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Perspective

GIS for the Potential Application of Renewable Energy in Buildings towards Net Zero: A Perspective

by
Yang Li
and
Haibo Feng
*
Department of Wood Science, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
*
Author to whom correspondence should be addressed.
Buildings 2023, 13(5), 1205; https://doi.org/10.3390/buildings13051205
Submission received: 3 March 2023 / Revised: 15 April 2023 / Accepted: 30 April 2023 / Published: 2 May 2023
(This article belongs to the Section Building Energy, Physics, Environment, and Systems)
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Abstract

:
Environmental, economic, and social activities involve inherent spatial dimensions. The geospatial information system (GIS), a platform containing principles, methods, and tools to link, create, visualize, analyze, and model artificial activities and environment, provides the possibility to develop sustainability in the building sector. With globally political collaborations across governments, the demands to manage and visualize sustainable data (e.g., building energy and environment with geospatial reference) and implement more rigorous building modelling are increasing. A systematic mapping at multiple scales will help urban engineers, architectural engineers, policymakers, and energy planners identify emission hotspots, locate spatial resources, restructure district energy mix, and achieve net zero energy targets. To achieve net zero energy goals, it is crucial to minimize energy consumption, improve energy efficiency, and most importantly, apply renewable energy in buildings. However, these processes imply many aspects and challenges, regarding e.g., data availability, scalability, integrability, and a lack of clear and applicable frameworks. In this conceptional perspective paper, we aim to explore the potential of applying and installing renewable energy in net zero energy buildings using the GIS. More specifically, the described virtual framework will effectively support policy- and decision-makers in optimizing the energy structure, reducing building emissions, and applying renewable energy technologies. We also present challenges, limitations, and future directions for real practice.

1. Introduction

The United Nations (UN) projections suggest that around 68% of the world’s population will choose to live in the metropolitan areas by 2050 [1]. Rising tensions between living needs and limited resources raise challenges for supporting people’s basic residential and industrial activities. Supply violations, energy costs, and raw material shortages cause difficulties in developing the building environment, while the use of fossil fuels has a great negative impacts on nature. With the significant influence of anthropogenic activities and the growing population, the demands for improving living standards and, at the same time, alleviating the reliance on resources and energy in people-intensive urban areas, are attracting more and more attention [2].
Net zero energy buildings provide the potential for human beings to face environmental challenges by reducing the direct energy use, improving energy efficiency, and implementing renewable energy [3]. Renewable energy, such as wind and solar energy, is considered to emit little to no greenhouse gases while being widely available, and is more economic compared to energy from most conventional fuels [4]. Therefore, the selection of energy alternatives for buildings such as renewable energy technologies, has become necessary and crucial to achieve building sustainability.
The selection and management of renewable energy relies on energy spatial data in terms of availability, density, and distribution. Geospatial technologies, including GIS, remote sensing (RS), and global positioning system (GPS), are widely applied to collect and analyze geospatial data in a large number of areas, such as cartography, hydrography, and photogrammetry [5]. The GIS databases can manage and display real-earth geographical information on one map, for example, to deploy resources in coordinate systems (with X, Y, Z values), monitor resource availabilities and changes, and identify precise locations [6,7].
After a review of relevant concept, GIS including geospatial, geometric, and topological information, can also be widely applied in the building sector, for example, for (1) the location/site selection of buildings, considering energy supply and demands, economic parameters, and dynamic local environmental conditions [8]; (2) environmental assessment integrated with GIS, that can explain comprehensive sustainable standards of residential buildings with the consideration of environmental indicators and three-dimensional (3D) models of buildings [9,10]; (3) the estimation of a building energy performance at a local, regional, or larger scale, including energy indicators and building energy characteristics [11,12,13]. These studies presented many opportunities for geospatial technologies to improve the built environment in diverse aspects.
To promote and support a global sustainable development in the building sector, the United Nations Environment Program (UNEP) started the Sustainable Buildings and Climate Initiative (SBCI) in 2006, aiming at improving energy efficiency and alleviating the climate change [14]. Similarly, in responding to the United Nations’ global Sustainable Development Goals, Canada has taken actions on providing strategies and developing building codes for retrofitting both new constructed and current existing buildings. Specifically, a “net zero energy ready” model building code will be developed by 2030 with the adaptation to provinces and territories [15]. The key objective of these launched policies is to help public and private stakeholders in the building sector receive the needed information to mitigate building-relevant emissions. In this regard, the support of mappings at the community or city scale is crucial for different stakeholders such as policy- and decision-makers.
Energy availability is one of the challenges to applying renewables, considering power quantities and qualities, resource locations, energy-acquiring abilities, and financial limitations [16]. Integrated with GIS technologies, the availability of resources can be well-assessed even at a distance to support decision making and develop energy-saving strategies. The GIS technologies are not new and innovative tools to manage recourses, and several studies indicate the potential to visualize energy-related projects [17,18]. More specifically, GIS has been applied to design renewable-energy infrastructure and support energy system planning, such as the potential assessment of renewable energy applications, energy simulation and modelling, building energy demand assessment, site selection, and graphical impact assessment [19].
However, only a few studies assessed and simulated multi-level energy demands, identified potential renewable energy, reduced energy use, and estimated/improved energy efficiency with geospatial information. The integration of building information and energy design and the assessment with geospatial information systems involve many aspects and challenges, such as data availability, scalability, and integrability, and a lack of clear and applicable frameworks. In real practice, urban planners and energy structure decision-makers are facing challenges when applying sustainable strategies without the support of mapping beyond single buildings, and the difficulties will increase at a distance.
In this paper, we selected and reviewed a wide range of literature studies to investigate the potential and characteristics of the GIS to design, optimize, and operate renewable energy systems in buildings towards net-zero targets. To explore the principles of GIS, we discuss the GIS application in buildings to understand how geospatial information can support the potential assessment, energy simulation, and infrastructure design. According to the examples and scenarios described in the literature, we also evaluated the assumptions, challenges and difficulties of GIS technologies in the built environment. We discuss the use of geospatial techniques for applying “net zero energy” technologies in buildings, which set up the future outline of sustainable building design and retrofitting strategies across disciplines and then consequentially affect the operational performances of buildings. We argue that a more systematic method integrated with the GIS should be developed so as to develop building sustainability, which serves as a decision making-supporting tool to plan, design, and manage sustainable communities and cities. Future studies could practically apply and demonstrate the proposed application of the framework to explore the “net zero energy” potentials in buildings using the GIS.

2. Research Methodology

The methods employed in this study were the Web of Science search engine and keyword queries. Peer-reviewed research articles were selected based on the research questions. The keywords used for searching included “building and net zero energy” “GIS and building”, and “GIS and net zero energy building”, covering the period from 2010 to 2022. We reviewed and screened the relevant articles, and the results are reported in the following Section 3.

3. Literature Review

3.1. GIS for Energy Management

The GIS is defined as a system including hardware, software, and data to collect, manage, analyze, and display various forms of geospatial information [20]. Practitioners can simply observe, recognize, construct. and visualize data in a variety of ways. Those data will help understand physical feature relations, shapes, and trends in the format of maps, reports, and charts. The GIS can also be summarized as a set of computational decision-/policy-making-supportive gears for integrating, analyzing, processing, and displaying spatial data from diverse sources. Thus, it is an influential tool to manage sustainable energy-related data with the ability of analyzing many spatial-inherent data.
The GIS was initially applied to conduct landform and forest surveys. Later, GIS techniques have been widely applied to visualize resources, roads, weather, etc. Meanwhile, spatial analysis databases and relevant tools were developed with the development of GIS data. By sharing data, GIS practitioners can easily access, collect, and compare geospatial data applied in different fields. Updated information can be obtained by overlaying layers. GIS data are free and easy to collect in many countries; therefore, the operators can apply them in different research fields.
The main improvement in the GIS history was the Digital Elevation Model (DEM) development that is also referred to as digital topography [21]. Various sources can generate the DEM, including raw Light Detection and Ranging (LiDAR) data, old topographic maps (terrain map), satellite images, or aerial photographs. The DEM can shortly shape landforms with various hydrological and morphometric characteristics. Based on different sources and data-processing procedures, the results can be different as regards accuracy [22]. A review about the GIS application for renewable energy is shown in Table 1. According to the literature review, the most common applications include identifying (1) appropriate locations/infrastructures, (2) potential energy sources/applications, and (3) energy demands associated with the compacity and size of the renewables.
Moderate-resolution imaging satellites, such as Landsat, attached to a thermal reflected radiometer, such as the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), can be effectively applied to recognize the potential distribution of geothermal energy on earth. Various remote sensing techniques can be applied as direct or indirect evidence of natural heat transfer processes, including multispectral, hyperspectral, thermal, and microwave remote sensing.
The development and optimization of wind energy rely on remote sensing and GIS techniques. The sonic detection and ranging (SODAR), LiDAR, and synthetic-aperture radar (SAR) are applicable to explore optimal design conditions for wind power, including wind power density, or its proximity to the other electric grids. The SAR data are helpful to develop detailed assessment methods for offshore winds. In addition, it is applied in mineral, biomass and solar energy mapping. The satellite images apply both passive (optical) and active (radar and LiDAR) sensors and can effectively evaluate several forest biophysical parameters of biomass energy [7]

3.2. GIS Applied in the Building Sector

Several studies have indicated and encouraged the potential application of GIS in the built environment [10,12,33,34]. The energy efficiency of buildings has been extensively discussed, for example, building performance improvement, such as window glass, wall insulation, and thermal ventilation system improvement (like Heating, Ventilation, Air Conditioning, HVAC, and Building Management System, BMS) [35]. This detailed geometric and semantic information can act as a digital twin to reflect a building structure; however, the ignored outside boundaries of the building, including its functional, physical, and spatial environment, should be considered on a large scale to maintain sustainable design and operation [20]. By integrating the technologies of digital twins, the design and planning of sustainable communities, cities, or even larger areas can be achieved using city information modeling (CIM) [36]. This information management system supports, models, monitors, simulates, assesses a city’s operation. Furthermore, this modelling framework with accurate data can be developed to support the building of intelligent cities, including networks, application, service systems [37].
In addition to the research discussed above, GIS-integrated tools are applied to multi-parameter modelling, e.g., (1) forecasting energy demands and (2) design, planning, and the location selection of district heating networks. More attributes/layers are collected and analyzed including energy population, building efficiency, household number, and local climates (heating/cooling degree days), to evaluate the potential of renewable-based district heating networks [38].
Recently, to minimize energy consumption and greenhouse gas (GHG) emissions, the retrofitting and upgrading of existing buildings towards net zero energy have attracted large attention in particular with regard to the governance requirements of the building code [39]. A review about the GIS applied methods and tools for achieving net zero energy targets is presented in Table 2. The 3D GIS-based tools can be useful to evaluate the environmental benefits of building-integrated photovoltaics (BIPV) installed in the façades of residential buildings [40]. Meanwhile, the potential of renewable energy applications, building envelope retrofitting, and urban energy balance is assessed to provide key information for energy design and planning [40].
Energy simulation at the city level has been developed many years ago, and some up-scaling Urban Building Energy Models (UBEM) already exist, such as the City Building Energy Saver (CtiyBES) developed by the Lawrence Berkeley National Laboratory, the Urban Modeling Interface (UMI) developed by the MIT Sustainable Design Lab, CitySim, Simstadt, City Fast Fluid Dynamics (City FFD), and city-related 3D geospatial data in an XML-based format (CityGML) [43,44]. Generally, there are two types of UBEM, including aggregated archetype and data-driven/physical-based models. The city-scale energy demands are influenced by urban geometry parameters in terms of building coverage ratio, building density, building orientation, building height, aspect ratio or height-to-width ratio, plan area ratio, plan area index, floor area ratio, green area ratio, sky view factor, and normalized difference vegetation index (NDVI) [45]. Up to now, only a few studies investigated the “net zero energy” potential considering all buildings in a city.
The keys to shift the analysis scale from a single building to larger areas, match the demands of distributed energy resource systems (such as power generation and multi-energy storage systems), and manage urban planning and operation, are to collect basic and geospatial data, including entire building inventories (building type, building envelope, and life span) and data from various components outside the buildings, such as climate, transportation, public utilities, and energy availability [20].

4. Potential Applications to Retrofit Buildings towards Net Zero Energy Using GIS

In Canada, the emission of the building sector accounts for 12% of national emissions, and those emissions derive form heating needs [39]. Governments are taking action to improve the energy efficiency of buildings for supporting indigenous, rural, and remote indigenous communities, including developing a model building code towards “net zero energy ready buildings” and improve building energy efficiency and performance [39]. To achieve the net-zero target, clean energy projects have been launched to alleviate the reliance on conventional energy and promote the transit to clean energy [51]. Meanwhile, the city of Vancouver, Canada, is investing in climate-resilience buildings and infrastructure, e.g., with the application of renewable energy technologies and planning to achieve “zero emissions” by reducing energy consumptions and emissions in new constructed buildings by 2030 [52].
To achieve the sustainable goals and assess the potential application of net zero energy technologies, energy mapping and planning play important roles for local and federal government decision making. The value of mapping provides basic information to investigate the feasibility of policies, targeting energy saving and production. More specifically, GIS-based tools have been developed to assess the suitability of renewable energy used in buildings towards net-zero targets. Building renovation, renewable energy installation, and power system improvements can be directly assessed. According to the required investment and the following lifetime returns, building renovation or power system improvements can individually be considered, particularly in the investigative part of site selection. That means different scenarios are designed, considering building retrofitting, solar PV installation, and power system improvements. Then, different procedures are compared based on the different scenarios listed above in order to evaluate the contribution of each procedure.
Many UBEM projects have applied city-wide GIS data combined with LiDAR to generate building footprints [53]. Based on the above reviewed literature, we summarize and present a framework to assess the potential to apply “net zero energy” technologies in buildings at a community or city scale, as can be observed in Figure 1. The GIS integrated to ground-based monitors can obtain real-time information about the earth surface and the building envelope, contributing significantly to identify natural renewable resources as well as energy-saving and efficient strategies. The early steps include collecting/integrating data in three dimensions, including environment (historical climate data such as temperature, precipitation, wind speed, etc.), buildings (building envelope, building sizes and types, time span, and energy/water use), and impacts (environmental, economic, and political impacts). These data are from different sources, e.g., the literature, surveys, building performance databases, energy performance certificate databases, and in the same data format. Building geometrical parameters and data collection and preparation are the most crucial and time-consuming part in a UBEM analysis.
The following steps are mainly to geo-reference and digitize the building modelling data with accurate geospatial information. Considering the economic and environmental payback time [54], impact inventories (such as building materials, structure, and energy and water use) [54], and energy structure strategies (such as passive design and building insulation) [3], a building energy performance at a multi-level scale integrated with GIS mapping will be demonstrated and described.
Furthermore, the modelling results will be used to test various urban building planning strategies and scenarios and identify the economically and environmentally appropriate areas for applying renewable energy, so as to support sustainable energy policy and decision making. Generally speaking, geospatial data may be used as direct or indirect signals of resources availability. For example, direct evidence relevant to surface data can be directly connected to geothermal activities, based on obvious physical processes. In addition, indirect identification needs further clarification, e.g., additional data to verify that the spatial information is relevant to the availability of renewable energy [7].
Figure 2 presents the geographical objects and the attributes used for the application of renewable energy in buildings in the GIS environment. The possible building density in an area is related to regional objects which can be observed using national census data on lands. Spatial aggregation and querying tools in the GIS environment will be applied to evaluate and present the energy demands in buildings. The identified energy production systems are initially located at the centroids of the regional objects, and their mechanical features are defined. The collecting renewable energy area for each single building is considerws the maximum acceptable energy transportation distance. The cost of electricity production for every identified power can be then assessed and evaluated using a road and grid network [29,30]
Different from singular building energy simulation and modelling, this framework can access larger levels of urban building information including building footprints, building height, and building density. Based on the provided information, non-geometric information, such as building envelope, types, and structure, can also be assessed considering archetype buildings (standards or reference buildings).
To sum up, the methods are designed to generate a virtual framework of residential and commercial districts at the city level, starting from Vancouver to other cities across Canada. The first step will apply GIS mapping tools to collet accessible geometric data including the year of built and the building envelope characteristics, and the following steps will use the collected data to evaluate energy demands/use, assess shadow impacts, and explore the renewable energy potential for the whole city. Once the model, including all useful attributes, is reproduced in the GIS environment, it can be added to a scene to assess the “net zero energy” potentials considering energy reduction plus energy efficiency improvement strategies and renewable energy applications.
The GIS-based framework of buildings implementing renewable energy can be used to predict or forecast building energy performance at various scales, such as on the district, city, province, and country level. Starting from the bottom, shapefiles of each scale are aggregated to the geospatial mapping at the largest scales. For example, single buildings are aggregated in a cluster area, and all buildings inside the area are aggregated into districts. The aggregated district level can shape the 3D and 2D building models of cities and countries. The completed mapping integrated with climate prediction scenarios can be applied to predict buildings design and analysis and test energy considerations and community planning.

5. Limitations and Future Directions in the Built Environment

This study aimed to illustrates and map a GIS-based framework applied to achieve urban net-zero emission targets. Challenges when using GIS-integrated methodologies to model and assess renewables in the building sector still exist. The integrated models need to consider the parameters of both geographical and energy dimensions, although data sources, structures, formats, and quality are highly variable. This integration may cause complexity and incompatibility. In addition, the availability of data and the level of granularity varies largely concerning building-specific parameters among different regions. In most cases, it is difficult to access and collect sufficient data to support buildings’ planning and design, retrofitting, as well as optimization, which cover landform, local weather conditions, 3D building information, and demographic information and density [19].
Although the challenge of data availability and accuracy is solved in site-specific and singular buildings on various scales (regional, urban, and local), future improvement of integrative models, with the consistency of data types and propitiate systems, connecting energy operators and topology networking, needs to be explored and achieved in many respects [55]. These systematic methods include 3D buildings and air transport models, heat and energy network expansion design and planning, 3D analysis algorithms for energy modelling, ray racing algorithms, high-resolution building analysis, considerations of building components, building information modeling (BIM) and GIS integration, and renewable energy generation systems analysis [56,57,58].
Conducting a comprehensive assessment with one software for entire buildings requires adaptations, considering energy use and efficiency, renewable energy applications, and power system upgrade. Therefore, we encourage the development and design of a platform considering numerous existing (commercial and open source) software and tools to make this assessment. However, data sources, structures, formats, and quality are highly variable. GIS-based energy system modelling requires a large amount of underling data, while data collection and granularity usually varies, considering different purposes and scales. For example, energy demands and production, renewable energy installations, and demographic density are simply accessed at a community or local level; however, these levels of data cannot be well collected and fit on a large scale, such as at a provincial and a territorial level. The former is expected to achieve higher accuracy, as a specific building is selected, including detailed building elements and configuration. By contrast, the latter can only collet partially reliable data with limited geospatial accuracy. Therefore, when integrating energy plans and GIS, researchers should be consistently: (1) use a shared scale (e.g., community level or city level) for all the data required for analysis or results verifications; (2) choose a fast simulation time; (3) ensure the required data privacy aspects.
In addition, it is crucial to propose a method that allows multi-scale processes and storage. One choice is to implement a geospatial database with a hierarchical structure, in which all the smaller units generate larger units. Small unit data (e.g., communities) can be aggregated to larger units (e.g., cities) using SQL queries. The same strategy could also be implemented to scales of finer resolution, including city regions, housing blocks, street sections, road grids, and individual buildings. Another choice for aggregation is to implement a regular grid on a lower resolution than the original data, e.g., 100 m by 100 m for heat and energy use modelling and visualization.
Future directions of the work include the investigation of the social aspect of the framework, considering human occupations, activity demands, and functional space. In addition, to expand to a larger scale such as national and intercontinental scales, building and environment databases will be involved and applied to support the analyses. In this regard, the use of remote sensing and other big data approaches is crucial. It is worth considering the integration with available databases such as Planet and Landsat.

6. Discussion and Conclusions

This study focused on reviewing the GIS applied in the building sector and exploring the potential of applying “net zero energy” technologies in urban buildings, including energy reduction, energy efficiency improvement, and renewable energy applications. Based on the review of relevant research identified by searching key words, it appeared that the GIS has been widely used in renewable energy identification, building footprints generation, and energy management.
This study proposes a virtual framework of energy performance evaluation integrated with GIS mapping in residential and commercial buildings on a multi-scale level. The models in this study represent a potential building dataset to manage energy by adding some elements and attributes in the GIS environment. The primary needs for the proposed framework are data collection, integration, data quality, and availability. Future studies will apply the method in residential and commercial districts at the city level, starting from Vancouver to other cities across Canada. In addition, the completed mapping integrated with climate prediction models could be applied to predict a building performance and consider energy optimization/community planning.
The following recommendations are made to better apply the GIS in construction towards net zero building. The key is to establish a public data environment, and within these platform, effective information and data can be easily accessed, allowing data sharing and reuse in various formats. The data include urban castral maps, building information, construction modelling and projects, and energy networks. In addition, comprehensive software and applications are recommended to be developed; thus, researchers can collaborate on the functional incorporation of different data. With the use of energy mapping, comprehensive energy strategies towards net zero buildings, such as energy reduction methods, energy improvement strategies, and renewable energy applications, can be set up and managed efficiently. To address local energy challenges and achieve sustainable goals it is necessary that federal, provincial, and municipal governments adapt and modify policies, work together, and provide support to their communities.

Author Contributions

Y.L.: Conceptualization, Methodology, Writing—original draft preparation, review and editing. H.F.: Writing—review and editing, Supervision. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. United Nations. World Urbanization Prospects The 2018 Revision; United Nations: New York, NY, USA, 2018. [Google Scholar]
  2. Huang, T.; Shi, F.; Tanikawa, H.; Fei, J.; Han, J. Materials Demand and Environmental Impact of Buildings Construction and Demolition in China Based on Dynamic Material Flow Analysis. Resour. Conserv. Recycl. 2013, 72, 91–101. [Google Scholar] [CrossRef]
  3. Li, Y.; Arulnathan, V.; Heidari, M.D.; Pelletier, N. Design Considerations for Net Zero Energy Buildings for Intensive, Confined Poultry Production: A Review of Current Insights, Knowledge Gaps, and Future Directions. Renew. Sustain. Energy Rev. 2022, 154, 111874. [Google Scholar] [CrossRef]
  4. Covert, T.; Greenstone, M.; Knittel, C.R. Will We Ever Stop Using Fossil Fuels? J. Econ. Perspect. 2016, 30, 117–138. [Google Scholar] [CrossRef]
  5. de Vries, W.T. Trends in the Adoption of New Geospatial Technologies for Spatial Planning and Land Management in 2021. Geoplanning 2021, 8, 85–98. [Google Scholar] [CrossRef]
  6. Habeeb, N.J.; Weli, S.T. Combination of GIS with Different Technologies for Water Quality: An Overview. HighTech Innov. J. 2021, 2, 262–272. [Google Scholar] [CrossRef]
  7. Avtar, R.; Sahu, N.; Aggarwal, A.K.; Chakraborty, S.; Kharrazi, A.; Yunus, A.P.; Dou, J.; Kurniawan, T.A. Exploring Renewable Energy Resources Using Remote Sensing and GIS—A Review. Resources 2019, 8, 149. [Google Scholar] [CrossRef]
  8. Kumar, S.; Bansal, V.K. A GIS-Based Methodology for Safe Site Selection of a Building in a Hilly Region. Front. Archit. Res. 2016, 5, 39–51. [Google Scholar] [CrossRef]
  9. Xu, Z.; Coors, V. Combining System Dynamics Model, GIS and 3D Visualization in Sustainability Assessment of Urban Residential Development. Build. Environ. 2012, 47, 272–287. [Google Scholar] [CrossRef]
  10. Vacca, G.; Quaquero, E. BIM-3D GIS: An Integrated System for the Knowledge Process of the Buildings. J. Spat. Sci. 2020, 65, 193–208. [Google Scholar] [CrossRef]
  11. Fabbri, K.; Zuppiroli, M.; Ambrogio, K. Heritage Buildings and Energy Performance: Mapping with GIS Tools. Energy Build. 2012, 48, 137–145. [Google Scholar] [CrossRef]
  12. Yu, H.; Wang, M.; Lin, X.; Guo, H.; Liu, H.; Zhao, Y.; Wang, H.; Li, C.; Jing, R. Prioritizing Urban Planning Factors on Community Energy Performance Based on GIS-Informed Building Energy Modeling. Energy Build. 2021, 249, 111191. [Google Scholar] [CrossRef]
  13. Mutani, G.; Todeschi, V. GIS-Based Urban Energy Modelling and Energy Efficiency Scenarios Using the Energy Performance Certificate Database. Energy Effic. 2021, 14, 47. [Google Scholar] [CrossRef]
  14. UN-Buildings and Climate Change—Status, Challenges and Opportunities-20073934. 2007. Available online: https://www.unep.org/resources/report/buildings-and-climate-change-status-challenges-and-opportunities (accessed on 30 April 2023).
  15. Asaee, S.R.; Ugursal, V.I.; Beausoleil-Morrison, I. Development and Analysis of Strategies to Facilitate the Conversion of Canadian Houses into Net Zero Energy Buildings. Energy Policy 2019, 126, 118–130. [Google Scholar] [CrossRef]
  16. International Energy Agency. Renewable Energy Market Update—Outlook for 2020 and 2021; International Energy Agency: Paris, France, 2021. [Google Scholar]
  17. Shorabeh, S.N.; Firozjaei, H.K.; Firozjaei, M.K.; Jelokhani-Niaraki, M.; Homaee, M.; Nematollahi, O. The Site Selection of Wind Energy Power Plant Using GIS-Multi-Criteria Evaluation from Economic Perspectives. Renew. Sustain. Energy Rev. 2022, 168, 112778. [Google Scholar] [CrossRef]
  18. Bharti, A.; Paritosh, K.; Mandla, V.R.; Chawade, A.; Vivekanand, V. Gis Application for the Estimation of Bioenergy Potential from Agriculture Residues: An Overview. Energies 2021, 14, 898. [Google Scholar] [CrossRef]
  19. Resch, B.; Sagl, G.; Trnros, T.; Bachmaier, A.; Eggers, J.B.; Herkel, S.; Narmsara, S.; Gündra, H. GIS-Based Planning and Modeling for Renewable Energy: Challenges and Future Research Avenues. ISPRS Int. J. Geoinf. 2014, 3, 662–692. [Google Scholar] [CrossRef]
  20. Xia, H.; Liu, Z.; Maria, E.; Liu, X.; Lin, C. Study on City Digital Twin Technologies for Sustainable Smart City Design: A Review and Bibliometric Analysis of Geographic Information System and Building Information Modeling Integration. Sustain. Cities Soc. 2022, 84, 104009. [Google Scholar] [CrossRef]
  21. Guth, P.L.; van Niekerk, A.; Grohmann, C.H.; Muller, J.P.; Hawker, L.; Florinsky, I.V.; Gesch, D.; Reuter, H.I.; Herrera-Cruz, V.; Riazanoff, S.; et al. Digital Elevation Models: Terminology and Definitions. Remote Sens. 2021, 13, 3581. [Google Scholar] [CrossRef]
  22. Punys, P.; Dumbrauskas, A.; Kvaraciejus, A.; Vyciene, G. Tools for Small Hydropower Plant Resource Planning and Development: A Review of Technology and Applications. Energies 2011, 4, 1258–1277. [Google Scholar] [CrossRef]
  23. Ramachandra, T.V. Solar Energy Potential Assessment Using GIS. Energy Educ. Sci. Technol. 2007, 18, 101. [Google Scholar]
  24. Wong, M.S.; Zhu, R.; Liu, Z.; Lu, L.; Peng, J.; Tang, Z.; Lo, C.H.; Chan, W.K. Estimation of Hong Kong’s Solar Energy Potential Using GIS and Remote Sensing Technologies. Renew. Energy 2016, 99, 325–335. [Google Scholar] [CrossRef]
  25. Aydin, N.Y.; Kentel, E.; Duzgun, S. GIS-Based Environmental Assessment of Wind Energy Systems for Spatial Planning: A Case Study from Western Turkey. Renew. Sustain. Energy Rev. 2010, 14, 364–373. [Google Scholar] [CrossRef]
  26. Baban, S.M.J.; Parry, T. Developing and Applying a GIS-Assisted Approach to Locating Wind Farms in the UK. Renew. Energy 2001, 24, 59–71. [Google Scholar] [CrossRef]
  27. Dezayes, C.; Famin, V.; Tourlière, B.; Baltassat, J.M.; Bénard, B. Potential Areas of Interest for the Development of Geothermal Energy in La Réunion Island Based on GIS Analysis. J. Volcanol. Geotherm. Res. 2022, 421, 107450. [Google Scholar] [CrossRef]
  28. Elbarbary, S.; Abdel Zaher, M.; Saibi, H.; Fowler, A.R.; Saibi, K. Geothermal Renewable Energy Prospects of the African Continent Using GIS. Geotherm. Energy 2022, 10, 8. [Google Scholar] [CrossRef]
  29. Voivontas, D.; Assimacopoulos, D.; Koukios, E.G. Assessment of Biomass Potential for Power Production: A GIS Based Method. Biomass Bioenergy 2001, 20, 101–112. [Google Scholar] [CrossRef]
  30. Kinoshita, T.; Inoue, K.; Iwao, K.; Kagemoto, H.; Yamagata, Y. A Spatial Evaluation of Forest Biomass Usage Using GIS. Appl. Energy 2009, 86, 1–8. [Google Scholar] [CrossRef]
  31. Larentis, D.G.; Collischonn, W.; Olivera, F.; Tucci, C.E.M. Gis-Based Procedures for Hydropower Potential Spotting. Energy 2010, 35, 4237–4243. [Google Scholar] [CrossRef]
  32. Serpoush, B.; Khanian, M.; Shamsai, A. Hydropower Plant Site Spotting Using Geographic Information System and a MATLAB Based Algorithm. J. Clean. Prod. 2017, 152, 7–16. [Google Scholar] [CrossRef]
  33. Le Guen, M.; Mosca, L.; Perera, A.T.D.; Coccolo, S.; Mohajeri, N.; Scartezzini, J.L. Improving the Energy Sustainability of a Swiss Village through Building Renovation and Renewable Energy Integration. Energy Build. 2018, 158, 906–923. [Google Scholar] [CrossRef]
  34. Ali, U.; Shamsi, M.H.; Bohacek, M.; Purcell, K.; Hoare, C.; Mangina, E.; O’Donnell, J. A Data-Driven Approach for Multi-Scale GIS-Based Building Energy Modeling for Analysis, Planning and Support Decision Making. Appl. Energy 2020, 279, 115834. [Google Scholar] [CrossRef]
  35. Niu, S.; Pan, W.; Zhao, Y. A BIM-GIS Integrated Web-Based Visualization System for Low Energy Building Design. Procedia Eng. 2015, 121, 2184–2192. [Google Scholar] [CrossRef]
  36. Nochta, T.; Wan, L.; Schooling, J.M.; Parlikad, A.K. A Socio-Technical Perspective on Urban Analytics: The Case of City-Scale Digital Twins. J. Urban Technol. 2021, 28, 263–287. [Google Scholar] [CrossRef]
  37. Yigitcanlar, T.; Kamruzzaman, M.; Foth, M.; Sabatini-Marques, J.; da Costa, E.; Ioppolo, G. Can Cities Become Smart without Being Sustainable? A Systematic Review of the Literature. Sustain. Cities Soc. 2019, 45, 348–365. [Google Scholar] [CrossRef]
  38. Eslami, S.; Noorollahi, Y.; Marzband, M.; Anvari-Moghaddam, A. District Heating Planning with Focus on Solar Energy and Heat Pump Using GIS and the Supervised Learning Method: Case Study of Gaziantep, Turkey. Energy Convers. Manag. 2022, 269, 116131. [Google Scholar] [CrossRef]
  39. Government of Canada Homes and Buildings. 2018. Available online: https://www.canada.ca/en/services/environment/weather/climatechange/climate-action/federal-actions-clean-growth-economy/homes-buildings.html (accessed on 30 April 2023).
  40. Saretta, E.; Caputo, P.; Frontini, F. An Integrated 3D GIS-Based Method for Estimating the Urban Potential of BIPV Retrofit of Façades. Sustain. Cities Soc. 2020, 62, 10241. [Google Scholar] [CrossRef]
  41. Deng, Z.; Chen, Y.; Yang, J.; Chen, Z. Archetype Identification and Urban Building Energy Modeling for City-Scale Buildings Based on GIS Datasets. Build. Simul. 2022, 15, 1547–1559. [Google Scholar] [CrossRef]
  42. Schiel, K.; Baume, O.; Caruso, G.; Leopold, U. GIS-Based Modelling of Shallow Geothermal Energy Potential for CO2 Emission Mitigation in Urban Areas. Renew. Energy 2016, 86, 1023–1036. [Google Scholar] [CrossRef]
  43. Mutani, G.; Todeschi, V.; Santantonio, S. Urban-Scale Energy Models: The Relationship between Cooling Energy Demand and Urban Form. J. Phys. Conf. Ser. 2022, 2177, 12016. [Google Scholar] [CrossRef]
  44. Deng, Z.; Chen, Y.; Yang, J.; Causone, F. AutoBPS: A Tool for Urban Building Energy Modeling to Support Energy Efficiency Improvement at City-Scale. Energy Build. 2023, 282, 112794. [Google Scholar] [CrossRef]
  45. Katal, A.; Mortezazadeh, M.; Wang, L.; Yu, H. Urban Building Energy and Microclimate Modeling—From 3D City Generation to Dynamic Simulations. Energy 2022, 251, 123817. [Google Scholar] [CrossRef]
  46. Haneef, F.; Battini, F.; Pernigotto, G.; Gasparella, A. A CitySim urban energy simulation for the development of retrofit scenarios for a neighborhood in Bolzano, Italy. In Proceedings of the 4th IBPSA-Italy Conference Bozen-Bolzano, Bolzano, Italy, 19–21 June 2019. [Google Scholar] [CrossRef]
  47. Borràs, I.M.; Neves, D.; Gomes, R. Using Urban Building Energy Modeling Data to Assess Energy Communities’ Potential. Energy Build. 2023, 282, 112791. [Google Scholar] [CrossRef]
  48. de Santoli, L.; Mancini, F.; Astiaso Garcia, D. A GIS-Based Model to Assess Electric Energy Consumptions and Usable Renewable Energy Potential in Lazio Region at Municipality Scale. Sustain. Cities Soc. 2019, 46, 101413. [Google Scholar] [CrossRef]
  49. Ferla, G.; Caputo, P.; Colaninno, N.; Morello, E. Urban Greenery Management and Energy Planning: A GIS-Based Potential Evaluation of Pruning by-Products for Energy Application for the City of Milan. Renew. Energy 2020, 160, 185–195. [Google Scholar] [CrossRef]
  50. Wang, Q.; M’Ikiugu, M.M.; Kinoshita, I. A GIS-Based Approach in Support of Spatial Planning for Renewable Energy: A Case Study of Fukushima, Japan. Sustainability 2014, 6, 2087–2117. [Google Scholar] [CrossRef]
  51. Government of Canada Clean Energy in Indigenous, Rural and Remote Communities. 2022. Available online: https://www.canada.ca/en/services/environment/weather/climatechange/climate-plan/reduce-emissions/reducing-reliance-diesel.html (accessed on 30 April 2023).
  52. The City of Vancouver Climate Emergency Action Plan. 2020. Available online: https://vancouver.ca/green-vancouver/vancouvers-climate-emergency.aspx (accessed on 30 April 2023).
  53. Wang, C.; Ferrando, M.; Causone, F.; Jin, X.; Zhou, X.; Shi, X. Data Acquisition for Urban Building Energy Modeling: A Review. Build. Environ 2022, 217, 109056. [Google Scholar] [CrossRef]
  54. Li, Y.; Allacker, K.; Feng, H.; Heidari, M.D.; Pelletier, N. Net Zero Energy Barns for Industrial Egg Production: An Effective Sustainable Intensification Strategy? J. Clean. Prod. 2021, 316, 128014. [Google Scholar] [CrossRef]
  55. Domínguez, J.; Amador, J. Geographical Information Systems Applied in the Field of Renewable Energy Sources. Comput. Ind. Eng. 2007, 52, 322–326. [Google Scholar] [CrossRef]
  56. Marzouk, M.; Othman, A. Planning Utility Infrastructure Requirements for Smart Cities Using the Integration between BIM and GIS. Sustain. Cities Soc. 2020, 57, 102120. [Google Scholar] [CrossRef]
  57. Lumbreras, M.; Diarce, G.; Martin-Escudero, K.; Campos-Celador, A.; Larrinaga, P. Design of District Heating Networks in Built Environments Using GIS: A Case Study in Vitoria-Gasteiz, Spain. J. Clean. Prod. 2022, 349, 131491. [Google Scholar] [CrossRef]
  58. Yang, A.; Han, M.; Zeng, Q.; Sun, Y. Adopting Building Information Modeling (BIM) for the Development of Smart Buildings: A Review of Enabling Applications and Challenges. Adv. Civ. Eng. 2021, 2021, 131491. [Google Scholar] [CrossRef]
Buildings 13 01205 g001 550
Figure 1. Framework of exploring net zero energy potentials in buildings by the GIS.
Figure 1. Framework of exploring net zero energy potentials in buildings by the GIS.
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Buildings 13 01205 g002 550
Figure 2. Research goals and attributes analyzed in GIS system.
Figure 2. Research goals and attributes analyzed in GIS system.
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Table
Table 1. Review of the application of renewable energy by using GIS-related methodologies.
Table 1. Review of the application of renewable energy by using GIS-related methodologies.
Energy TypesResearch PurposeResearch MethodologyScaleReference
SolarSolar energy supplyGIS along with Remote Sensing (RS)State[23]
SolarEstimation of city-wide photovoltaic systemsGIS along with RSCity[24]
WindEnvironmental assessment and site selectionFuzzy decision-making approach and GISCountry[25]
WindSite suitability assistanceQuestionnaire and GISCountry[26]
GeothermalLocate the most promising geothermal areasGISIsland[27]
GeothermalEstimate the geothermally promising areasGIS and multi-criteria decision analysis (MCDA); fuzzy logic overlay analyses; analytic hierarchy process (AHP) methodContinent[28]
BiomassEvaluation of forest biomass usageGISTown[29]
BiomassEstimate the availability of biomass resourcesGIS decision support system (DSS)Island[30]
HydropowerHydropower potential sitesRS and regional streamflow data, surveyCountry[31]
HydropowerSpot hydropower plan best locationGIS and MATLAB-based algorithm; economic feasibility assessmentLocal[32]
Table
Table 2. A review of GIS relevant tools and methods used for designing net zero energy buildings.
Table 2. A review of GIS relevant tools and methods used for designing net zero energy buildings.
Net Zero Energy Relevant TargetsResearch Scope (Building Sizes or Types)Research GoalsResearch MethodsLocationReference
Reducing direct energy use68,966 residential and commercial buildingsExplore urban energy saving potentialRandom forest algorithm, GIS data Changsha, China[41]
A city of approximately 87,000 inhabitants including residential, commercial buildingsReduce CO2 by applying shallow geothermal energySmart City Energy Platform, a GIS model and assessmentLudwigsburg, Germany[42]
Improving energy efficiency5 types of residential regions, 30 buildings in eachSimulate the cooling demands of residential buildingsCitySim tool and ISO 52016 assessmentTurin, Italy[43]
3633 residential and commercial buildingsAnalyze energy demand and retrofit and solar PV application for urban buildingsAutomated Building Performance Simulation (AutoBPS), GIS dataChangsha, China[44]
550 m × 600 m with
255 buildings		Develop an archetype library to estimate building envelope3D model, City FFD and City BEMMontreal, Canada[45]
95 residential
dwellings		Identify “net zero energy district” potentials GIS data and CitySim modelBolzano, Italy[46]
Implementing renewable energy15 single residential
buildings, 10 multi residential, and 5 school buildings in Madre de Deus neighborhood		Assess the potential of energy communities’ creation, such as the usage of solar energyGIS data, City Energy Analysis, energy demands calculation, and photovoltaic potential evaluationLisbon, Portugal[47]
5 provinces coving 19,572 km2Assess consumed electricity and explore solar energy potential at the municipality scaleGIS dada. Electricity use analysis and
renewable electricity power evaluation		Lazio, Italy[48]
Covering a 1.3 million population over an area of 181.76 km2Evaluate the potential to convert urban tree pruning biomass to energyGIS tools and a census for georeferencing public treesMilan. Italy[49]
Covering an area of 13,782 km2Regional-level renewable energy spatial designs, including wind energy, photovoltaic solar energy, biomass, geothermal, hydropowerEnergy demand evaluation, renewable energy consumption calculation, GIS mapping, energy self-sufficiency analysisFukushima, Japan[50]
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Li, Y.; Feng, H. GIS for the Potential Application of Renewable Energy in Buildings towards Net Zero: A Perspective. Buildings 2023, 13, 1205. https://doi.org/10.3390/buildings13051205

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Li Y, Feng H. GIS for the Potential Application of Renewable Energy in Buildings towards Net Zero: A Perspective. Buildings. 2023; 13(5):1205. https://doi.org/10.3390/buildings13051205

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Li, Yang, and Haibo Feng. 2023. "GIS for the Potential Application of Renewable Energy in Buildings towards Net Zero: A Perspective" Buildings 13, no. 5: 1205. https://doi.org/10.3390/buildings13051205

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