Development of Aggregation Models for Building Energy Demands applied to Smart Cities

Master thesis by Panagiota Gianniou, August 2014

Abstract – The mitigation of climate change has been a priority to most countries’ agendas
nowadays. Energy and environmental policies have been introduced to facilitate the
achievement of national targets. At the same time, rapid urbanization has resulted in
converting cities into the main energy consumers and generators of GHG emissions.
Thus, they are an ideal platform where sustainable solutions can be applied which will
improve their durability and functionality. The concept of Smart Cities has the
potential to integrate sustainable technologies and innovative systems into urban
areas. At the same time, the building sector occupies a key place in the development
of Smart Cities. Energy demand of the building sector affects significantly national
energy balances. Furthermore, estimating energy demand of a cluster of buildings, a
district or city requires the aggregation of them. When handling aggregated energy
demand data, future energy predictions and the creation of what-if scenarios for
demand-side energy management are enabled. It also facilitates urban planning, as
well as the development of energy hubs into urban areas.
In the current Thesis, the theoretical background needed to study aggregation of
building energy demands is presented and analyzed. Two methods of aggregating
energy demands of buildings are identified and implemented on a real case-study,
being located in Sønderborg, Denmark. This consists of 16 single-family houses all
connected to the regional district heating system. These were modelled by Termite, a
newly-developed parametric tool, which uses Danish Be10 for energy simulating.
According to the first aggregation way, individual buildings’ energy simulations are
carried out. This method necessitates extensive data availability. Six different
information levels are investigated, concluding that apart from general data about
building’s functionality, floor area and age of construction, also information about the
most recent energy refurbishment state of the building is crucial for achieving high
accuracy in energy demand estimations. According to the second aggregation way,
building typologies are used, where five example buildings representing each type are
simulated. The results highlight that the specific example buildings represent quite
well the respective buildings, but present a deviation from the measured energy
demands. However, the annual aggregate heat demand of this method is found to be
very close to the measured one. Extensive discussion on the challenges and
uncertainties of the present city energy model is also presented.

MSc Thesis_Panagiota Gianniou_s121414.

Panagiote Gianniou is today a PhD at DTU Byg. You find her work at


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