Building electricity consumption prediction method and system based on Stacking model fusion

Abstract

The invention discloses a building electricity consumption prediction method and system based on Stacking model fusion, and belongs to the field of building electricity consumption prediction. According to the method, multiple regression models are integrated by adopting a Stacking model fusion algorithm, an electricity consumption Stacking integrated model is constructed, the advantages of the multiple models are integrated, and prediction deviation is reduced; for buildings with unstable electricity consumption, the electricity consumption Stacking integrated model is trained by utilizing multiple influence factors such as historical electricity consumption, temperature, wind power, humidity and time information, so that the prediction accuracy is improved, managers of the buildings caneffectively control the energy consumption of the buildings, and the situation that the difference between the electricity consumption and the estimated electricity consumption is too large is avoided; according to the invention, reasonable estimation and purchase are carried out when a building manager participates in electricity market transaction, so that the building manager can effectively control electric charge expenditure, electricity selling arrangement of an electric power department or an electricity selling company is facilitated, the effects of energy conservation and emission reduction can be achieved, and good social benefits and economic benefits are achieved.

Description

technical field [0001] The invention belongs to the field of building power consumption forecasting, and more particularly relates to a building power consumption forecasting method and system based on Stacking model fusion. Background technique [0002] The forecast of monthly electricity consumption in buildings belongs to the type of time series forecast. Time series is a group of random variables that depend on time. There is a dependency relationship between this group of variables, and the correlation characteristics indicate the continuity of the development of the predicted object. By describing the autocorrelation characteristics contained in it with a mathematical model, the past value and present value of the time series can be used to predict the future value. [0003] In the existing technology of building monthly power consumption forecasting, most of the forecasting methods use multivariate regression methods for forecasting. Through multivariate modeling of ...

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Problems solved by technology

[0006] Aiming at the above defects or improvement needs of the prior art, the present invention provides a method and system for predicting building power consumption based on Stacking model fusion, the purpose of which is to solve the existing method for building power consumption under the condition of unstable power usage state. Quantity forecasting, there is a technical problem of large forecasting error

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