Prediktivni termički modeli potrošača u sistemima daljinskog grejanja
Докторанд
Protić, Milan Z.Ментор
Mitić, DraganЧланови комисије
Živković, LjiljanaNikolić, Vlastimir
Todorović, Branimir
Raos, Miomir
Метаподаци
Приказ свих података о дисертацијиСажетак
Today, district heating systems (DHS) in developed EU countries, especially in Scandinavia,
are among the most efficient ways of providing citizens with heat in urban areas. Currently
there are more than 6,000 DHS in Europe. In Serbia, there are DHS in 53 cities and towns,
with the total installed capacity of 6,180 MW. Significant amounts of energy could be
conserved by optimization of any segment in DHS operation.
Despite the expansion of DHS in Europe, the operation of DHS in Serbia has been facing
numerous issues in recent years. The issues pertain to inefficient and uneconomical DHS
operations, which are compensated by raised prices of distributed heat, consequently causing
consumer dissatisfaction. The issues became evident when DHS stopped being subsidised by
the local governments, and especially after gas market liberalization. Uneconomical operation
of DHS is mostly the result of the way thermal energy is produced and of inefficient
management.
It is possible to change the meth...od of thermal energy production in DHS by replacing the
existing heat sources with less costly ones, such as cogeneration facilities running on fossil
fuels or biomass, or by introducing incineration, solar, and geothermal plants. However, any
change of heat sources in the current DHS constellation is a long-lasting process that requires
considerable investment. On the other hand, by improving the existing inefficient
management method, it is possible to significantly enhance DHS operations with relatively
small investment and it is this aspect of DHS operation improvement that is the focus of this
dissertation.
The conducted research was based on three initial assumptions: (1) DHS in cold and moderate
climate regions are among the most efficient and most economical ways of providing heat to
citizens in urban areas; (2) The installed capacity and the currently energy-inefficient and
uneconomical DHS operation in Serbia justify research aimed at optimizing these systems;
and (3) Development of robust and credible predictive thermal models of consumers and their
integration into management strategies of DHS can immensely contribute to more efficient
and more economical production and distribution of thermal energy.
The abovementioned assumptions converge into the aim of this research: development and
verification of predictive thermal models of consumers in district heating systems, which will
enable more energy-efficient and economical management of thermal energy production and
distribution.
The research includes a theoretical and a practical segment. The methods used are analysis,
synthesis, experiment, and result verification. Theoretical research involves a review and
referencing of foreign and domestic current and competent literature, while the synthesis of
the listed information is used to determine the most suitable approach to resolving the
identified issues. The first part of the practical segment involves the setup of an experimental
installation in the substation of the Niš DHS in order to sample relevant data, which are then
used for the development of predictive thermal models. The second part involves the
application of three methods of statistical learning (neural networks with direct signal
propagation and Bayesian regularization, support vector machines, and a boosting method) for
the creation of predictive models. The obtained results were verified by means of the data that
were not used for predictive modelling. Additionally, the potential of the developed models
was also verified against the data obtained from the Novi Sad DHS.
The following are the key contributions of this dissertation: (1) It analyzes the benefits of
DHS as a sustainable and energy-efficient method of providing heat to people living in urban
areas, with a special focus on the possible use of renewable energy sources; (2) It
systematizes previous research regarding predictive thermal modelling of consumers in DHS;
(3) It involves a setup of an experimental installation, which conducted continuous
measurement and sampling of relevant quantities from a Niš DHS substation; (4) It involves
development and testing of several types of predictive models of heating load for different
prediction horizons based on statistical learning methods: neural networks with direct signal
propagation and Bayesian regularization, support vector machines, and a boosting method
using the experimentally obtained data; (5) It analyzes the possibility of applying the selected
methods to predictive modelling in the differently operationally organized DHS in Novi Sad,
as well; (6) It develops short-term predictive models of outdoor temperature; and (7) It
establishes that the developed predictive models of heating load can be considerably
improved by introduction of predictive models of climatic parameters, in particular of the
outdoor temperature.
The obtained results indicate that the application of statistical learning methods, especially the
support vector machines, allows the development of predictive thermal models of consumers
with satisfactory performance. This creates the necessary conditions for their integration into
an advanced managerial environment and thus for optimal and more economical operation of
DHS.