Abstract
Building energy consumption prediction plays an important role in realizing building energy conservation control. Limited by some external factors such as temperature, there are some problems in practical applications, such as complex operation and low prediction accuracy. Aiming at the problem of low prediction accuracy caused by poor timing of existing building energy consumption prediction methods, a building energy consumption prediction and analysis method based on the deep learning network is proposed in this paper. Before establishing the energy consumption prediction model, the building energy consumption data source is preprocessed and analyzed. Then, based on the Keras deep learning framework, an improved long short-term memory (ILSTM) prediction model is built to support the accurate analysis of the whole cycle of the prediction network. At the same time, the adaptive moment (Adam) estimation algorithm is used to update and optimize the weight parameters of the model to realize the adaptive and rapid update and matching of network parameters. The simulation experiment is based on the actual dataset collected by a university in Southwest China. The experimental results show that the evaluation indexes MAE and RMSE of the proposed method are 0.015 and 0.109, respectively, which are better than the comparison method. The simulation experiment proves that the proposed method is feasible.
Highlights
With the advent of the twenty-first century, China has officially entered a period of rapid economic development
The current method lacks consideration of the time correlation of time series data and requires the artificial addition of time features. e calculation complexity is large, and it is difficult to maintain accurate prediction and analysis throughout the entire cycle. In response to this problem, this paper proposes a new building energy loss prediction model based on improved long shortterm memory (ILSTM) and adaptive moment (Adam) optimization algorithms to support the sustainable development of environmental resources. e paper modularizes the hidden layer nodes of the traditional LSTM network model based on clock distribution and completes the directional connection between modules according to the clock frequency from high to low so as to improve the fast calculation ability of the prediction network in different processing cycles
Academic Building Administration building Dormitory building buildings. is phenomenon reflects that different building types represent different energy use patterns. erefore, when building energy consumption prediction models, we should dig deeper into the hidden information contained in different building types
Summary
Building energy consumption prediction plays an important role in realizing building energy conservation control. Limited by some external factors such as temperature, there are some problems in practical applications, such as complex operation and low prediction accuracy. Aiming at the problem of low prediction accuracy caused by poor timing of existing building energy consumption prediction methods, a building energy consumption prediction and analysis method based on the deep learning network is proposed in this paper. Before establishing the energy consumption prediction model, the building energy consumption data source is preprocessed and analyzed. En, based on the Keras deep learning framework, an improved long shortterm memory (ILSTM) prediction model is built to support the accurate analysis of the whole cycle of the prediction network. E simulation experiment proves that the proposed method is feasible
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