Fuzzy information granulation and support vector machine-based heating load prediction method

Abstract

The invention discloses a fuzzy information granulation and support vector machine-based heating load prediction method, relates to a heating load prediction method, belongs to the technical field of heating load prediction, and provides a novel heating load prediction method for overcoming the shortcoming of low adaptability of the conventional point prediction method to own nonlinearity or external disturbance randomness of a heating system load. The prediction method mainly comprises the following steps of: 1, performing fuzzy information granulation on sample data, and constructing an information granulation sample set; 2, establishing a support vector machine prediction model by utilizing the constructed information granulation sample set; 3, determining parameters of the support vector machine prediction model by adopting a cross validation method; and 4, evaluating the prediction accuracy of the method. The load prediction method adapts to own nonlinearity of the heating system load as well as the external disturbance randomness, and the increasing engineering requirements of load optimal scheduling, energy-saving control and the like of a heating system are met.

Description

technical field [0001] The invention relates to a heating load forecasting method, which belongs to the technical field of heating load forecasting. Background technique [0002] my country's building heating energy consumption accounts for about one-third of the whole society's energy consumption, and the building heating energy-saving potential is huge. The influencing factors of building heating load are complex, mainly divided into two categories: 1) External disturbance factors (outdoor temperature, solar radiation, wind speed and other meteorological factors), which are random; 2) Self-characteristic factors (thermal characteristics of buildings , geometric characteristics, structural characteristics, use characteristics and other factors), which have nonlinear characteristics such as large inertia and large time delay. Therefore, the heating load presents its own nonlinear characteristics such as large inertia and large time lag, and the randomness of external distur...

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Problems solved by technology

[0005] In order to solve the problem that the existing point forecasting method has low adaptability to the nonlinearity of the heating system load itself or the randomness of the external disturbance, the load forecasting accuracy is not high, and further provides a method based on fuzzy information granulation and support vector machine Heating Load Forecasting Method

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