Building Stock Energy Models and ICT Solutions for Urban Energy Systems

Building Stock Energy Models and ICT Solutions for Urban Energy Systems

Ilaria Ballarini (Politecnico di Torino, Italy), Vincenzo Corrado (Politecnico di Torino, Italy) and Matteo Piro (Politecnico di Torino, Italy)
DOI: 10.4018/978-1-7998-7091-3.ch022
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Abstract

The existing building stock presents a high potential of energy savings and CO2 emissions reductions. To this purpose, literature provides novel city-scale building-oriented studies, aimed at developing suitable tools for stakeholders, city planners, and decision-makers. To achieve an effective urban energy planning, urban energy systems (UES) models are developed; they employ a multi-domain approach, embracing the complex interactions in urban areas, such as energy flows, environmental indicators, social and economic factors. To perform an advanced modelling and to simulate the complexity of the UES, ICT (information and communications technology) represents nowadays the right answer to the needs of integration of data, tools, and actors in different domains. The chapter investigates the current studies in the field of building stock energy modeling and the application of advanced technologies to develop UES models. As an exemplification, the technological approach followed in the SEMANCO project to support urban scale energy modelling is presented.
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Introduction

The world population is currently about 7.8 billion and it is expected to increase to 10 billion in 2050. The current share of urban and rural population at world level is 56% and 44%, respectively, but it is foreseen a growing of the population share living in urban areas to 68% in 30 years. In Europe, despite 3.7% decrease of total population is expected in 2050 compared to 2020, the urban population share will move from 75% in 2020 to 84% in 2050 (Ritchie & Roser, 2018). The increase of urbanization determines a corresponding intensification of human activity and the related energy consumption; thus, it is necessary to carry out effective urban energy planning that takes into account sustainable development plans and quantifies the effect of related policies.

The building stock represents a significant share in energy use and greenhouse gas emissions; in 2017, it accounted for 42% of final energy consumption and 17% of CO2 emissions from fuel combustion in Europe (European Commission [EC], 2019). Therefore, it offers a great potential for energy efficiency and integrated sustainable energy solutions. The key-role of buildings in the energy balance of cities has boosted novel city-scale building-oriented studies, aimed at developing suitable tools for stakeholders, city planners and decision-makers. These tools will enable to understand urban energy systems and to formulate energy plans, suggest sustainable initiatives and decide on constructive policies (Torabi Moghadam et al., 2017).

To develop urban energy planning tools, models of Urban Energy Systems (UES) should be conceived through an integrated multi-domain approach, which embraces complex interactions in urban areas, such as energy flows, environmental indicators, social and economic factors. The increase of model data and complexity requires more advanced technologies. In this field, Information and Communications Technology (ICT) represents the right answer to the need of integration of data, tools and actors, spanning across different domains and scales of analysis (i.e. building, neighborhood, city, and region). ICT is a very wide term; sometimes “digitization” and “datafication” are used to refer to the issues derived from data retrieval, modelling and analysis in urban contexts. This technology grants automatic access to data dispersed in different sources, interoperability of data and tools for energy simulation, optimization and decision support through multi-criteria analysis, and visualization in a 3D environment. To these objectives, ontology and semantics form the basis to develop UES models using ICT (Pauwels et al., 2017). Future UES models will take advantage from the nascent era of digitalization, through the inclusion of the Internet of Things (IoT) and Artificial Intelligence (AI) agents (e.g. data analytics, machine learning) (Boje et al., 2020), as to boost the development of Smart Cities.

In this context, the present chapter aims to investigate recent studies on integrated urban energy models, starting from building stock models and related tools, highlighting their strengths and limits, up to multi-domain energy models. In the field of UES, the chapter specifically focuses on projects and initiatives addressed to the creation of effective urban energy planning tools. As an exemplary case, the experience of SEMANCO (Semantic Tools for Carbon Reduction in Urban Planning), a European project part of the EC 7th Framework Programme, as well as the peculiarities of the UES tool developed in the project (Corrado et al., 2015) are presented. SEMANCO aimed to create an effective decision support system to reduce carbon emissions. Its approach was based on the interrelation of different actors – policy makers, planners, engineers, consultants, and inhabitants – to correlate a diversity of problems, spanning across distinct domains and geographic scales.

The chapter has been structured as follows:

Key Terms in this Chapter

Ontology: An explicit specification of a conceptualization, in which a set of objects is described through relationships among them to form the universe of discourse.

Urban Metabolism: Process that involves the exchange of energy flows and materials (e.g. water, food, waste) within an environment.

Urban Energy System: The combined process of acquiring and using energy to satisfy the demands of a given urban area that is modelled using integrated tools, as to mirror the dynamism and the complexity of cities with a hierarchical approach.

Bottom-Up Building Stock Energy Model: Energy model of the building stock, in which disaggregated data are used to estimate the end-use energy of individual buildings or group of buildings, and then the aggregated energy demand is extrapolated at a wider territorial scale.

Use-Case: The pre-conceptualization of a model that represents an urban energy system, as thought by experts within a particular context.

Top-Down Building Stock Energy Model: Energy model of the building stock, in which aggregated data of energy consumption are correlated to economic or other parameters to derive relationships for the prediction of the energy demand at a smaller territorial scale.

Urban Scale Energy Model: Energy model that involves a spatio-temporal resolution of energy flows in cities, allowing the evaluation of system losses, load peaks, unconsumed energy shares, and the relationship between consumer and supplier.

Urban Building Energy Model: Type of bottom-up building stock energy model that derives the energy demand through an engineering or physical-based approach, i.e. applying building heat balance equations.

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