the UTCI stress categories. This indicates a potential supportive role for SL, as it will not (yet) fully replace the bio-meteorological expert knowledge.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Skills of Statistical Learning Algorithms in Thermal Stress Assessment Compared with the Expert Judgement Inherent to the Universal Thermal Climate Index (UTCI)

Bröde,

Fiala,

Kampmann

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…However, the key difference between traditional statistical approaches and ML is that, in ML, a model learns from examples rather than being programmed with rules. For a given task, examples are provided in the form of inputs (called features or attributes) and outputs (called labels or classes) [44,45].…”

Section: Data Collectionmentioning

confidence: 99%

Predicting Academic Success of College Students Using Machine Learning Techniques

Guanin-Fajardo,

Guaña-Moya,

Casillas

2024

Data

View full text Add to dashboard Cite

College context and academic performance are important determinants of academic success; using students’ prior experience with machine learning techniques to predict academic success before the end of the first year reinforces college self-efficacy. Dropout prediction is related to student retention and has been studied extensively in recent work; however, there is little literature on predicting academic success using educational machine learning. For this reason, CRISP-DM methodology was applied to extract relevant knowledge and features from the data. The dataset examined consists of 6690 records and 21 variables with academic and socioeconomic information. Preprocessing techniques and classification algorithms were analyzed. The area under the curve was used to measure the effectiveness of the algorithm; XGBoost had an AUC = 87.75% and correctly classified eight out of ten cases, while the decision tree improved interpretation with ten rules in seven out of ten cases. Recognizing the gaps in the study and that on-time completion of college consolidates college self-efficacy, creating intervention and support strategies to retain students is a priority for decision makers. Assessing the fairness and discrimination of the algorithms was the main limitation of this work. In the future, we intend to apply the extracted knowledge and learn about its influence of on university management.

show abstract

Maximizing supply chain performance leveraging machine learning to anticipate customer backorders

Ali,

Jayaraman,

Azar

et al. 2024

Computers & Industrial Engineering

View full text Add to dashboard Cite

A comparative analysis of machine learning and statistical methods for evaluating building performance: A systematic review and future benchmarking framework

Cited by 10 publications

References 31 publications

Skills of Statistical Learning Algorithms in Thermal Stress Assessment Compared with the Expert Judgement Inherent to the Universal Thermal Climate Index (UTCI)

Skills of Statistical Learning Algorithms in Thermal Stress Assessment Compared with the Expert Judgement Inherent to the Universal Thermal Climate Index (UTCI)

Predicting Academic Success of College Students Using Machine Learning Techniques

Maximizing supply chain performance leveraging machine learning to anticipate customer backorders

Contact Info

Product

Resources

About