A study on data-driven hybrid heating load prediction methods in low-temperature district heating: An example for nursing homes in Nordic countries

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A study on data-driven hybrid heating load prediction methods in low-temperature district heating: An example for nursing homes in Nordic countries

Article at: Energy Conversion and Management

Title: A study on data-driven hybrid heating load prediction methods in low-temperature district heating: An example for nursing homes in Nordic countries

Language: English

Authors: Yiyu Ding a,*, Thomas Ohlson Timoudas b, Qian Wang c,d, Shuqin Chen e, Helge Brattebø a,Natasa Nord a

a Department of Energy and Process Engineering, Norwegian University of Science and Technology (NTNU), Trondheim 7491, Norway

b RISE Research Institutes of Sweden, Sweden

c Department of Civil and Architectural Engineering, KTH Royal Institute of Technology, Brinellvagen ¨ 23, Stockholm 100 44, Sweden

d Uponor AB, Hackstavagen ¨ 1, Vasterås ¨ 721 32, Sweden

e College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China

Abstract: In the face of green energy initiatives and progressively increasing shares of more energy-efficient buildings, there is a pressing need to transform district heating towards low-temperature district heating. The substantially lowered supply temperature of low-temperature district heating broadens the opportunities and challenges to integrate distributed renewable energy, which requires enhancement on intelligent heating load prediction. Meanwhile, to fulfill the temperature requirements for domestic hot water and space heating, separate energy conversion units on user-side, such as building-sized boosting heat pumps shall be implemented to upgrade the temperature level of the low-temperature district heating network. This study conducted hybrid heating load prediction methods with long-term and short-term prediction, and the main work consisted of four steps: (1) acquisition and processing of district heating data of 20 district heating supplied nursing homes in the Nordic climate (2016–2019); (2) long-term district heating load prediction through linear regression, energy signature curve in hourly resolution, providing an overall view and boundary conditions for the unit sizing; (3) short-term district heating load prediction through two Artificial Neural Network models, f72 and g120, with different prediction input parameters; (4) evaluation of the predicted load profiles based on the measured data. Although the three prediction models met the quality criteria, it was found that including the historical hourly heating loads as the input to the forecasting model enhanced the prediction quality, especially for the peak load and low-mild heating season. Furthermore, a possible application of the heating load profiles was proposed by integrating two building-sized heat pumps in low-temperature district heating, which may be a promising heat supply method in low-temperature district heating.