Abstract
District heating system is designed to minimize energy consumption and environmental pollution by employing centralized production facilities connected to demand regions. Traditionally, optimization based algorithms were applied to the heat production planning problem in the district heating systems. Optimization-based models provide near optimal solutions, while it takes a while to generate solutions due to the characteristics of the underlying solution mechanism. When prompt re-planning due to any parameter changes is necessary, the traditional approaches might be inefficient to generate modified solutions quickly. In this study, we developed a two-phase solution mechanism, where deep learning algorithm is applied to learn optimal production patterns from optimization module. In the first training phase, the optimization module generates optimal production plans for the input scenarios derived from operations history, which are provided to the deep learning module for training. In the second planning phase, the deep learning module with trained parameters predicts production plan for the test scenarios. The computational experiments show that after the training process is completed, it has the characteristic of quickly deriving results appropriate to the situation. By combining optimization and deep learning modules in a solution framework, it is expected that the proposed algorithm could be applied to online optimization of district heating systems.
Highlights
A district heating system supplies heat to local demands through a heat transfer network from centralized production facilities [1]
We propose a heat production planning algorithm applying the deep learning technique, which has been successfully applied to various prediction and pattern recognition problems in real world applications [15,16,17,18,19,20,21]
We proposed deep learning based heat production planning models for a district
Summary
A district heating system supplies heat to local demands through a heat transfer network from centralized production facilities [1]. Heat demand for district heating is generally related to house and building heating and tends to be higher in winter than in summer. Local heat demand is high at night and early morning when the temperature is lower. It is reasonable to produce heat at a time when demand is high (typically mid-nights), but due to the limitation of the maximum production capacity and the difference in heat production cost by time, it might be economical to adjust heat production schedules considering heat production productivity. If production cost is lower in day time, it would be better to produce heat during day time when heat demand is low and to store heat in Energies 2020, 13, 6641; doi:10.3390/en13246641 www.mdpi.com/journal/energies
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