A data-driven approach for multi-scale GIS-based building energy modeling for analysis, planning and support decision making

Ali, Usman; Shamsi, Mohammad Haris; Bohacek, Mark; Purcell, Karl; Hoare, Cathal; Mangina, Eleni; O’Donnell, James

doi:10.1016/j.apenergy.2020.115834

Cited by 100 publications

(48 citation statements)

References 43 publications

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“…The data-driven building energy consumption prediction has been gaining raising research interest in recent years [12]: it has been widely used to predict building energy consumption of buildings with different functions, such as residential [13][14][15][16][17][18][19][20][21][22], office [23][24][25][26][27][28][29], institutional [30,31], educational [32,33] and commercial [34]. However, the application of the data-driven approach in large scale building stock energy consumption prediction is rather limited [34][35][36], this might because the majority of existing research about data-driven building energy consumption prediction is focused on residential or non-residential buildings only [12], although building stock usually consists of a mix of both types of building.…”

Section: Data-driven Building Energy Consumption Predictionmentioning

confidence: 99%

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Modelling heating and cooling energy demand for building stock using a hybrid approach

Yao

2021

Energy and Buildings

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Section: Data-driven Building Energy Consumption Predictionmentioning

confidence: 99%

“…AP=H/8760 (22) Where H is the total number of hours per annual when heating (or cooling) is available from the HVAC system.…”

Section: Predictor Variables Selectionmentioning

confidence: 99%

Modelling heating and cooling energy demand for building stock using a hybrid approach

Yao

2021

Energy and Buildings

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“…In [124], it is mentioned that a prediction based system has enhanced energy demand, system operations, and energy management. Also, it is mentioned in [125], that a prediction accuracy of a system has reached 88% using artificial intelligence based algorithm related to buildings' energy performance. The system has utilized information and analysis of metadata from a nation-wise level to obtain the prediction percentage.…”

Section: ) Prediction-oriented Ems-in-bsmentioning

confidence: 99%

Energy Management Systems and Strategies in Buildings Sector: A Scoping Review

et al. 2021

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Energy management systems in buildings (EMSs-in-Bs) play key roles in energy saving and management to which an efficient energy management system in buildings (EMS-in-Bs) design contributes. Different scope-based designs of EMS-in-Bs are reviewed. The objective is to highlight different scope-based designs of EMS-in-Bs in which scopes of reviewed papers aim to implement a function of, for example, "monitor energy performance", "estimate energy-use", or "control energy-use". This paper aims to constitute a comprehensive conception of how efficient such an EMS-in-Bs to perform more than one scope (i.e., function). Meaning, is the proposed EMS-in-Bs able to perform several sequential functions? This paper's contribution is to give a function-focused EMS's review utilizing the scope of reviewed papers. That is, reviewed papers are classified based on the scope/function the selected EMS-in-Bs is designed for. This could help select an EMS-in-Bs to perform certain scope/function(s). Another contribution is that, numerous EMSsin-Bs are reviewed in a classified way so that the most adequate EMS-in-Bs for a certain scenario considering the performed scopes/functions e.g., "monitor" are highlighted. Findings showed that "control-optimize"functioned EMS-in-Bs achieved highest energy-saving rates ~30% compared to "estimate-predict" with 10%. Findings, insights given by reviewed studies, current problems faced, future directions, and remarks are drawn in conclusion. Analysis done on reviewed papers has found that the highest and lowest averagedenergy saving rates were obtained with papers whose their scopes are implementing "control"-with-"optimize" and "estimate"-with-"predict", respectively. Energy saving rates for these two classes of scopes have been equal to 22.57% and 10%, respectively. We recommend that there is a need to enhance the estimation-and prediction-related EMS-in-Bs to achieve a higher energy saving rate.

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“…e finalized geographic information is displayed so that the users can access it for an easy and quick understanding of data and decision-making [34].…”

Section: E Benefits Of Geographic Information System (Gis)mentioning

confidence: 99%

The Adoption of Geographic Information Systems in the Public Sector of Saudi Arabia: A Conceptual Model

Alzahrani

Abdullah

Mohamed

et al. 2021

Mathematical Problems in Engineering

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The development of fuzzy sets in geographic information systems (GIS) arose out of the need to handle uncertainty and the ability of soft computing technology to support fuzzy information processing. Fuzzy logic is an alternative logical foundation coming from artificial intelligence (AI) technology with several useful implications for spatial data handling. GIS has been found to have a crucial role in the performance of public sector organizations (PSO). However, the literature shows no universal model to support and shed light on GIS adoption, which lessens the chances for effective GIS adoption and usage. Therefore, a new model is needed for successful adoption and eventual enhanced organization’s performance. Thus, there is a need to investigate the factors that can bring about GIS adoption. Models for GIS adoption in literature are few and far between, and the few that exist are not applicable as they do not cover all the significant factors that can contribute to adoption success. Hence, this paper brought a GIS adoption model for PSOs to promote their performance. The model was developed through the extension of the Technology Acceptance Model (TAM) in addition to the DeLone and McLean’s Success Model. The study involved interviews with ten experts in ranking the extracted factors, and data was analyzed through thematic analysis. On the basis of the obtained analysis findings, the fundamental factors were found to be significant in their effects, and GIS adoption sufficiently related to the overall performance. Thus, the study contributes to the body of knowledge by filling the gap in the literature.

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A data-driven approach for multi-scale GIS-based building energy modeling for analysis, planning and support decision making

Cited by 100 publications

References 43 publications

Modelling heating and cooling energy demand for building stock using a hybrid approach

Modelling heating and cooling energy demand for building stock using a hybrid approach

Energy Management Systems and Strategies in Buildings Sector: A Scoping Review

The Adoption of Geographic Information Systems in the Public Sector of Saudi Arabia: A Conceptual Model

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