A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Energy demand models for buildings in a smart cities context
Energy is one of the major drivers in smart cities along with smart environment and smart living. The role that buildings can play in the development and operation of smart energy cities is important, due to the large share of energy use they are responsible for and the smart energy solutions they can potentially integrate. The increasing number of smart cities initiatives and their focus on city level energy policy management has emphasised the need to move from the traditional micro-level building energy modelling towards the development of aggregated energy demand models. To accomplish that, methods that can be scalable to higher levels of aggregation, ranging from clusters of buildings to neighbourhoods and cities are needed. The present thesis aims at providing enhanced modelling methodologies that provide building energy demand related insights in a high spatial and temporal resolution, which can help to evaluate energy policies and demand side management strategies. The main objectives of the thesis are to propose and investigate engineering-based approaches, statistical methods and data mining techniques that can contribute to the accurate building energy demand modelling at urban scale; to determine the potential that buildings can have on the stabilization of the energy grid and the flexible operation of the energy system -considering a smart energy cities context- both at building level and urban level; and finally, to indicate the suitability of each category of proposed modelling methodologies and provide guidelines for future investigations. The most important findings of the research performed in this thesis are summarized in the following: Aggregation of building energy demands is enhanced with archetypal approaches that significantly reduce the modelling and computation time. The minimum information level to model reliably a housing stock contains basic typological information including knowledge about building construction characteristics and floor areas, as well as current refurbishment state ...
Energy demand models for buildings in a smart cities context
Energy is one of the major drivers in smart cities along with smart environment and smart living. The role that buildings can play in the development and operation of smart energy cities is important, due to the large share of energy use they are responsible for and the smart energy solutions they can potentially integrate. The increasing number of smart cities initiatives and their focus on city level energy policy management has emphasised the need to move from the traditional micro-level building energy modelling towards the development of aggregated energy demand models. To accomplish that, methods that can be scalable to higher levels of aggregation, ranging from clusters of buildings to neighbourhoods and cities are needed. The present thesis aims at providing enhanced modelling methodologies that provide building energy demand related insights in a high spatial and temporal resolution, which can help to evaluate energy policies and demand side management strategies. The main objectives of the thesis are to propose and investigate engineering-based approaches, statistical methods and data mining techniques that can contribute to the accurate building energy demand modelling at urban scale; to determine the potential that buildings can have on the stabilization of the energy grid and the flexible operation of the energy system -considering a smart energy cities context- both at building level and urban level; and finally, to indicate the suitability of each category of proposed modelling methodologies and provide guidelines for future investigations. The most important findings of the research performed in this thesis are summarized in the following: Aggregation of building energy demands is enhanced with archetypal approaches that significantly reduce the modelling and computation time. The minimum information level to model reliably a housing stock contains basic typological information including knowledge about building construction characteristics and floor areas, as well as current refurbishment state ...
Energy demand models for buildings in a smart cities context
Gianniou, Panagiota (author)
2018-01-01
Gianniou , P 2018 , Energy demand models for buildings in a smart cities context . B Y G D T U. Rapport , no. 390 , Technical University of Denmark, Department of Civil Engineering .
Book
Electronic Resource
English
/dk/atira/pure/sustainabledevelopmentgoals/affordable_and_clean_energy , SDG 7 - Affordable and Clean Energy , /dk/atira/pure/sustainabledevelopmentgoals/sustainable_cities_and_communities , /dk/atira/pure/sustainabledevelopmentgoals/responsible_consumption_and_production , SDG 12 - Responsible Consumption and Production , SDG 11 - Sustainable Cities and Communities
DDC:
690