2023
DOI: 10.1016/j.enbuild.2023.113549
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Assessment of climate classification methodologies used in building energy efficiency sector

Raj Gupta,
Jyotirmay Mathur,
Vishal Garg
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Cited by 9 publications
(3 citation statements)
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“…Concurrently, there is a marked trend towards leveraging machine learning techniques to refine and increase the precision of energy consumption forecasts [25]. In pursuit of advancing the exactitude of these predictive models, researchers are increasingly integrating exhaustive assessments of environmental parameters, thereby enriching the analytical robustness of their scientific inquiries [26], researchers are progressively applying Geographic Information Systems (GIS) to emulate the environmental context of adjacent building aggregations, thereby achieving a more comprehensive and nuanced model of the interplay between built environments [27], and combine hybrid algorithms with the capacity to train using extended data sequences [28]. These techniques have demonstrated their effectiveness by boosting the accuracy of predictions to beyond 75%.…”
Section: Building Energy Simulationmentioning
confidence: 99%
“…Concurrently, there is a marked trend towards leveraging machine learning techniques to refine and increase the precision of energy consumption forecasts [25]. In pursuit of advancing the exactitude of these predictive models, researchers are increasingly integrating exhaustive assessments of environmental parameters, thereby enriching the analytical robustness of their scientific inquiries [26], researchers are progressively applying Geographic Information Systems (GIS) to emulate the environmental context of adjacent building aggregations, thereby achieving a more comprehensive and nuanced model of the interplay between built environments [27], and combine hybrid algorithms with the capacity to train using extended data sequences [28]. These techniques have demonstrated their effectiveness by boosting the accuracy of predictions to beyond 75%.…”
Section: Building Energy Simulationmentioning
confidence: 99%
“…This classification is based on parameters such as temperature, rainfall, and vegetation for climatic distribution. Although its use has spread globally, the authors of [41] indicated that it has lower accuracy in assessing the energy performance of buildings compared with other methodologies. Therefore, other climate classifications, such as the degree day method, have been developed to describe heating and cooling needs in specific climates.…”
Section: Key Research Findingsmentioning
confidence: 99%
“…Conversely, the use of clustering algorithms in climate classification is one of the most popular methods in recent times, especially for its contribution to urban planning and resource management sectors [41]. These methods are typically based on unsupervised analysis, providing a holistic assessment when these labels are unavailable [21].…”
Section: Key Research Findingsmentioning
confidence: 99%