# A Heat Load Prediction Method for District Heating Systems Based on the AE-GWO-GRU Model
## 3 Case Study  
### Research Object  
In this paper, the DHS of a multifunctional comprehensive region is taken as the research object. The total heating building area in this region is 43,000 m\({}^{2}\), including offices buildings, apartments, public restaurants, and clinics. The buildings have an ordinary brick-concrete structure with good thermal insulation performance. The DHS comprises three parts: the heat source, heating pipe network, and heat users, as shown in Figure 6. The heating system is equipped with a data acquisition system that collects and stores operation parameters and outdoor meteorological parameters in the database. These parameters include the temperature and pressure of the hot water supply and return, the hot water flow, outdoor temperature, and outdoor relative humidity. Due to the short running time of the data acquisition system and the insufficient amount of stored data, a data augmentation method needs to be discussed later to improve the model prediction performance. In this experiment, data collected from December 2023 to February 2024 were selected from the database, with measurements taken every 15 min, resulting in a total of 7464 data sets.  
Figure 4: Candidate wolves position update diagram.  
Figure 5: GWO algorithm flowchart.  
The heating objects of the system include diverse building types, varying heating times, and complex heating characteristics. The heat load is defined as the heat supplied by the heat source system per unit time to maintain the indoor calculated temperature under outdoor meteorological conditions. In this paper, it is assumed that the heating capacity of the system is approximately equal to the heat load, so the heating capacity is used as a proxy for the heat load. The original operational data of the heating objects from 8 February to 14 February 2024, are selected to further analyze the heat load characteristics, as shown in Figure 7.  
The comprehensive region, which serves both office and residential functions, requires 24-h heating during the heating season. As shown in Figure 7, the general trend of daily heat load changes is evident, generally decreasing as the outdoor temperature increases. The heat load data acquisition and control period of the original control system is 15 min. Due to the large area of the region, the physical distance between the heat users and the heat source is significant, resulting in a large time lag in heat consumption due to the transmission time of the hot water. The original simple feedback control strategy for heating return water, shown in Figure 8, controls the heat source load rate and adjusts the water supply temperature based on the deviation between the actual and set values of the heating return water temperature. However, this strategy struggles to meet the control  
Figure 6: Data acquisition system.  
Figure 7: One-week heat load change trend diagram (acquisition cycle is 15 min).  
requirements of systems with large time lags. When heating demand changes, the system cannot adjust in time to meet the new demand. Additionally, using heating capacity as a proxy for heat load results in large short-term fluctuations due to the influence of the control strategy, significantly complicating accurate load prediction.