Research on Prediction Model of Building Energy Consumption in Shopping Mall Based on Chaos Theory

2021 ◽  
Author(s):  
Wenqiang Jing ◽  
Meng Zhen ◽  
Hongjie Guan ◽  
Wei Luo ◽  
XinYi Liu
Keyword(s):  
Energy Consumption ◽  
Prediction Model ◽  
Chaos Theory ◽  
Building Energy ◽  
Shopping Mall ◽  
Building Energy Consumption

scholarly journals Intelligent Prediction Method of Building Energy Consumption Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Bingqian Fan ◽  
Xuanxuan Xing
Keyword(s):  
Deep Learning ◽  
Energy Consumption ◽  
Prediction Model ◽  
Prediction Accuracy ◽  
Simulation Experiment ◽  
Short Term Memory ◽  
Building Energy ◽  
Building Energy Consumption ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

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.

scholarly journals Predicting Building Energy Consumption with a New Grey Model

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Yan Zhang ◽  
Huiping Wang ◽  
Yi Wang
Keyword(s):  
Energy Consumption ◽  
Prediction Model ◽  
Building Energy ◽  
Grey Model ◽  
Annual Growth Rate ◽  
Grey Prediction ◽  
Building Energy Consumption ◽  
Total Energy Consumption ◽  
Grey Prediction Model ◽  
Grey Models

Based on the existing grey prediction model, this paper proposes a new grey prediction model (the fractional discrete grey model, FDGM (1, 1, t α )), introduces the modeling mechanism and characteristics of the FDGM (1, 1, t α ), and uses three groups of data to verify its effectiveness compared with that of other grey models. This paper forecasts the building energy consumption in China over the next five years based on the idea of metabolism. The results show that the FDGM (1, 1, t α ) can be transformed into other grey models through parameter setting changes, so the new model has strong adaptability. The FDGM (1, 1, t α ) is more reliable and effective than the other six compared grey models. From 2018 to 2022, the total energy consumption levels of civil buildings, urban civil buildings, and civil buildings specifically in Beijing will exhibit steady upward trends, with an average annual growth rate of 2.61%, 1.92%, and 0.78%, respectively.

Cloud-edge collaboration based transferring prediction of building energy consumption

2021 ◽  
pp. 1-13
Author(s):  
Jinping Zhang ◽  
Xiaoping Deng ◽  
Chengdong Li ◽  
Guanqun Su ◽  
Yulong Yu
Keyword(s):  
Energy Consumption ◽  
Prediction Model ◽  
Prediction Models ◽  
Historical Data ◽  
Computational Cost ◽  
Building Energy ◽  
Fine Tuning ◽  
Observation Data ◽  
Building Energy Consumption ◽  
Model Library

Building energy consumption (BEC) prediction often requires constructing a corresponding model for each building based historical data. However, the constructed model for one building is difficult to be reused in other buildings. Recent approaches have shown that cloud-edge collaboration architecture is promising in realizing model reuse. How to complete the reuse of cloud energy consumption prediction models at the edge and reduce the computational cost of the model training is one of the key issues that need to be solved. To handle the above problems, a cloud-edge collaboration based transferring prediction method for BEC is proposed in this paper. Specifically, a model library stored prediction models for different types of buildings is constructed based the historical energy consumption data and the long short-term memory (LSTM) network in the cloud firstly; then, the similarity measurement strategies of time series with different granularity are given, and the model to be transferred from the model library is matched by analyzing the similarity between observation data uploaded to the cloud and the historical data collected in the cloud; finally, the fine-tuning strategy of the matching prediction model is given, and this model is fine-tuned at the edge to achieve its reuse in concrete application scenarios. Experiments on practical datasets reveal that compared with the prediction model which doesn’t utilize the transfer strategy, the proposed prediction model has better performance according to MAE and RMSE. Experimental results also confirm that the proposed method effectively reduces the computational cost of the network training at the edge.

scholarly journals Research on the Combined Prediction Model of Residential Building Energy Consumption Based on Random Forest and BP Neural Network

Geofluids ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xuenan Zhang ◽  
Jinxin Zhang ◽  
Jinhua Zhang ◽  
YuChuan Zhang
Keyword(s):  
Neural Network ◽  
Energy Efficiency ◽  
Energy Consumption ◽  
Random Forest ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Residential Building ◽  
Building Energy ◽  
Building Energy Consumption ◽  
Data Set

As the energy consumption of residential building takes a large part in the building energy consumption, it is important to promote energy efficiency in residential building for green development. In order to evaluate the energy consumption of residential building more effectively, this paper proposes a combined prediction model based on random forest and BP neural network (RF-BPNN). To verify the prediction effect of the RF-BPNN combined model, experiments were performed by using the energy efficiency data set in the UCI database, and the model was evaluated with five indicators: mean absolute error, root mean square deviation, mean absolute percentage error, correlation coefficient, and coincidence index. Compared with the random forest, BP neural network model, and other existing models, respectively, it is proven by the experimental results that the RF-BPNN model possesses higher prediction accuracy and better stability.

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