CNN-LSTM-based building energy consumption prediction method and system
A technology for building energy consumption and forecasting methods, applied in forecasting, neural learning methods, instruments, etc., can solve problems such as difficulty in obtaining building parameters and errors, and achieve the effects of optimizing building energy management strategies, improving accuracy, and improving operating energy efficiency
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[0029] The present invention will be described in detail below in conjunction with specific embodiments. The following examples will help those skilled in the art to further understand the present invention, but do not limit the present invention in any form. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all belong to the protection scope of the present invention.
[0030] The present invention is a building energy consumption prediction method and system based on CNN-LSTM. Through data preprocessing and reorganization, a model of a specific structure is established, and the model is trained and saved to obtain the predicted value of energy consumption of the building. The algorithm can predict the energy consumption of public buildings. High-precision prediction of real-time and future energy consumption.
[0031] Such as figure 1 Shown, a kind of building energy...
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