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Alshehri, Fawaz
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Alshehri, Fawaz
Official Name
Alshehri, Fawaz
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Now showing 1 - 5 of 5
- PublicationApplication Of Intelligent Algorithms For Residential Building Energy Performance Rating Prediction(IBPSA, 2019-09-04)
; ; ; ; Energy Performance Certificates (EPC) provide an indication of buildings’ energy use. The creation of an EPC for individual building requires information surveys. Hence, these ratings are typically non-existent for entire building stock. This paper addresses these information gaps using machine-learning models. Developed models were evaluated with Irish EPC data that included approximately 650,000 residential buildings with 199 inputs variables. Results indicate that the deep learning algorithm produces results with highest accuracy level of 88% when only 82 input variables are available. This identified approach will allow stakeholders such as authorities, policy makers and urban-planners to determine the EPC rating for the rest of the building stock using limited data.266 - PublicationComparative Analysis of Machine Learning Algorithms for Building Archetypes Development in Urban Building Energy Modeling(ASHRAE, 2018-09-28)
; ; ; ; The most common approach for urban building energy modeling (UBEM) involves segmenting a building stock into archetypes. Development Building archetypes for urban scale is a complex task and requires a lot of extensive data. The archetype development methodology proposed in this paper uses unsupervised machine learning approaches to identify similar clusters of buildings based on building specific features. The archetype development process considers four crucial processes of machine learning: data preprocessing, feature selection, clustering algorithm adaptation and results validation. The four different clustering algorithms investigated in this study are KMean, Hierarchical, Density-based, K-Medoids. All the algorithms are applied on Irish Energy Performance Certificate (EPC) that consist of 203 features. The obtained results are then used to compare and analyze the chosen algorithms with respect to performance, quality and cluster instances. The K-mean algorithm preforms the best in terms of cluster formation.545 - PublicationUncertainty Quantification In Predictive Modelling Of Heat Demand Using Reduced-order Grey Box ModelsAs building energy modelling becomes more sophisticated, the amount of user input and the number of parameters used to define the models continue to grow. There are numerous sources of uncertainty in these parameters especially when a modelling process is being performed before construction and commissioning. Therefore, uncertainty quantification is important in assessing and predicting the performance of complex energy systems, especially in absence of adequate experimental or real-world data.The main aim of this research is to formulate an uncertainty framework to identify and quantify different types of uncertainties associated with reduced-order grey box energy models used in heat demand prediction of the building stock. The uncertainties are characterized and then propagated using the Monte-Carlo sampling technique. Results signify the importance of uncertainty identification and propagation within a system and thus, an integrated approach to uncertainty quantification is necessary to maintain the relevance of developed models.
243 - PublicationA Framework To Assess The Interoperability Of Commercial Buildings At A District Scale(International Building Simulation Association England, 2018-09-12)
; ; ; Expensive control technology coupled with absence of a proper framework result in buildings that operate independently for their entire operating life. This paper introduces a framework to assess the potential of buildings to function together using heat load demand patterns and buildings thermal mass. Buildings are characterized as possessing variable and stable heat demand patterns and internal conditions are modified to achieve a peak heat demand reduction. Results indicate 8% reduction in overall peak heat demand when two buildings are operated together. The analysis clearly establishes the significance of an integrated energy system that leads to a reduction in peak loads.212 - PublicationExtending IFC to support thermal comfort prediction during design(European Council on Computing in Construction, 2019-07-12)
; ; ; ; ; During the early design stage, designers often rely on general rules of thumb to make critical decisions about the geometry, construction systems and materials without fully evaluating their effects on indoor thermal environment requirements and constraints. Currently, reviewing a design’s sustainability requires designers to spend a significant amount of time manually extracting Thermal Comfort (TC) data from BIMs because of the tedious nature of this task. This paper is motivated by the absence of a standard method and a schema for extracting the necessary data for an automated TC assessment of building designs. The aim is to generate a reusable and retrievable set of Exchange Requirement’s for BIM-based BTCS to facilitate efficient data extraction and exchanges from design models using the IFC file format. Furthermore, we develop an MVD mechanism that provides a structured framework for the definition and exchange of the target data as a step towards standardisation and production of BTCS related information, the results from which contribute to a proposed MVD. The application of the MVD in building design has the potential to improve the early-stage TC assessment of design alternatives. Further, it could reduce the time required to conduct the assessment, increase the reproducibility of results, and formalises the method used.816