Construction of deep learning prediction model for buildingcooling load with limited sensors

Accurate prediction of building cooling load is a crucial foundation for implementing model-based optimization control of HVAC systems and achieving energy efficiency in buildings. Establishing an accurate cooling load prediction model for actual buildings is a challenging engineering task, including sensor installation, data communication and reading, data preprocessing and model identification. In practice, it is difficult to obtain the necessary model identification data due to the limited number and variety of sensors installed or being damaged during operation, and the complex site conditions of the actual building make it difficult to establish a physical model. In order to solve the above two problems, this paper develops a deep learning model for predicting building cooling load with limited sensors. Through an actual case study, this paper analyses the main factors affecting cooling load, constructs the model input set by using the methods of sensor substitution, supplementation and data interpolation, establishes a high-performance cooling load prediction model by using the long short-term memory model, and analyses the model performance under different feature inputs. In the cooling load prediction for a tobacco factory workshop, the mean absolute percentage error (MAPE) of the long short-term memory model established by the recognition process of the cooling load prediction model constructed in this paper is 9.09%.