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Shamsi, Mohammad Haris
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Shamsi, Mohammad Haris
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Shamsi, Mohammad Haris
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Now showing 1 - 10 of 15
- PublicationForecasting the space heating demand using reduced-order modelling approaches for commercial buildings(University College Dublin. School of Mechanical and Materials Engineering, 2021)
; 0000-0002-5910-9081Energy-efficient retrofits have become crucial in the building sector as approximately 80% of the buildings in developed countries are over 10 years old and consume a major portion of total energy demand. The development and use of thermal models are an integral part of the design process in new and existing buildings due for refurbishment. Building energy performance simulation tools have become quite popular and are now being used to provide estimates of energy consumption at different scales. These tools implement various types of models which differ based on enclosed details. Not all these models are effective in terms of computation and cost. Recently, the total number of developed energy models has grown tremendously, which vary considerably in terms of characteristics and features. Hence, it is crucial to identify the type and characteristics of a model most suited to a certain purpose and situation. Alongside, the sophistication of simulation tools has significantly increased the number of user inputs, thereby, introducing uncertainty in simulation outputs. Grey-box modelling combines the advantages of data-driven and physical modelling approaches. Therefore, these models deliver an appropriate level of accuracy and are also computationally efficient. However, the design approach of grey-box models is often application-specific, for instance, the design approach for grey-box modelling of commercial buildings differs on a case by case basis. Furthermore, the scalability of these models is limited by the network order, which defines the level of complexity incorporated in the model. There is a need for a generalization framework to address the limitations associated with grey-box networks. As different applications require models of varying complexities, it is necessary to identify the model features, such as scalability, flexibility and interoperability, associated with different stakeholders of the building stock. Furthermore, previous uncertainty analysis studies have either failed to segregate the existing uncertainties or considered only one type of uncertainty in the analysis. The thesis introduces a novel generalizable methodology which formulates grey-box networks for different types of commercial buildings. Physical parameters and the nature of the operation of individual buildings constitute the key elements of the developed framework. The framework also relies on the past retrofit history and installed HVAC systems to deduce the order of the grey box network. The methodology further formulates an experimental design technique to associate and assess various model features pertinent to reduced-order grey-box models using pre-defined key performance indicators. Lastly, this research systematically identifies the various sources of uncertainty in grey-box models and develops a framework to include these sources in overall uncertainty quantification. This thesis uses a combination of real-time commercial buildings and reference building archetypes to test and validate the devised techniques. The devised approach reduces the complexities associated with the identification of the network order while maintaining the desired level of accuracy. The approach provides additional insights and information to designers considering novel alternative design approaches, where prior information may not be readily available. The feature assessment frameworks act as a decision support tool in the identification of appropriate model characteristics. The results of this study could support the current need for the assessment of consumption patterns of commercial building stock. The framework could further be implemented to study the post-retrofit heat consumption patterns at the individual building as well as the district scale. A probabilistic framework employing advanced techniques would allow stakeholders to identify influential inputs by considering the factors behind the risks in a given family of distributions.228 - PublicationGIS-Based Residential Building Energy Modeling at District Scale(International Building Performance Simulation Association, 2018-09-12)
; ; ; Urban planners often develop strategic sustainable energy planning processes that aim to minimize the overall energy consumption and CO2 emissions of buildings. Planning at such scales could be informed by the use of building energy modeling approaches. However, due to inconsistencies in available urban energy data and a lack of scalable building modeling approaches, a gap persists between building energy modeling and traditional planning practices. This paper develops a methodology based on bottom-up approach for GIS-based residential building energy modeling at a district scale. The methodology is applied to districts in Dublin and modeling results indicate where and what type of buildings have the greatest potential for energy savings throughout the city.512 - PublicationAn Intelligent Knowledge-based Energy Retrofit Recommendation System for Residential Buildings at an Urban Scale(2018-09-28)
; ; ; ; Buildings play a significant role in driving the urban demand and supply of energy. Research conducted in the urban buildings sector indicates that there is a considerable potential to achieve significant reductions in energy consumption and greenhouse gas emissions. These reductions are possible through retrofitting existing buildings into more efficient and sustainable buildings. Building retrofitting poses a huge challenge for owners and city planners because they usually lack expertise and resources to identify and evaluate cost-effective energy retrofit strategies. This paper proposes a new methodology based on machine learning algorithms to develop an intelligent knowledge-based recommendation system which has the ability to recommend energy retrofit measures. The proposed methodology is based on the following four steps: archetypes development, knowledge-base development, recommendation system development and building retrofitting or performance analysis. A case study of Irish buildings dataset shows that the proposed system can provide effective energy retrofits recommendation and improve building energy performance.224 - 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.328 - 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.
110 - 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.91 - PublicationFeasibility analysis of community-based PV systems for residential districts: A comparison of on-site centralized and distributed PV installationsPhotovoltaic systems are one of the most promising renewable energy technologies for on-site generation. Most of the techno-economic studies consider distributed standalone photovoltaic generation with little consideration of community-based standalone photovoltaic systems. Location-based case studies are required to provide economic and reliable photovoltaic systems to meet the peak loads of residential neighbourhoods in an optimized manner. This paper devises an integrated evaluation methodology; a combination of white-box energy modelling and black box photovoltaic design optimization. This research uses optimization methods to develop a quantitative optimized model for analysing the opportunities of centralized systems to adequately meet the demands of a residential neighbourhood and support the grid. This analysis includes three metrics including the level of the energy production, reliability of system for peak power and finally the capital cost of implementation in residential districts. Results indicate that the size of a centralized photovoltaic installation is less when compared to distributed installations to support a similar single peak load. The required converter size is reduced for the centralized system owing to the reduced system size. Centralized installations require fewer batteries to store surplus energy produced due to increased interaction of energy flows. Centralized installations are economically more viable than distributed ones.
108Scopus© Citations 9 - PublicationA generalization approach for reduced order modelling of commercial buildingsEnergy-efficient retrofits have become crucial in building sector as approximately 80% of buildings in developed countries are over 10 years old. Building simulation tools are now being used to provide estimates of energy consumption and implement various models which differ on the basis of enclosed details. Not all of these models are effective in terms of computation and the associated computational costs. This work devises a novel and generalized reduced-order grey-box modelling approach to predict the thermal behaviour of commercial buildings. The generalization approach reduces the order/complexity of model and lays out a general structure to obtain reduced-order models based on easily identifiable building metrics. We also implemented a forward-selection procedure to compare results obtained using a metrics-based approach. The network order obtained using metrics-based approach matches with the network order predicted by the forward selection procedure. The generalized structure would reduce the complexities involved in the dynamic simulation of urban building stock.
363Scopus© Citations 5 - PublicationA data-driven approach for multi-scale building archetypes development(Elsevier, 2019-11)
; ; ; ; Globally the building sector accounts for a significant portion of the overall energy demand and greenhouse gas emissions of any country. The most common approach for the collection of modeling and benchmarking data that can be used for predictions of energy performance at a national or urban scale is through classification of the building stock into representative archetypes. Developing such building archetypes is a complex task due to the difficulties associated with gathering detailed geometric and non-geometric data at an urban scale. Although existing databases and projects provide a valuable overview of a building stock, the information about buildings’ physical descriptions are not regularly updated. Moreover, these databases cover only the national top-level archetypes and lack crucial information related to city or district scale building stocks. The use of national scale archetypes requires many assumptions that may not hold true for energy modeling at urban or district scale. This paper proposes a multi-scale (national, city, county and district) archetype development methodology using different data-driven approaches. The methodology consists of following five steps: 1) data collection, 2) segmentation, 3) characterization, 4) quantification, and 5) modeling results. We developed a test case based on the available building stock data of Ireland. The test case used previously developed archetype geometries coupled with the parameters determined by the characterization process to calculate annual energy use (kWh) of buildings at a multiple-scales. The resulting archetypes at national, city, county and district scale are analyzed and compared against one another. The results indicate that significant differences occur in terms of energy modeling results when national scale archetypes are used to simulate the energy performance of buildings at the local scale. These multi-scale building archetypes will aid local authorities and city planners when analyzing energy efficiency and consequently, help to improve sustainable energy policy decisions.397Scopus© Citations 28 - PublicationA data-driven approach for multi-scale GIS-based building energy modeling for analysis, planning and support decision making(Elsevier, 2020-12-01)
; ; ; ; ; ; Urban planners, local authorities, and energy policymakers often develop strategic sustainable energy plans for the urban building stock in order to minimize overall energy consumption and emissions. Planning at such scales could be informed by building stock modeling using existing building data and Geographic Information System-based mapping. However, implementing these processes involves several issues, namely, data availability, data inconsistency, data scalability, data integration, geocoding, and data privacy. This research addresses the aforementioned information challenges by proposing a generalized integrated methodology that implements bottom-up, data-driven, and spatial modeling approaches for multi-scale Geographic Information System mapping of building energy modeling. This study uses the Irish building stock to map building energy performance at multiple scales. The generalized data-driven methodology uses approximately 650,000 Irish Energy Performance Certificates buildings data to predict more than 2 million buildings’ energy performance. In this case, the approach delivers a prediction accuracy of 88% using deep learning algorithms. These prediction results are then used for spatial modeling at multiple scales from the individual building level to a national level. Furthermore, these maps are coupled with available spatial resources (social, economic, or environmental data) for energy planning, analysis, and support decision-making. The modeling results identify clusters of buildings that have a significant potential for energy savings within any specific region. Geographic Information System-based modeling aids stakeholders in identifying priority areas for implementing energy efficiency measures. Furthermore, the stakeholders could target local communities for retrofit campaigns, which would enhance the implementation of sustainable energy policy decisions.242Scopus© Citations 50