Building energy consumption prediction method and system based on improved LSTM

Abstract

The invention discloses a building energy consumption prediction method and system based on an improved LSTM. The method comprises the following steps: obtaining an optimal parameter corresponding to an LSTM neural network; introducing the optimal parameters into the LSTM variant neural network, optimizing hyper-parameters in the LSTM variant neural network by using a stochastic gradient optimization algorithm based on weight attenuation to obtain optimal hyper-parameters of the LSTM variant neural network, and taking the LSTM variant neural network corresponding to the optimal hyper-parameters as an optimal LSTM prediction model; and processing the acquired data influencing the building load by using the optimal LSTM prediction model, and predicting the load data of the building at specified time to realize building energy consumption prediction. The method is higher in prediction precision, better in stability and more suitable for commercial building short-term energy consumption prediction.

Description

technical field [0001] The invention belongs to the technical field of energy consumption prediction, and in particular relates to a building energy consumption prediction method and system based on an improved LSTM. Background technique [0002] With the acceleration of urbanization and the increase in the number of urban buildings, the proportion of building energy consumption in the entire energy consumption system is increasing. Global building energy consumption has surpassed industry and transportation, accounting for 46% of total energy consumption, and building carbon emissions account for up to 36%. Humans spend 90% of their time in buildings, and people's continuous pursuit of thermal comfort has resulted in an increase in building energy consumption and greenhouse gases, which makes energy demand management in the building industry, which accounts for a huge proportion of energy consumption, an important research field. [0003] Among all building types, the ene...

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