Joint Planning of Heat and Power Production Using Hybrid Deep Neural Networks

Jungwoo Ahn, Sangjun Lee, In-Beom Park () and Kwanho Kim ()
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Jungwoo Ahn: 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, 1-19

Abstract: As demand for heat and power continues to grow, production planning of a combined heat and power (CHP) system becomes one of the most crucial optimization problems. Due to the fluctuations in demand and production costs of heat and power, it is necessary to quickly solve the production planning problem of the contemporary CHP system. In this paper, we propose a Hybrid Time series Informed neural Network (HYTIN) in which, a deep learning-based planner for CHP production planning predicts production levels for heat and power for each time step. Specifically, HYTIN supports inventory-aware decisions by utilizing a long short-term memory network for heat production and a convolutional neural network for power production. To verify the effectiveness of the proposed method, we build ten independent test datasets of 1200 h each with feasible initial states and common limits. Experimentation results demonstrate that HYTIN achieves lower operation cost than the other baseline methods considered in this paper while maintaining quick inference time, suggesting the viability of HYTIN when constructing production plans under dynamic variations in demand in CHP systems.

Keywords: smart energy systems; building energy management systems; energy forecasting; hybrid neural networks (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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