Doctoral theses of the School of Science at Aaltodoc (external link)
Doctoral theses of the School of Science are available in the open access repository maintained by Aalto, Aaltodoc.
Public defence in the field of Systems and Operations Research, M.Sc. Pengmin Hua
Demand-side management for buildings connected to district heating
Public defence from the Aalto University School of Science, Department of Mathematics and Systems Analysis
Public defence from the Aalto University School of Science, Department of Mathematics and Systems Analysis
When
–
Where
Event language(s)
English
Title of the thesis: Demand-side management for buildings connected to district heating
Thesis defender: Pengmin Hua
Opponent: Professor Natasa Nord, Norwegian University of Science and Technology (NTNU), Norway
Custos: Professor Risto Lahdelma, MS & ENG Aalto University School of Science
This thesis focuses on demand-side management in buildings connected to district heating systems, aiming to enhance energy efficiency and operational flexibility while safeguarding indoor thermal comfort.
The research makes a significant contribution to the field of building energy systems by addressing practical gaps in heat load forecasting, control strategies, and demand response. The study produced three principal outcomes: first, a generalizable methodology for district heating load pattern analysis and short-term forecasting at both city and building levels, employing regression and neural network models; second, the development of a multi-objective model predictive control framework capable of balancing thermal comfort, energy efficiency, and hydraulic stability in room-level applications; and third, the introduction of an integrated demand response strategy, combining fuzzy logic and dynamic pricing to optimize multi-room heating performance and reduce costs.
The findings demonstrate that advanced demand-side management methods can significantly improve the efficiency and flexibility of district heating systems without compromising comfort. This work thus provides new knowledge and practical tools for energy utilities, engineers, and policymakers, supporting the integration of intelligent control and demand response strategies into sustainable urban heating infrastructures.
Keywords: District heating; Machine learning; Model predictive control; Demand response; Energy efficiency
Contact information:
+358504704803
pengmin.hua@aalto.fi
https://www.linkedin.com/in/pengmin-hua-9427711b6/
Thesis available for public display 7 days prior to the defence at Aaltodoc.
Doctoral theses of the School of Science
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