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Demand-side Management for Buildings Connected to District Heating
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School of Science |
Doctoral thesis (article-based)
| Defence date: 2025-09-09
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Language
en
Pages
70 + app. 56
Series
Aalto University publication series Doctoral Theses, 162/2025
Abstract
Buildings, with various functions, are essential for maintaining comfortable indoor environments and meeting customers’ fundamental needs, which leads to significant energy consumption. For example, buildings account for one-third of the world’s energy consumption and 40% of the European Union’s (EU) energy consumption. In EU, the energy consumption of buildings’ heating systems is primary, surpassing electricity and cooling requirements. There is a pressing need to conserve heating energy in buildings. To address the growing challenges posed by the high energy demand of buildings, there is an urgent need for innovative methods that improve energy efficiency while maintaining indoor thermal comfort. Demand response (DR) offers a promising solution by enabling buildings to adjust their heating demand in response to external factors such as energy prices and system load. This thesis presents a comprehensive investigation into the development and implementation of DR strategies for buildings connected to district heating (DH) systems through three interrelated studies.
The first study developed a novel analysis method for annual DH load patterns for a prominent city and various building types, examining the structure of DH load. Based on these analyses, general short-term DH load forecasting methods were proposed at both the building and city levels. These methods utilize multiple linear regression and artificial neural network models to forecast DH load.
The second study developed a novel model predictive control (MPC) framework grounded in a simple physical room model and carefully selected parameters. This MPC framework emphasizes control accuracy, energy efficiency, and hydraulic stability to maintain room-level thermal comfort. Furthermore, this study introduced a multi-criteria evaluation method to assess the performance of MPC controllers, aiding decision-makers in finding the most preferred MPC parameters.
The third study developed a novel integrated DR method for multiple rooms with varying thermal comfort requirements. This method comprises an optimization layer and a control layer aimed at maintaining thermal comfort while conserving energy and reducing heating costs. This study applies fuzzy logic in adjusting dynamic indoor temperature setpoints based on dynamic heat prices and adopts the optimal heat supply method for the rooms with various heat sources. The results show that proposed methods and strategies in this thesis do well in maintain indoor thermal comfort, enhancing energy usage flexibility with minimum heat costs for buildings connected to DH.
Description
Supervising professor
Lahdelma, Risto, Prof., Aalto University, Department of Mathematics and Systems Analysis, FinlandThesis advisor
Wang, Haichao, Aalto University, Academy Research Fellow, FinlandOther note
Parts
- [Publication 1]: Hua, Pengmin; Wang, Haichao; Xie, Zichan; Lahdelma, Risto. District heating load patterns and short-term forecasting for buildings and city level. Energy 2024; 289: 129866.
Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202401041236DOI: 10.1016/j.energy.2023.129866 View at publisher
- [Publication 2]: Hua, Pengmin; Wang, Haichao; Xie, Zichan; Lahdelma, Risto. Multi-criteria evaluation of novel multi-objective model predictive control method for indoor thermal comfort. Energy 2024; 289: 129883.
Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202401041101DOI: 10.1016/j.energy.2023.129883 View at publisher
- [Publication 3]: Hua, Pengmin; Wang, Haichao; Xie, Zichan; Lahdelma, Risto. Integrated demand response method for heating multiple rooms based on fuzzy logic considering dynamic price. Energy 2024; 307: 132577.
Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202408285973DOI: 10.1016/j.energy.2024.132577 View at publisher