scholarly journals Design and Implementation of Large-Scale Public Building Energy Consumption Monitoring Platform Based on BP Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xiancheng Liu ◽  
Congxiang Tian
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Bp Neural Network ◽  
Large Scale ◽  
Rapid Development ◽  
Building Energy ◽  
Building Energy Consumption ◽  
Fast Training ◽  
Simulation Calculation ◽  
Monitoring Platform

With the rapid development of network technology, people are increasingly dependent on the internet. When BP neural network (BNN) performs simulation calculation, it has the advantages of fast training speed, high accuracy, and strong robustness and is widely used in large-scale public (LSP) building energy consumption (BEC) monitoring platforms (LPB). Therefore, the purpose of this paper to study the energy consumption monitoring platform of large public (LP) buildings is to better monitor the energy consumption of public buildings, so as to supplement or remedy at any time. This article mainly uses the data analysis method and the experimental method to carry on the relevant research and the system test to the BNN. The experimental results show that the monitoring system (MS) platform designed in this paper has real-time performance, and its time consumption is between 2 s and 3 s, and the data accords with theory and reality.

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.

Design of Building Energy Consumption Monitoring Platform

2014 ◽  
Vol 687-691 ◽  
pp. 1066-1070
Author(s):  
Zong Liang Yang ◽  
Yan Min Zhang
Keyword(s):  
Energy Consumption ◽  
Data Collection ◽  
Building Energy ◽  
Dynamic Monitoring ◽  
Building Energy Consumption ◽  
Time Data ◽  
Transmission Modes ◽  
Building Management ◽  
System Data ◽  
Monitoring Platform

The present study analyzed data collection, coding and transmission modes involved in building energy consumption monitoring platform. By analyzing and designing the functions, data collection and management of the system, data analysis and display, data monitoring and remote control, monitoring and prediction and analysis of pipe network, it proposed a design framework of the building energy consumption monitoring platform; and made a verification in the campus building energy consumption monitoring platform, which realized the real-time data collection of energy consumption monitoring and the dynamic monitoring of building energy consumption, and improved the decision level of intelligent building management.

scholarly journals Building Energy Consumption Raw Data Forecasting Using Data Cleaning and Deep Recurrent Neural Networks

Buildings ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. 204 ◽  
Author(s):  
Yang ◽  
Tan ◽  
Santamouris ◽  
Lee
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Energy Consumption ◽  
Missing Data ◽  
Recurrent Neural Networks ◽  
Building Energy ◽  
Building Energy Consumption ◽  
Data Set ◽  
Raw Data ◽  
Deep Recurrent Neural Network

With the rising focus on building energy big data analysis, there lacks a framework for raw data preprocessing to answer the question of how to handle the missing data in the raw data set. This study presents a methodology and framework for building energy consumption raw data forecasting. A case building is used to forecast the energy consumption by using deep recurrent neural networks. Four different methodologies to impute missing data in the raw data set are compared and implemented. The question of sensitivity of gap size and available data percentage on the imputation accuracy was tested. The cleaned data were then used for building energy forecasting. While the existing studies explored only the use of small recurrent networks of 2 layers and less, the question of whether a deep network of more than 2 layers would be performing better for building energy consumption forecasting should be explored. In addition, the problem of overfitting has been cited as a significant problem in using deep networks. In this study, the deep recurrent neural network is then used to explore the use of deeper networks and their regularization in the context of an energy load forecasting task. The results show a mean absolute error of 2.1 can be achieved through the 2*32 gated neural network model. In applying regularization methods to overcome model overfitting, the study found that weights regularization did indeed delay the onset of overfitting.

scholarly journals Neural-Network-Based Building Energy Consumption Prediction with Training Data Generation

Processes ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 731 ◽  
Author(s):  
Sanghyuk Lee ◽  
Jaehoon Cha ◽  
Moon Keun Kim ◽  
Kyeong Soo Kim ◽  
Van Huy Pham ◽  
...  
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
False Negative ◽  
Building Energy ◽  
Training Data ◽  
Superior Performance ◽  
Data Generation ◽  
Building Energy Consumption ◽  
Data Set ◽  
Input Variables

The importance of neural network (NN) modelling is evident from its performance benefits in a myriad of applications, where, unlike conventional techniques, NN modeling provides superior performance without relying on complex filtering and/or time-consuming parameter tuning specific to applications and their wider ranges of conditions. In this paper, we employ NN modelling with training data generation based on sensitivity analysis for the prediction of building energy consumption to improve performance and reliability. Unlike our previous work, where insignificant input variables are successively screened out based on their mean impact values (MIVs) during the training process, we use the receiver operating characteristic (ROC) plot to generate reliable data with a conservative or progressive point of view, which overcomes the issue of data insufficiency of the MIV method: By properly setting boundaries for input variables based on the ROC plot and their statistics, instead of completely screening them out as in the MIV-based method, we can generate new training data that maximize true positive and false negative numbers from the partial data set. Then a NN model is constructed and trained with the generated training data using Levenberg–Marquardt back propagation (LM-BP) to perform electricity prediction for commercial buildings. The performance of the proposed data generation methods is compared with that of the MIV method through experiments, whose results show that data generation using successive and cross pattern provides satisfactory performance, following energy consumption trends with good phase. Among the two options in data generation, i.e., successive and two data combination, the successive option shows lower root mean square error (RMSE) than the combination one by around 400~900 kWh (i.e., 30%~75%).

scholarly journals Research On Public Building Energy Consumption Prediction Method Based On NAR Neural Network Prediction Technology

2019 ◽  
Vol 118 ◽  
pp. 04010
Author(s):  
Heng-jie Li ◽  
Zhen Qiao ◽  
Wei Chen ◽  
Xian-qiang Zeng ◽  
Long Wu
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Prediction Method ◽  
Building Energy ◽  
Building Energy Consumption ◽  
Public Buildings ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

In order to solve the problem of high energy consumption of public buildings and optimize and improve energy conservation of public buildings, we built a building energy consumption prediction model based on NAR neural network prediction technology improved by BP neural network algorithm, and the energy consumption value is predicted. The large public buildings as the research object, the key factors to determine the effect of building energy consumption and collect the corresponding data processing, as the input parameters of neural network prediction public buildings energy consumption value, according to the actual situation will eventually NAR prediction of neural network and BP network prediction method and the comparative analysis the measured data. The results show that NAR neural network can predict the energy consumption of public buildings more accurately than BP neural network under different building parameters.

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