A Multi-Head Attention-Based TimesNet for Heat Production Planning Under Unknown Future Demands

Jahun Kim, Sangjun Lee, In-Beom Park () and Kwanho Kim ()
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Jahun Kim: Department of Industrial and Systems Engineering, Dongguk University, Seoul 04620, Republic of Korea
Sangjun Lee: Department of Industrial and Systems Engineering, Dongguk University, Seoul 04620, Republic of Korea
In-Beom Park: Department of Industrial and Systems Engineering, Dongguk University, Seoul 04620, Republic of Korea
Kwanho Kim: Department of Industrial and Systems Engineering, Dongguk University, Seoul 04620, Republic of Korea

Energies, 2025, vol. 18, issue 22, 1-17

Abstract: Efficient operational planning in district heating systems (DHSs) is essential for minimizing operating costs and maximizing energy efficiency. However, since practitioners must determine future production plans under unknown future demands and costs in real-world energy systems, it is challenging to solve the production planning problems of DHSs. In this paper, we propose a multi-head attention-based TimesNet (MATN) in which a transformer decoder is incorporated that operates solely on a 24 h lookback window without requiring any future information. Specifically, the model is trained in an end-to-end manner, for which the training dataset was built by solving a mixed integer programming (MIP) model. Experimental results demonstrate that the proposed MATN model significantly outperforms baseline deep learning-based methods. A qualitative analysis of the hourly production plans further indicates that MATN generates robust operational plans that mimic those generated by an MIP model, which suggests the effectiveness of the proposed approach in terms of economic efficiency and operational stability without depending on future information.

Keywords: district heating system; heat production planning; multi-head attention; deep neural networks; unknown future demands (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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