Assessment of climate classification methodologies used in building energy efficiency sector

Gupta, Raj; Mathur, Jyotirmay; Garg, Vishal

doi:10.1016/j.enbuild.2023.113549

Cited by 9 publications

(3 citation statements)

References 74 publications

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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%

Advanced Energy Performance Modelling: Case Study of an Engineering and Technology Precinct

Tahmasebinia,

Lin,

et al. 2024

Buildings

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The global demand for energy is significantly impacted by the consumption patterns within the building sector. As such, the importance of energy simulation and prediction is growing exponentially. This research leverages Building Information Modelling (BIM) methodologies, creating a synergy between traditional software methods and algorithm-driven approaches for comprehensive energy analysis. The study also proposes a method for monitoring select energy management factors, a step that could potentially pave the way for the integration of digital twins in energy management systems. The research is grounded in a case study of a newly constructed educational building in New South Wales, Australia. The digital physical model of the building was created using Autodesk Revit, a conventional software for BIM methodology. EnergyPlus, facilitated by OpenStudio, was employed for the traditional software-based energy analysis. The energy analysis output was then used to develop preliminary algorithm models using regression strategies in Python. In this regression analysis, the temperature and relative humidity of each energy unit were used as independent variables, with their energy consumption being the dependent variable. The sigmoid algorithm model, known for its accuracy and interpretability, was employed for advanced energy simulation. This was combined with sensor data for real-time energy prediction. A basic digital twin (DT) example was created to simulate the dynamic control of air conditioning and lighting, showcasing the adaptability and effectiveness of the system. The study also explores the potential of machine learning, specifically reinforcement learning, in optimizing energy management in response to environmental changes and usage conditions. Despite the current limitations, the study identifies potential future research directions. These include enhancing model accuracy and developing complex algorithms to boost energy efficiency and reduce costs.

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Section: Building Energy Simulationmentioning

confidence: 99%

Advanced Energy Performance Modelling: Case Study of an Engineering and Technology Precinct

Tahmasebinia,

Lin,

et al. 2024

Buildings

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“…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%

Microclimate Zoning Based on Double Clustering Method for Humid Climates with Altitudinal Gradient Variations: A Case Study of Colombia

Mejía-Parada,

Mora-Ruiz,

Soto-Paz

et al. 2024

Atmosphere

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Climatic classification is essential for evaluating climate parameters that allow sustainable urban planning and resource management in countries with difficult access to meteorological information. Clustering methods are on trend to identify climate zoning; however, for microclimate, it is necessary to apply a double clustering technique to reduce the variability from former clusters. This research raised a climate classification of an emerging country, Colombia, using climatological models based on freely available satellite image data. A double clustering approach was applied, including climatological, geographic, and topographic patterns. The research was divided into four stages, covering the collection and selection of climatic and geographic data, and multivariate statistical analysis including principal components analysis (PCA) and agglomerative hierarchical clustering (HAC). The meteorological data were from reliable sources from the Center for Hydrometeorology and Remote Sensing (CHRS) and the National Renewable Energy Laboratory (NREL). The results showed that a total of 17 microclimates distributed across the country were identified, each characterized by a different threshold of the climatic and geographic factors evaluated. This subdivision provided a detailed understanding of local climatic conditions, especially in the mountain chains of the Andes.

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The development of an adaptive heat stress compensability classification applied to the United States

Guzman-Echavarria,

Middel,

Vecellio

et al. 2024

Int J Biometeorol

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Assessment of climate classification methodologies used in building energy efficiency sector

Cited by 9 publications

References 74 publications

Advanced Energy Performance Modelling: Case Study of an Engineering and Technology Precinct

Advanced Energy Performance Modelling: Case Study of an Engineering and Technology Precinct

Microclimate Zoning Based on Double Clustering Method for Humid Climates with Altitudinal Gradient Variations: A Case Study of Colombia

The development of an adaptive heat stress compensability classification applied to the United States

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