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Machine learning in district heating system energy optimization
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0002-8681-9572
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0002-4031-2872
2014 (English)In: 2014 IEEE International Conference on Pervasive Computing and Communications workshops: PERCOM WORKSHOPS 2014, Budapest, Hungary; 24-28 March 2014, Piscataway, NJ: IEEE Communications Society, 2014, p. 224-227, article id 6815206Conference paper, Published paper (Refereed)
Abstract [en]

This paper introduces a work in progress, where we intend to investigate the application of Reinforcement Learning (RL) and online Supervised Learning (SL) to achieve energy optimization in District-Heating (DH) systems. We believe RL is an ideal approach since this task falls under the control-optimization problem where RL has yielded optimal results in previous work. The magnitude and scale of a DH system complexity incurs the curse of dimensionalities and model, hereby making RL a good choice since it provides a solution for the problem. To assist RL even further with the curse of dimensionalities, we intend to investigate the use of SL to reduce the state space. To achieve this, we shall use historical data to generate a heat load sub-model for each home. We believe using the output of these sub-models as feedback to the RL algorithm could significantly reduce the complexity of the learning task. Also, it could reduce convergence time for the RL algorithm. The desired goal is to achieve a realtime application, which takes operational actions when it receives new direct feedback. However, considering the dynamics of DH system such as large time delay and dissipation in DH network due to various factors, we hope to investigate things such as the appropriate data sampling rate and new parameters / sensors that could improve knowledge about the state of the system, especially on the consumer side of the DH network.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Communications Society, 2014. p. 224-227, article id 6815206
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing; Enabling ICT (AERI)
Identifiers
URN: urn:nbn:se:ltu:diva-32000DOI: 10.1109/PerComW.2014.6815206Scopus ID: 2-s2.0-84901361899Local ID: 658e9b02-e0f4-418c-a2e3-7fe90e666b29OAI: oai:DiVA.org:ltu-32000DiVA, id: diva2:1005234
Conference
IEEE International Conference on Pervasive Computing and Communication Workshops : 24/03/2014 - 28/03/2014
Note
Godkänd; 2014; 20140605 (samidu)Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2018-07-10Bibliographically approved
In thesis
1. Applied Machine Learning in District Heating System
Open this publication in new window or tab >>Applied Machine Learning in District Heating System
2018 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

In an increasingly applied domain of pervasive computing, sensing devices are being deployed progressively for data acquisition from various systems through the use of technologies such as wireless sensor networks. Data obtained from such systems are used analytically to advance or improve system performance or efficiency. The possibility to acquire an enormous amount of data from any target system has made machine learning a useful approach for several large-scale analytical solutions. Machine learning has proved viable in the area of the energy sector, where the global demand for energy and the increasingly accepted need for green energy is gradually challenging energy supplies and the efficiency in its consumption.

This research, carried out within the area of pervasive computing, aims to explore the application of machine learning and its effectiveness in the energy sector with dependency on sensing devices. The target application area readily falls under a multi-domain energy grid which provides a system across two energy utility grids as a combined heat and power system. The multi-domain aspect of the target system links to a district heating system network and electrical power from a combined heat and power plant. This thesis, however, focuses on the district heating system as the application area of interest while contributing towards a future goal of a multi-domain energy grid, where improved efficiency level, reduction of overall carbon dioxide footprint and enhanced interaction and synergy between the electricity and thermal grid are vital goals. This thesis explores research issues relating to the effectiveness of machine learning in forecasting heat demands at district heating substations, and the key factors affecting domestic heat load patterns in buildings.

The key contribution of this thesis is the application of machine learning techniques in forecasting heat energy consumption in buildings, and our research outcome shows that supervised machine learning methods are suitable for domestic thermal load forecast. Among the examined machine learning methods which include multiple linear regression, support vector machine,  feed forward neural network, and regression tree, the support vector machine performed best with a normalized root mean square error of 0.07 for a 24-hour forecast horizon. In addition, weather and time information are observed to be the most influencing factors when forecasting heat load at heating network substations. Investigation on the effect of using substation's operational attributes, such as the supply and return temperatures, as additional input parameters when forecasting heat load shows that the use of substation's internal operational attributes has less impact.

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2018
Series
Licentiate thesis / Luleå University of Technology, ISSN 1402-1757
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-68486 (URN)978-91-7790-130-3 (ISBN)978-91-7790-131-0 (ISBN)
Presentation
2018-05-15, Skellefteå, 09:00 (English)
Opponent
Supervisors
Available from: 2018-04-24 Created: 2018-04-24 Last updated: 2018-06-28Bibliographically approved

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Publisher's full textScopushttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6815206

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Idowu, SamuelÅhlund, ChristerSchelén, Olov

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