scholarly journals Building Energy Consumption Prediction: An Extreme Deep Learning Approach

Energies ◽  
2017 ◽  
Vol 10 (10) ◽  
pp. 1525 ◽  
Author(s):  
Chengdong Li ◽  
Zixiang Ding ◽  
Dongbin Zhao ◽  
Jianqiang Yi ◽  
Guiqing Zhang
Keyword(s):  
Deep Learning ◽  
Energy Consumption ◽  
Building Energy ◽  
Learning Approach ◽  
Building Energy Consumption ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

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.

Building Energy Consumption Prediction Evaluation Model

2011 ◽  
Vol 280 ◽  
pp. 101-105 ◽  
Author(s):  
Nan Li ◽  
Jing Zhao ◽  
Neng Zhu
Keyword(s):  
Energy Consumption ◽  
Evaluation Model ◽  
Operation Time ◽  
Building Energy ◽  
Building Energy Consumption ◽  
Wide Range ◽  
Good Movement ◽  
Energy Consumption Prediction ◽  
Prediction Evaluation ◽  
Consumption Prediction

Building energy consumption prediction provides the possibility for regulating running condition of equipments in advance. Then the equipments will keep good movement and building energy consumption will reduce obviously. This paper built an energy consumption prediction evaluation model according to Matlab Artificial Neural Network Toolbox. The model was trained and simulated by operation data in June-September of 2008 and 2009 of a case building. Then it can be used to predict this building energy consumption by special data, such as meteorological characteristics of prediction year, operation load, operation time and energy consumption of last year. With more building samples, the model will be used in wide range of building energy consumption prediction.

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