A study on data-driven hybrid heating load prediction methods in low-temperature district heating: An example for nursing homes in Nordic countries. (1st October 2022)

• Record Type: Journal Article
• Authors: Ding, Yiyu; Timoudas, Thomas Ohlson; Wang, Qian; Chen, Shuqin; Brattebø, Helge; Nord, Natasa
• Is Part Of: Energy conversion and management. Volume 269(2022)
• Publication Date: 2022-10-01
• Subjects: Nursing homes -- District heating load prediction -- Linear regression -- Artificial neural network -- Low-temperature district heating; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2022.116163
• Languages: English
• ISSNs: 0196-8904

Abstract

Highlights: Long-term prediction for sizing, short-term prediction for operation were proposed. Model additionally including historical load data as input stood out in prediction. Measured district heating data for 20 nursing homes in Nordic climate were used. Possible application for integrating two building-sized heat pumps was proposed. 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, f 72 and g 120, 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.