Metadata for the energy performance certificates of buildings in smart cities

SusCity is a MIT Portugal project that falls within the scope of smart cities. One of its tasks aims to research and develop metadata artefacts to be used in the scope of a Linked Open Data platform. In this article, we report the process and results associated with the development of the following metadata artefacts: an application profile, a metadata schema and four controlled vocabularies. The application field is the energy certification of buildings. For the development of the application profile, we inspired ourselves in the Me4MAP method although we did not use it thoroughly. The creation of the metadata schema and controlled vocabularies involved the use of Wikidata, so all new terms (RDFS classes and properties and SKOS concepts) are related to Wikidata terms. The results include the application profile, the metadata schema and the controlled vocabularies. The application profile has 13 properties, four of which are new. The controlled vocabulary on measures for energy performance has 22 new terms spread over four levels. The remaining controlled vocabularies just hold a few terms each. All the artefacts are open to the community for use and reuse.(FCT/MITP-TB/CS/0026/2013)info:eu-repo/semantics/publishedVersio

Machine Learning Techniques Focusing on the Energy Performance of Buildings: A Dimensions and Methods Analysis

109 “Consumo SMART”. This work is partially funded by national funds through FCT—Foundation for Science and Technology, I.P., under the project FCT UIDB/04466/2020.The problem of energy consumption and the importance of improving existing buildings’ energy performance are notorious. This work aims to contribute to this improvement by identifying the latest and most appropriate machine learning or statistical techniques, which analyze this problem by looking at large quantities of building energy performance certification data and other data sources. PRISMA, a well-established systematic literature review and meta-analysis method, was used to detect specific factors that influence the energy performance of buildings, resulting in an analysis of 35 papers published between 2016 and April 2021, creating a baseline for further inquiry. Through this systematic literature review and bibliometric analysis, machine learning and statistical approaches primarily based on building energy certification data were identified and analyzed in two groups: (1) automatic evaluation of buildings’ energy performance and, (2) prediction of energy-efficient retrofit measures. The main contribution of our study is a conceptual and theoretical framework applicable in the analysis of the energy performance of buildings with intelligent computational methods. With our framework, the reader can understand which approaches are most used and more appropriate for analyzing the energy performance of different types of buildings, discussing the dimensions that are better used in such approaches.publishersversionpublishe

Machine learning techniques focusing on the energy performance of buildings: A dimensions and methods analysis

The problem of energy consumption and the importance of improving existing buildings’ energy performance are notorious. This work aims to contribute to this improvement by identifying the latest and most appropriate machine learning or statistical techniques, which analyze this problem by looking at large quantities of building energy performance certification data and other data sources. PRISMA, a well-established systematic literature review and meta-analysis method, was used to detect specific factors that influence the energy performance of buildings, resulting in an analysis of 35 papers published between 2016 and April 2021, creating a baseline for further inquiry. Through this systematic literature review and bibliometric analysis, machine learning and statistical approaches primarily based on building energy certification data were identified and analyzed in two groups: (1) automatic evaluation of buildings’ energy performance and, (2) prediction of energy-efficient retrofit measures. The main contribution of our study is a conceptual and theoretical framework applicable in the analysis of the energy performance of buildings with intelligent computational methods. With our framework, the reader can understand which approaches are most used and more appropriate for analyzing the energy performance of different types of buildings, discussing the dimensions that are better used in such approaches.info:eu-repo/semantics/publishedVersio

Statistical Building Energy Model from Data Collection, Place-Based Assessment to Sustainable Scenarios for the City of Milan

Building energy modeling plays an important role in analyzing the energy efficiency of the existing building stock, helping in enhancing it by testing possible retrofit scenarios. This work presents an urban scale and place-based approach that utilizes energy performance certificates to develop a statistical energy model. The objective is to describe the energy modeling methodology for evaluating the energy performance of residential buildings in Milan; in addition, a comprehensive reference dataset for input data from available open databases in Italy is provided a critical step in assessing energy consumption and production at territorial scale. The study employs open-source software QGIS 3.28.8 to model and calculate various energy-related variables for the prediction of space heating, domestic hot water consumptions, and potential solar production. By analyzing demand/supply profiles, the research aims to increase energy self-consumption and self-sufficiency in the urban context using solar technologies. The presented methodology is validated by comparing simulation results with measured data, achieving a Mean Absolute Percentage Error (MAPE) of 5.2%, which is acceptable, especially considering city-scale modeling. The analysis sheds light on key parameters affecting building energy consumption/production, such as type of user, volume, surface-to-volume ratio, construction period, systems’ efficiency, solar exposition and roof area. Additionally, this assessment attempts to evaluate the spatial distribution of energy-use and production within urban environments, contributing to the planning and realization of smart cities

Energy efficiency and GHG emissions mapping of buildings for decision-making processes against climate change at local level

[EN] Buildings have become a key source of greenhouse gas (GHG) emissions due to the consumption of primary energy, especially when used to achieve thermal comfort conditions. In addition, buildings play a key role for adapting societies to climate change by achieving more energy efficiency. Therefore, buildings have become a key sector to tackle climate change at the local level. However, public decision-makers do not have tools with enough spatial resolution to prioritise and focus the available resources and efforts in an efficient manner. The objective of the research is to develop an innovative methodology based on a geographic information system (GIS) for mapping primary energy consumption and GHG emissions in buildings in cities according to energy efficiency certificates. The developed methodology has been tested in a representative medium-sized city in Spain, obtaining an accurate analysis that shows 32,000 t of CO2 emissions due to primary energy consumption of 140 GWh in residential buildings with high spatial resolution at single building level. The obtained results demonstrate that the majority of residential buildings have low levels of energy efficiency and emit an average of 45 kg CO2/m(2). Compared to the national average in Spain, this obtained value is on the average, while it is slightly better at the regional level. Furthermore, the results obtained demonstrate that the developed methodology is able to directly identify city districts with highest potential for improving energy efficiency and reducing GHG emissions. Additionally, a data model adapted to the INSPIRE regulation has been developed in order to ensure interoperability and European-wide application. All these results have allowed the local authorities to better define local strategies towards a low-carbon economy and energy transition. In conclusion, public decision-makers will be supported with an innovative and user-friendly GIS-based methodology to better define local strategies towards a low-carbon economy and energy transition in a more efficient and transparent way based on metrics of high spatial resolution and accuracy.This work was supported by the City Council of Quart de Poblet (Valencia, Spain).Lorenzo-Sáez, E.; Oliver Villanueva, JV.; Coll-Aliaga, E.; Lemus Zúñiga, LG.; Lerma Arce, V.; Reig Fabado, A. (2020). Energy efficiency and GHG emissions mapping of buildings for decision-making processes against climate change at local level. Sustainability. 12(7):1-17. https://doi.org/10.3390/su12072982S1171272050 Long-Term Strategy https://ec.europa.eu/clima/policies/strategies/2050_enYang, J., McBride, J., Zhou, J., & Sun, Z. (2005). The urban forest in Beijing and its role in air pollution reduction. Urban Forestry & Urban Greening, 3(2), 65-78. doi:10.1016/j.ufug.2004.09.001Escobedo, F. J., Kroeger, T., & Wagner, J. E. (2011). Urban forests and pollution mitigation: Analyzing ecosystem services and disservices. Environmental Pollution, 159(8-9), 2078-2087. doi:10.1016/j.envpol.2011.01.010Zhao, M., Kong, Z., Escobedo, F. J., & Gao, J. (2010). Impacts of urban forests on offsetting carbon emissions from industrial energy use in Hangzhou, China. Journal of Environmental Management, 91(4), 807-813. doi:10.1016/j.jenvman.2009.10.010McPherson, E. G., Scott, K. I., & Simpson, J. R. (1998). Estimating cost effectiveness of residential yard trees for improving air quality in Sacramento, California, using existing models. Atmospheric Environment, 32(1), 75-84. doi:10.1016/s1352-2310(97)00180-5Nowak, D. J., & Crane, D. E. (2002). Carbon storage and sequestration by urban trees in the USA. Environmental Pollution, 116(3), 381-389. doi:10.1016/s0269-7491(01)00214-7Nowak, D. J., Crane, D. E., & Stevens, J. C. (2006). Air pollution removal by urban trees and shrubs in the United States. Urban Forestry & Urban Greening, 4(3-4), 115-123. doi:10.1016/j.ufug.2006.01.007Energy ec.europa.eu/energy/efficiency/buildings/buildings_en.htmGouldson, A., Colenbrander, S., Sudmant, A., Papargyropoulou, E., Kerr, N., McAnulla, F., & Hall, S. (2016). Cities and climate change mitigation: Economic opportunities and governance challenges in Asia. Cities, 54, 11-19. doi:10.1016/j.cities.2015.10.010Gouldson, A., Colenbrander, S., Sudmant, A., McAnulla, F., Kerr, N., Sakai, P., … Kuylenstierna, J. (2015). Exploring the economic case for climate action in cities. Global Environmental Change, 35, 93-105. doi:10.1016/j.gloenvcha.2015.07.009Wilson, E. (2006). Adapting to Climate Change at the Local Level: The Spatial Planning Response. Local Environment, 11(6), 609-625. doi:10.1080/13549830600853635Kavgic, M., Mavrogianni, A., Mumovic, D., Summerfield, A., Stevanovic, Z., & Djurovic-Petrovic, M. (2010). A review of bottom-up building stock models for energy consumption in the residential sector. Building and Environment, 45(7), 1683-1697. doi:10.1016/j.buildenv.2010.01.021Mastrucci, A., Baume, O., Stazi, F., & Leopold, U. (2014). Estimating energy savings for the residential building stock of an entire city: A GIS-based statistical downscaling approach applied to Rotterdam. Energy and Buildings, 75, 358-367. doi:10.1016/j.enbuild.2014.02.032Evola, G., Fichera, A., Gagliano, A., Marletta, L., Nocera, F., Pagano, A., & Palermo, V. (2016). Application of a Mapping tool to Plan Energy Saving at a Neighborhood Scale. Energy Procedia, 101, 137-144. doi:10.1016/j.egypro.2016.11.018Nouvel, R., Mastrucci, A., Leopold, U., Baume, O., Coors, V., & Eicker, U. (2015). Combining GIS-based statistical and engineering urban heat consumption models: Towards a new framework for multi-scale policy support. Energy and Buildings, 107, 204-212. doi:10.1016/j.enbuild.2015.08.021Bentzen, J., & Engsted, T. (2001). A revival of the autoregressive distributed lag model in estimating energy demand relationships. Energy, 26(1), 45-55. doi:10.1016/s0360-5442(00)00052-9Fonseca, J. A., & Schlueter, A. (2015). Integrated model for characterization of spatiotemporal building energy consumption patterns in neighborhoods and city districts. Applied Energy, 142, 247-265. doi:10.1016/j.apenergy.2014.12.068Caputo, P., Costa, G., & Ferrari, S. (2013). A supporting method for defining energy strategies in the building sector at urban scale. Energy Policy, 55, 261-270. doi:10.1016/j.enpol.2012.12.006Theodoridou, I., Karteris, M., Mallinis, G., Papadopoulos, A. M., & Hegger, M. (2012). Assessment of retrofitting measures and solar systems’ potential in urban areas using Geographical Information Systems: Application to a Mediterranean city. Renewable and Sustainable Energy Reviews, 16(8), 6239-6261. doi:10.1016/j.rser.2012.03.075Howard, B., Parshall, L., Thompson, J., Hammer, S., Dickinson, J., & Modi, V. (2012). Spatial distribution of urban building energy consumption by end use. Energy and Buildings, 45, 141-151. doi:10.1016/j.enbuild.2011.10.061Heiple, S., & Sailor, D. J. (2008). Using building energy simulation and geospatial modeling techniques to determine high resolution building sector energy consumption profiles. Energy and Buildings, 40(8), 1426-1436. doi:10.1016/j.enbuild.2008.01.005CALENER-VYP: Viviendas y Edificios Terciarios Pequeños y Medianos. Manual de Usuario https://www.idae.es/uploads/documentos/documentos_CALENER_05_VYP_Manual_Usuario_A2009_A_4c6978f8.pdfKampelis, N., Ferrante, A., Kolokotsa, D., Gobakis, K., Standardi, L., & Cristalli, C. 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Taxonomy, Semantic Data Schema, and Schema Alignment for Open Data in Urban Building Energy Modeling

Urban Building Energy Modeling (UBEM) is a critical tool to provide quantitative analysis on building decarbonization, sustainability, building-to-grid integration, and renewable energy applications on city, regional, and national scales. Researchers usually use open data as inputs to build and calibrate UBEM. However, open data are from thousands of sources covering various perspectives of weather, building characteristics, etc. Besides, a lack of semantic features of open data further increases the engineering effort to process information to be directly used for UBEM as inputs. In this paper, we first reviewed open data types used for UBEM and developed a taxonomy to categorize open data. Based on that, we further developed a semantic data schema for each open data category to maintain data consistency and improve model automation for UBEM. In a case study, we use three popular open data to show how they can be automatically processed based on the proposed schematic data structure using large language models. The accurate results generated by large language models indicate the machine-readability and human-interpretability of the developed semantic data schema

Mining Heterogeneous Urban Data at Multiple Granularity Layers

The recent development of urban areas and of the new advanced services supported by digital technologies has generated big challenges for people and city administrators, like air pollution, high energy consumption, traffic congestion, management of public events. Moreover, understanding the perception of citizens about the provided services and other relevant topics can help devising targeted actions in the management. With the large diffusion of sensing technologies and user devices, the capability to generate data of public interest within the urban area has rapidly grown. For instance, different sensors networks deployed in the urban area allow collecting a variety of data useful to characterize several aspects of the urban environment. The huge amount of data produced by different types of devices and applications brings a rich knowledge about the urban context. Mining big urban data can provide decision makers with knowledge useful to tackle the aforementioned challenges for a smart and sustainable administration of urban spaces. However, the high volume and heterogeneity of data increase the complexity of the analysis. Moreover, different sources provide data with different spatial and temporal references. The extraction of significant information from such diverse kinds of data depends also on how they are integrated, hence alternative data representations and efficient processing technologies are required. The PhD research activity presented in this thesis was aimed at tackling these issues. Indeed, the thesis deals with the analysis of big heterogeneous data in smart city scenarios, by means of new data mining techniques and algorithms, to study the nature of urban related processes. The problem is addressed focusing on both infrastructural and algorithmic layers. In the first layer, the thesis proposes the enhancement of the current leading techniques for the storage and elaboration of Big Data. The integration with novel computing platforms is also considered to support parallelization of tasks, tackling the issue of automatic scaling of resources. At algorithmic layer, the research activity aimed at innovating current data mining algorithms, by adapting them to novel Big Data architectures and to Cloud computing environments. Such algorithms have been applied to various classes of urban data, in order to discover hidden but important information to support the optimization of the related processes. This research activity focused on the development of a distributed framework to automatically aggregate heterogeneous data at multiple temporal and spatial granularities and to apply different data mining techniques. Parallel computations are performed according to the MapReduce paradigm and exploiting in-memory computing to reach near-linear computational scalability. By exploring manifold data resolutions in a relatively short time, several additional patterns of data can be discovered, allowing to further enrich the description of urban processes. Such framework is suitably applied to different use cases, where many types of data are used to provide insightful descriptive and predictive analyses. In particular, the PhD activity addressed two main issues in the context of urban data mining: the evaluation of buildings energy efficiency from different energy-related data and the characterization of people's perception and interest about different topics from user-generated content on social networks. For each use case within the considered applications, a specific architectural solution was designed to obtain meaningful and actionable results and to optimize the computational performance and scalability of algorithms, which were extensively validated through experimental tests

Methodologies for energy performance assessment based on location data: Proceedings of the workshop, Ispra, 12-14 September 2016

This expert workshop is one of a series covering the following topics: energy, buildings, location, assessment methods and data in relation to European Directives on Energy Efficiency (EED), Energy Performance of Buildings (EPBD), INSPIRE, establishing an Infrastructure for Spatial Information in Europe and the Covenant of Mayors (CoM) initiative. These workshops are jointly organised by the EC DG JRC project on Energy and Location and the European Union Location Framework (EULF) action of the EC ISA Programme (Interoperability Solutions for Public Administration) . So far the JRC team has produced a feasibility study and started a dedicated pilot project on location data for buildings related energy efficiency policies . The first event organised was the workshop on “Spatial data for modelling building stock energy needs” held at JRC in Ispra 23-25 November 2015 . The aim of this second workshop was to discuss different approaches and methodologies to assess energy efficiency measures as well as energy usage and monitoring of energy flows at building, urban and regional level, representing an opportunity to share information, integrate stakeholders’ views and set the ground for mutual collaboration. Eleven invited leading organisations and EU projects were invited to take part in this workshop sending experts to present their projects and discuss how to assess synergies and how to arrive to a coherent approach for assessment of energy use in the built environment. Another twelve people from JRC, experts on energy efficiency, energy performance, geospatial data modelling and processing participated to the workshop. From the discussions, it has emerged that a holistic approach would give more evidence of the needs for measures to reduce energy consumption. This is a bit in contrast to what the EU policy requests by the present energy related Directives. More and more it becomes evident that the target should be reducing emissions and not necessarily reducing energy consumption. Integration of energy technologies are playing an important role at a higher level than the building only (i.e. at the EPBD–level). The energy market (gas and electricity) is able to provide an enormous buffer in storing energy virtually and the buildings itself should be much better balanced in energy terms to the thermal needs, e.g. heating and cooling. At the same time the energy network requires buildings for balancing. INSPIRE could be very relevant for energy assessment in the built environment and for this reason the Energy Pilot initiated under the “Energy and Location” and “European Union Location Framework” projects will be continued over the next years. Main objectives of the pilot project will be to continue to work on Use Cases already outlined, to be further elaborated based on the information gathered at the workshop. The JRC will seek to develop partnerships to implement the defined use cases with the selected partners.JRC.B.6-Digital Econom

Combining Green Metrics and Digital Twins for Sustainability Planning and Governance of Smart Buildings and Cities

Creating a more sustainable world will require a coordinated effort to address the rise of social, economic, and environmental concerns resulting from the continuous growth of cities. Supporting planners with tools to address them is pivotal, and sustainability is one of the main objectives. Modeling and simulation augmenting digital twins can play an important role to implement these tools. Although various green best practices have been utilized over time and there are related attempts at measuring green success, works in the published literature tend to focus on addressing a single problem (e.g., energy efficiency), and a comprehensive approach that takes the multiple facets of sustainable urban planning into consideration has not yet been identified. This paper begins with a review of recent research efforts in green metrics and digital twins. This leads to developing an approach that evaluates organizational green best practices to derive metrics, which are used for computational decision support by digital twins. Furthermore, it leverages these research results and proposes a metric-driven framework for sustainability planning that understands a city as a sociotechnical complex system. Such a framework allows the practitioner to take advantage of recent developments and provides computational decision support for the complex challenge of sustainability planning at the various levels of urban planning and governance

The Energy Application Domain Extension for CityGML: enhancing interoperability for urban energy simulations

The road towards achievement of the climate protection goals requires, among the rest, a thorough rethinking of the energy planning tools (and policies) at all levels, from local to global. Nevertheless, it is in the cities where the largest part of energy is produced and consumed, and therefore it makes sense to focus the attention particularly on the cities as they yield great potentials in terms of energy consumption reduction and efficiency increase. As a direct consequence, a comprehensive knowledge of the demand and supply of energy resources, including their spatial distribution within urban areas, is therefore of utmost importance. Precise, integrated knowledge about 3D urban space, i.e. all urban (above and underground) features, infrastructures, their functional and semantic characteristics, and their mutual dependencies and interrelations play a relevant role for advanced simulation and analyses. As a matter of fact, what in the last years has proven to be an emerging and effective approach is the adoption of standard-based, integrated semantic 3D virtual city models, which represent an information hub for most of the abovementioned needs. In particular, being based on open standards (e.g. on the CityGML standard by the Open Geospatial Consortium), virtual city models firstly reduce the effort in terms of data preparation and provision. Secondly, they offer clear data structures, ontologies and semantics to facilitate data exchange between different domains and applications. However, a standardised and omni-comprehensive urban data model covering also the energy domain is still missing at the time of writing (January 2018). Even CityGML falls partially short when it comes to the definition of specific entities and attributes for energy-related applications. Nevertheless, and starting from the current version of CityGML (i.e. 2.0), this article describes the conception and the definition of an Energy Application Domain Extension (ADE) for CityGML. The Energy ADE is meant to offer a unique and standard-based data model to fill, on one hand, the above-mentioned gap, and, on the other hand, to allow for both detailed single-building energy simulation (based on sophisticated models for building physics and occupant behaviour) and city-wide, bottom-up energy assessments, with particular focus on the buildings sector. The overall goal is to tackle the existing data interoperability issues when dealing with energy-related applications at urban scale. The article presents the rationale behind the Energy ADE, it describes its main characteristics, the relation to other standards, and provides some examples of current applications and case studies already adopting it