Neural Network for Predicting Building Heat Consumption

Heat demand forecasting is necessary to achieve optimal management of building energy consumption. The purpose of this article is to identify the most important factors influencing the accuracy of forecasting heat consumption of buildings using neural networks, which is in line with the national strategy for the development of artificial intelligence of the Russian Federation. The article studies the dependence of modeling accuracy on various combinations of environmental parameters, as well as on the application of different activation functions of neural networks, widely used in the practice of creating artificial intelligence systems. It is demonstrated that machine learning models based on a large number of data on thermal consumption have great possibilities in predicting real patterns and trends of consumption, and the value of the average absolute percentage error of the best prediction model is comparable to the value of the maximum limit of the tolerable relative error of thermal energy measurements by the measuring channel of the heat meter. On the basis of data obtained using the developed system of remote monitoring of individual heating points of buildings, a comparison of actual values of heat consumption and values of heat consumption obtained using the prediction model was demonstrated. Savings of energy, heat carrier and other things at the object cannot be measured directly, because the savings represent the absence of consumption, so a universal approach using artificial intelligence for a technically sound and economically feasible method of predicting the results of the application of energy-saving solutions to compare the measured energy consumption before and after the implementation of energy-efficient measures may allow to improve the efficiency of decision-making in the field of saving energy resources.

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