About combining determinated and stochastic approaches for prediction of the heating balance of the building for water sports

Abstract

Forecasting the demand for thermal energy by energy complexes of buildings and structures is an urgent task. To achieve the necessary accuracy of the calculation, it is customary to use various deterministic methods based on the available changing and slightly changing data about the object of study. At the same time, statistical data can also be used in analysis by stochastic methods. The purpose of this article is to analyze the question of the admissibility of combining deterministic and stochastic approaches in order to increase the accuracy of the calculation. Formulas for calculating the components of the expenditure part of the heat balance are shown on the example of a building for water sports. Based on the above formulas, a calculation with a monthly discretization in the period from January 2009 is carried out. until January 2019. An example is given of calculating the accuracy of the forecast of demand for thermal energy through multivariate regression analysis and the use of artificial neural networks. Based on the same data, an artificial neural network was trained on seven different factors: six independent and seventh — the idealized value of the building’s heat loss through the building envelope. An example of the analysis of a building for practicing water sports shows the inadmissibility of the described approach if the same initial data are used in the deterministic and stochastic method. Results: the accuracy of the forecast made using regression analysis increases with an increase in the number of factors. However, the use of an additional group of factors in the stochastic method, for example, which are numerically processed climate data that are already used as initial data, will lead to an unreasonable overestimation of the significance of the twice used factor. The presence in the predictive models using artificial neural networks of collinearity and multicollinearity of variables does not negatively affect the forecast. Conclusion: the combination of the deterministic and stochastic approaches in preparing the predicted heat balance by using only the same input data that is used in the stochastic approach in the deterministic approach is unacceptable.

Keywords

• comparable conditions
• multivariate regression
• forecasting
• demand for thermal energy
• artificial neural network
• error