A GIS tool for the calculation of solar irradiation on buildings at the urban scale, based on Italian standards

Abstract The development of tools for urban environment analysis and representation, as a support for energy retrofitting and urban renewal designing strategies, is a prominent issue for the transition to more sustainable and resilient settlement patterns. This paper aims to develop and test a GIS-based tool for the calculation of solar irradiation, as a factor of the building energy performance, by applying, at the urban scale, the simplified method of the international standard (ISO 13790:2008) proposed by Italian specifications (UNI TS 11300: 2008–2014). This standard is a common cultural reference for technicians, practitioners, decision makers and ordinary citizens because it is the basis for the calculation of Energy Performance Certificates (EPC) for buildings. The standard physical model is simplified, but requires some base data that practitioners have to define from their expertise by an in-situ survey. So the adaptation of the calculation of solar irradiation according to the standard at the urban scale needs some simplifications and typological approaches related to the availability of base data. A simple and repeatable standard consistent methodology for the calculation of the solar irradiation on building surfaces has been developed, adopting commonly available base data on urban morphology such as topographic maps or Digital Terrain Models. A summary on the state of the art of GIS tools for urban solar analysis and the standard algorithms for the calculation of the solar irradiation is also provided. For the study of the practitioner's approach in the use of the standard calculation a questionnaire is set up. The work then illustrates the methodology approach adopted for the tool developing and testing. The discussion focuses on the compliance of the results with standard calculation and on solar irradiation values reliability. Such consistency has been assessed via a sensitivity analysis based on different ideal cases. In order to assess the reliability of the results of the GIS tool, a comparison with the values obtained from a dynamic solar simulation environment (Daysim) is performed.

[1]  F. Golay,et al.  Urban environment quality indicators: application to solar radiation and morphological analysis on built area , 2010 .

[2]  Maria Wall,et al.  The Solar Map as a Knowledge Base for Solar Energy Use , 2014 .

[3]  Giorgio Guariso,et al.  Methods and tools to evaluate the availability of renewable energy sources , 2011 .

[4]  M. Brito,et al.  Solar energy potential on roofs and facades in an urban landscape , 2013 .

[5]  Volker Coors,et al.  The influence of data quality on urban heating demand modeling using 3D city models , 2017, Comput. Environ. Urban Syst..

[6]  Martin Rutzinger,et al.  A new multi-scale 3D-GIS-approach for the assessment and dissemination of solar income of digital city models , 2016, Comput. Environ. Urban Syst..

[7]  Andrea Acquaviva,et al.  A Novel Integrated Real-time Simulation Platform for Assessing Photovoltaic Penetration Impacts in Smart Grids , 2017 .

[8]  Sérgio Freire,et al.  Applications of solar mapping in the urban environment , 2014 .

[9]  Miguel P. Amado,et al.  Solar Energy Integration in Urban Planning: GUUD Model☆ , 2014 .

[10]  K. Steemers,et al.  Solar energy and urban morphology: Scenarios for increasing the renewable energy potential of neighbourhoods in London , 2015 .

[11]  T. Williamson,et al.  Urban Microclimate: Designing the Spaces Between Buildings , 2010 .

[12]  D. Robinson.  Urban morphology and indicators of radiation availability , 2006 .

[13]  Giuliano Dall'O',et al.  A methodology for the energy performance classification of residential building stock on an urban scale , 2012 .

[14]  Niko Heeren,et al.  A component based bottom-up building stock model for comprehensive environmental impact assessment and target control , 2013 .

[15]  Rahul B. Hiremath,et al.  Indicator-based urban sustainability—A review , 2013 .

[16]  Andrew Stone,et al.  SUNtool - A new modelling paradigm for simulating and optimising urban sustainability , 2007 .

[17]  D. Robinson,et al.  Internal illumination prediction based on a simplified radiosity algorithm , 2006 .

[18]  Christoph F. Reinhart,et al.  The simulation of annual daylight illuminance distributions — a state-of-the-art comparison of six RADIANCE-based methods , 2000 .

[19]  T. Santos,et al.  Photovoltaic potential in a Lisbon suburb using LiDAR data , 2012 .

[20]  D. Robinson,et al.  Solar radiation modelling in the urban context , 2004 .

[21]  Cristina Catita,et al.  Extending solar potential analysis in buildings to vertical facades , 2014, Comput. Geosci..

[22]  Miguel Brito,et al.  Modelling solar potential in the urban environment: State-of-the-art review , 2015 .

[23]  M. Biberacher.  GIS based model to optimize the utilization of renewable energy carriers and related energy flows , 2009 .

[24]  Ming Li,et al.  An open-source 3D solar radiation model integrated with a 3D Geographic Information System , 2015, Environ. Model. Softw..

[25]  G. Kamali,et al.  Evaluation of 12 models to estimate hourly diffuse irradiation on inclined surfaces , 2008 .

[26]  Christoph F. Reinhart,et al.  Validation of dynamic RADIANCE-based daylight simulations for a test office with external blinds , 2001 .

[27]  J. Hofierka,et al.  The solar radiation model for Open source GIS: implementation and applications , 2002 .

[28]  J. Kaňuk,et al.  Assessment of photovoltaic potential in urban areas using open-source solar radiation tools , 2009 .

[29]  M. Santamouris,et al.  Heat Island Research in Europe: The State of the Art , 2007 .

[30]  Qing Zhu,et al.  Design and implementation of 3D model database for general-purpose 3D GIS , 2010, Geo spatial Inf. Sci..

[31]  Enrico Macii,et al.  The Energy Efficiency Management at Urban Scale by Means of Integrated Modelling , 2015 .

[32]  C. Reinhart,et al.  A method for predicting city-wide electricity gains from photovoltaic panels based on LiDAR and GIS data combined with hourly Daysim simulations , 2013 .

[33]  Miguel P. Amado,et al.  Energy efficient city: A model for urban planning , 2016 .

[34]  C. Ratti,et al.  Energy consumption and urban texture , 2005 .

[35]  Joshua M. Pearce,et al.  Estimating Potential Photovoltaic Yield with r.sun and the Open Source Geographical Resources Analysis Support System , 2010 .

[36]  Anne Power,et al.  Does demolition or refurbishment of old and inefficient homes help to increase our environmental, social and economic viability? , 2008 .

[37]  Theodoros Theodosiou,et al.  Feasibility of energy saving renovation measures in urban buildings. The impact of energy prices and the acceptable pay back time criterion , 2002 .

[38]  Jaroslav Hofierka,et al.  A New 3‐D Solar Radiation Model for 3‐D City Models , 2012, Trans. GIS.

[39]  Edmundas Kazimieras Zavadskas,et al.  The concept model of sustainable buildings refurbishment , 2008 .

[40]  Baizhan Li,et al.  A simplified mathematical model for urban microclimate simulation , 2011 .

[41]  V. Ismet Ugursal,et al.  Modeling of end-use energy consumption in the residential sector: A review of modeling techniques , 2009 .

[42]  Songnian Li,et al.  GIS Modeling of Solar Neighborhood Potential at a Fine Spatiotemporal Resolution , 2014 .

[43]  G. Štumberger,et al.  Rating of roofs’ surfaces regarding their solar potential and suitability for PV systems, based on LiDAR data , 2013 .

[44]  Carlo Ratti,et al.  Sunscapes: 'Solar envelopes' and the analysis of urban DEMs , 2009, Comput. Environ. Urban Syst..

[45]  Massimiliano Manfren,et al.  Paradigm shift in urban energy systems through distributed generation: Methods and models , 2011 .

[46]  H. Takebayashi,et al.  Study to examine the potential for solar energy utilization based on the relationship between urban morphology and solar radiation gain on building rooftops and wall surfaces , 2015 .

[47]  N. Fueyo,et al.  A method for estimating the geographical distribution of the available roof surface area for large-scale photovoltaic energy-potential evaluations , 2008 .

[48]  C. Ratti,et al.  Solar Radiation over the Urban Texture: LIDAR Data and Image Processing Techniques for Environmental Analysis at City Scale , 2009 .

[49]  Wided Medjroubi,et al.  GIS-based urban energy systems models and tools: Introducing a model for the optimisation of flexibilisation technologies in urban areas , 2017 .