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Applied Machine Learning in District Heating System
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
2018 (engelsk)Licentiatavhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Luleå: Luleå University of Technology, 2018.
Serie
Licentiate thesis / Luleå University of Technology, ISSN 1402-1757
HSV kategori
Forskningsprogram
Distribuerade datorsystem
Identifikatorer
URN: urn:nbn:se:ltu:diva-68486ISBN: 978-91-7790-130-3 (tryckt)ISBN: 978-91-7790-131-0 (digital)OAI: oai:DiVA.org:ltu-68486DiVA, id: diva2:1200786
Presentation
2018-05-15, Skellefteå, 09:00 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2018-04-24 Laget: 2018-04-24 Sist oppdatert: 2020-03-06bibliografisk kontrollert
Delarbeid
1. Applied Machine Learning: Forecasting Heat Load in District Heating System
Åpne denne publikasjonen i ny fane eller vindu >>Applied Machine Learning: Forecasting Heat Load in District Heating System
2016 (engelsk)Inngår i: Energy and Buildings, ISSN 0378-7788, E-ISSN 1872-6178, Vol. 133, s. 478-488Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Forecasting energy consumption in buildings is a key step towards the realization of optimized energy production, distribution and consumption. This paper presents a data driven approach for analysis and forecast of aggregate space and water thermal load in buildings. The analysis and the forecast models are built using district heating data unobtrusively collected from ten residential and commercial buildings located in Skellefteå, Sweden. The load forecast models are generated using supervised machine learning techniques, namely, support vector machine, regression tree, feed forward neural network, and multiple linear regression. The model takes the outdoor temperature, historical values of heat load, time factor variables and physical parameters of district heating substations as its input. A performance comparison among the machine learning methods and identification of the importance of models input variables is carried out. The models are evaluated with varying forecast horizons of every hour from 1 up to 48 hours. Our results show that support vector machine, feed forward neural network and multiple linear regression are more suitable machine learning methods with lower performance errors than the regression tree. Support vector machine has the least normalized root mean square error of 0.07 for a forecast horizon of 24 hour.

HSV kategori
Forskningsprogram
Distribuerade datorsystem
Identifikatorer
urn:nbn:se:ltu:diva-59596 (URN)10.1016/j.enbuild.2016.09.068 (DOI)000389087300045 ()2-s2.0-84992362157 (Scopus ID)
Merknad

Validerad; 2016; Nivå 2; 2016-11-08 (andbra)

Tilgjengelig fra: 2016-10-10 Laget: 2016-10-10 Sist oppdatert: 2018-07-10bibliografisk kontrollert
2. Forecasting Heat Load for Smart District Heating Systems: A Machine Learning Approach
Åpne denne publikasjonen i ny fane eller vindu >>Forecasting Heat Load for Smart District Heating Systems: A Machine Learning Approach
2014 (engelsk)Inngår i: 2014 IEEE International Conference on Smart Grid Communications (SmartGridComm 2014): Venice, 3-6 Nov. 2014, Piscataway, NJ: IEEE Communications Society, 2014, s. 554-559Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
Piscataway, NJ: IEEE Communications Society, 2014
HSV kategori
Forskningsprogram
Distribuerade datorsystem; Möjliggörande IKT (FOI)
Identifikatorer
urn:nbn:se:ltu:diva-34352 (URN)10.1109/SmartGridComm.2014.7007705 (DOI)2-s2.0-84922458188 (Scopus ID)886feb09-67f9-4347-84a0-516e622f7f5c (Lokal ID)978-1-4799-4934-2 (ISBN)886feb09-67f9-4347-84a0-516e622f7f5c (Arkivnummer)886feb09-67f9-4347-84a0-516e622f7f5c (OAI)
Konferanse
International Conference on Smart Grid Communications : 06/11/2014 - 06/11/2014
Merknad
Godkänd; 2014; 20140817 (samidu)Tilgjengelig fra: 2016-09-30 Laget: 2016-09-30 Sist oppdatert: 2018-07-10bibliografisk kontrollert
3. Machine learning in district heating system energy optimization
Åpne denne publikasjonen i ny fane eller vindu >>Machine learning in district heating system energy optimization
2014 (engelsk)Inngår i: 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, s. 224-227, artikkel-id 6815206Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
Piscataway, NJ: IEEE Communications Society, 2014
HSV kategori
Forskningsprogram
Distribuerade datorsystem; Möjliggörande IKT (FOI)
Identifikatorer
urn:nbn:se:ltu:diva-32000 (URN)10.1109/PerComW.2014.6815206 (DOI)2-s2.0-84901361899 (Scopus ID)658e9b02-e0f4-418c-a2e3-7fe90e666b29 (Lokal ID)658e9b02-e0f4-418c-a2e3-7fe90e666b29 (Arkivnummer)658e9b02-e0f4-418c-a2e3-7fe90e666b29 (OAI)
Konferanse
IEEE International Conference on Pervasive Computing and Communication Workshops : 24/03/2014 - 28/03/2014
Merknad
Godkänd; 2014; 20140605 (samidu)Tilgjengelig fra: 2016-09-30 Laget: 2016-09-30 Sist oppdatert: 2018-07-10bibliografisk kontrollert

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