Abstract
The refined control of heating substations is of great significance for on-demand heating provision and for the efficient operation of district heating systems (DHSs). This paper proposes an integrated control strategy for substations based on the prediction of the water-supply temperature and indoor temperature. Firstly, online sequential extreme learning machine (OS-ELM) is used to predict the water-supply temperature. Then, a linear prediction model is established to predict the indoor temperature. Finally, the integrated regulation strategy is established with the goal of minimizing operational costs, aiming at ensuring heating quality and meeting the limits of the flow rate and of the supply- and return-water temperatures. The heat-saving rate, power-saving rate and indoor-temperature satisfactory rate are introduced to evaluate the regulation effect of the proposed method. The field study results show that the performance index of operation executed with the regulation strategy proposed in this paper is 9.31%, 16.33% and 20.87% higher than that without our energy-saving regulation strategy respectively. The fluctuations in the water-supply pressure and differential pressure of the secondary network are significantly reduced, and the energy-saving effect is obvious.
Highlights
Heating systems account for about 21% of building energy consumption and are the key objects of low-carbon energy reforms [1]
In order to improve the control effect of heating substations, this paper proposes an integrated strategy featuring the minimization of operational costs by combining watersupply-temperature and indoor-temperature predictions
The results showed that the prediction accuracy and generalization ability of limit learning machine (ELM) were better than BP neural networks and genetic algorithm optimization neural networks (GA-BPs)
Summary
Heating systems account for about 21% of building energy consumption and are the key objects of low-carbon energy reforms [1]. When aiming at ensuring users’ thermal comfort, to achieve on-demand heating provision, energy conservation and consumption reduction is an urgent problem. Aiming at minimizing operational costs, Gu et al [3] proposed an optimization model based on mixed integer nonlinear programming considering the thermal inertia of district heating networks and buildings. The obtained results of a simulation of an actual heating system in Jilin Province showed high wind-power utilization with low operational costs. Fang et al [4] used the genetic algorithm (GA) to optimize the water-supply temperature of a multi-heat-source branch heating network, with the goal of optimizing the total costs of fuel consumption and pump power consumption of the heating system. There are studies on strategies for the multi-objective optimization of heating systems
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