The application of BP neural net real-time data forecasting model used in home environment

Cao, Yong; Tian, Lijun; Zhao, Hongwei

doi:10.1109/cyber.2015.7288164

Cited by 7 publications

(6 citation statements)

References 1 publication

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“…BP neural network is a typical multi-layer forward neural network which has one input layer, multiple implicit layer and one output layer, So we use three-layer BP network to design the model of carbon emissions abnormal data screening [11][12][13][14] .…”

Section: Carbon Emissions Abnormal Data Screening Based On Bp Neural Network Algorithmmentioning

confidence: 99%

Abnormal Data Screening Method of Thermal Power Based on BP Neural Network Algorithm

Li¹,

Wang²,

Hu³

et al. 2019

dtcse

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As China has been the biggest carbon dioxide emissions country in the world and taken electric power industry as a breakthrough to build carbon market, it is necessary to promote the use of carbon continuous emission monitoring system to improve the accuracy of carbon emissions accounting. Carbon emission model is built to convert CO2 flow rate and concentration to mass. The CO2 emissions per gas inflow is a relatively stable value with a random fluctuation and will be affected by gas inflow, which is called per CO2 emissions. Based on the BP neural network algorithm, we take per CO2 emissions, gas inflow, gas turbine load, steam turbine load as input layers to obtain a real value prediction interval of per CO2 emission, and conduct carbon emission abnormal data screening. In the case study, it is proved that the neural network algorithm give an efficient way to screen big CO2 emission abnormal data.

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Section: Carbon Emissions Abnormal Data Screening Based On Bp Neural Network Algorithmmentioning

confidence: 99%

Abnormal Data Screening Method of Thermal Power Based on BP Neural Network Algorithm

Li¹,

Wang²,

Hu³

et al. 2019

dtcse

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show abstract

“…The NAR model is based on the feed forward multilayer perception model with two inputs and one output. Reference [19] presents a BP neural net real-time data forecasting model which is suitable for the home environment by using the correlation between the indoor temperature, outdoor humidity, and indoor humidity. According to problems of petroleum price prediction and the feasibility of petroleum price prediction model, the improved BP model [20] for petroleum price prediction is proposed.…”

Section: Related Workmentioning

confidence: 99%

Design and analysis of a general data evaluation system based on social networks

Mao

et al. 2018

J Wireless Com Network

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A social network is a social structure with a set of social actors and other social interactions between actors. The social network supports a set of methods to explain the structure of whole social entities as well as a variety of theories in these structures. The study of these structures uses social network analysis to explain local and global patterns and examine network dynamics. The related economic data is important for our economic developments. We could analyze the economic data to explore the investments. In this paper, we focus on the design and analysis of automatic management systems to make a decision about the petroleum projects. Petroleum is the lifeblood of economic development. So, we need an automatic and effective management system to make the mining investments of petroleum. Before deciding to invest in the petroleum project, the engineers will describe this project by providing the sufficient economic data. Based on this data, the decision can undertake professional analysis, to determine whether the project is feasible. To automate the manual process and overcome the traditional evaluation method, the BP (back propagation) neural network is applied in a petroleum project economic evaluation. This method opens up a new way for the evaluation of petroleum projects. In this article, we first introduce the application of BP neural network. Then, we introduce the principle and shortcoming of BP neural network. Based on it, we optimize the BP neural network. Secondly, according to the characteristics of petroleum projects, we obtain the economic evaluation indicators about petroleum projects, which are used as the input of the BP neural network. Thirdly, we propose the design of the BP neural network and select the smallest simulation error of the BP neural network as a forecasting model of petroleum projects. In order to improve the accuracy, the labeled training samples are selected for BP neural network training. Finally, based on extensive simulation experimental results, we demonstrate our scheme about the BP neural network of petroleum project evaluation is effective and has overperformance than traditional schemes in terms of convenience and accurate decision-making suggestions.

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“…In recent years, several studies have been conducted to use statistical or machine learning methods to predict indoor PM2.5 concentrations 11–13 . Wei et al 14 reviewed recent studies about machine learning and statistical models for predicting indoor air quality.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, several studies have been conducted to use statistical or machine learning methods to predict indoor PM2.5 concentrations. [11][12][13] Wei et al 14 reviewed recent studies about machine learning and statistical models for predicting indoor air quality. They found that artificial neural networks (ANNs) have shown acceptable performance for the prediction of indoor PM2.5 concentrations.…”

Section: Introductionmentioning

confidence: 99%

A recurrent neural network using historical data to predict time series indoor PM2.5 concentrations for residential buildings

Dai

Liu

2021

Indoor Air

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Due to the severe outdoor PM2.5 pollution in China, many people have installed aircleaning systems in homes. To make the systems run automatically and intelligently, we developed a recurrent neural network (RNN) that uses historical data to predict the future indoor PM2.5 concentration. The RNN architecture includes an autoencoder and a recurrent part. We used data measured in an apartment over the course of an entire year to train and test the RNN. The data include indoor/outdoor PM2.5 concentration, environmental parameters and time of day. By comparing three different input strategies, we found that a strategy employing historical PM2.5 and time of day as inputs performed best. With this strategy, the model can be applied to predict the relatively stable trend of indoor PM2.5 concentration in advance. When the input length is 2 h and the prediction horizon is 30 min, the median prediction error is 8.3 µg/m 3 for the whole test set. For times with indoor PM2.5 concentrations between (20,50] µg/m 3 and (50,100] µg/m 3 , the median prediction error is 8.3 and 9.2 µg/m 3 , respectively. The low prediction error between the ground-truth and predicted values shows that the RNN can predict indoor PM2.5 concentrations with satisfactory performance.

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The application of BP neural net real-time data forecasting model used in home environment

Cited by 7 publications

References 1 publication

Abnormal Data Screening Method of Thermal Power Based on BP Neural Network Algorithm

Abnormal Data Screening Method of Thermal Power Based on BP Neural Network Algorithm

Design and analysis of a general data evaluation system based on social networks

A recurrent neural network using historical data to predict time series indoor PM2.5 concentrations for residential buildings

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