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Forecasting Heat Load for Smart District Heating Systems: A Machine Learning Approach
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.
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 Smart Grid Communications (SmartGridComm 2014): Venice, 3-6 Nov. 2014, Piscataway, NJ: IEEE Communications Society, 2014, p. 554-559Conference paper, Published paper (Refereed)
Abstract [en]

The rapid increase in energy demand requires effective measures to plan and optimize resources for efficient energy production within a smart grid environment. This paper presents a data driven approach to forecasting heat load for multi- family apartment buildings in a District Heating System (DHS). The forecasting model is built using six and eleven weeks of data from five building substations. The external factors and internal factors influencing the heat load in substations are parameters used as our model’s input. Short-term forecast models are generated using four supervised Machine Learning (ML) techniques: Support Vector Regression (SVR), Regression Tree, Feed Forwards Neural Network (FFNN) and Multiple Linear Regression (MLR). Performance comparison among these ML methods was carried out. The effects of combining the internal and external factors influencing heat load at substations was studied. The models are evaluated with varying horizon up to 24-hours ahead. The results show that SVR has the best accuracy of 5.6% MAPE for the best-case scenario.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Communications Society, 2014. p. 554-559
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing; Enabling ICT (AERI)
Identifiers
URN: urn:nbn:se:ltu:diva-34352DOI: 10.1109/SmartGridComm.2014.7007705Local ID: 886feb09-67f9-4347-84a0-516e622f7f5cISBN: 978-1-4799-4934-2 (print)OAI: oai:DiVA.org:ltu-34352DiVA, id: diva2:1007602
Conference
International Conference on Smart Grid Communications : 06/11/2014 - 06/11/2014
Note
Godkänd; 2014; 20140817 (samidu)Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2018-05-09Bibliographically 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: 2018-05-15Bibliographically approved

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