Abstract
This paper analyses the factors affecting the heating consumption of a heating substation. The input parameters of neural network prediction model are analysed and selected. The average absolute error, average absolute percentage error, and mean square error are used to evaluate the effect of the prediction model. The results show that when the model input parameters are the maximum outdoor temperature, the average outdoor temperature, the average temperature difference between the primary supply and return of domestic hot water, the heating load in the previous three days, the heating load in the previous two days, the heating load in the previous day and when the model input parameters are the maximum outdoor temperature, the minimum outdoor temperature, the average outdoor temperature, the average temperature difference between the primary supply and return of domestic hot water, the heating load of the previous three days, the heating load of the previous two days, the heating load of the previous day, the effects are better.
Highlights
Building energy consumption accounts for a large proportion of various energy consumption in China, building energy saving is of great significance to energy saving
Among the total energy consumption of buildings in China, urban heating energy consumption accounts for the largest proportion
Wang Su Yu adopted a data mining method for heating operation. This method takes into account the thermal inertia of the building and introduces the concept of equivalent outdoor air temperature, and finds out the relationship between the heat load and the outdoor air temperature in previous days.[3]
Summary
Building energy consumption accounts for a large proportion of various energy consumption in China, building energy saving is of great significance to energy saving. Werner used statistical analysis methods to analyze the influence of outdoor temperature, solar radiation, wind speed and other factors on the heating load. Wang Su Yu adopted a data mining method for heating operation This method takes into account the thermal inertia of the building and introduces the concept of equivalent outdoor air temperature, and finds out the relationship between the heat load and the outdoor air temperature in previous days.[3] Architectural factors include building age, building type, building function and building envelope structure. Matteo Caldera et al used the sensitivity analysis method to analyze the relationship between the geometric and thermophysical parameters of the building and the heating energy consumption of the building They pointed out that the construction age of the building will affect the heating energy consumption of the building. The factors that affect the accuracy of heat load forecasting model are researched, and the prediction results of different input parameters are compared and analysed to obtain the input parameters to ensure the accuracy of load forecasting
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