CN105528650A - Machine room temperature and humidity prediction method based on principle component analysis and BP neural network - Google Patents
Machine room temperature and humidity prediction method based on principle component analysis and BP neural network Download PDFInfo
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Abstract
The invention provides a machine room temperature and humidity prediction method based on principle component analysis and a BP neural network, solving the problem of poor temperature and humidity monitor effects due to failure in predicting change trends of temperature and humidity in a machine room in the prior art. The prediction method comprises following steps: influencing factors which can affect the temperature and humidity of a machine room are analyzed and determined; main factors which affect the temperature and humidity change are analyzed and selected by using the principle component analysis (PCA) method; then regression prediction is performed to historical data of the main factors by using the BP neural network to obtain accurate temperature and humidity prediction values. By use of the prediction method, the change trend of temperature and humidity of a machine room can be accurately predicted so that temperature and humidity configuration adjustments can be accurately made by a monitor system, and the temperature and humidity monitor effects are improved.
Description
Technical field
The present invention relates to a kind of machine room humiture trend method, particularly relate to a kind of machine room humiture Forecasting Methodology based on principal component analysis (PCA) and BP neural network, belong to field of electrical equipment.
Background technology
Machine room unified monitoring is the main project of Jiangsu company 12 informatization.Each machine room location distribution scope of the whole province is wide, and machine room inner structure situation is different, has set up the monitoring of machine room humiture at present, realizes the data acquisition of machine room humiture, alarm etc.But configure about the threshold value of machine room humiture, monitor staff cannot accomplish dynamically to make according to different situations to adjust accurately.Diverse geographic location, Various Seasonal, the not square one of machine room humiture even caused because of the difference of calculator room equipment performance, the Configuration Values of corresponding machine room humiture monitoring is also distinguishing.Therefore need supervisory system according to machine room actual conditions, current machine room humiture can be doped, help the configuration that monitor staff adjusts the humiture of machine room accurately.
The present invention mainly studies the variation tendency of machine room humiture, by analysis of history data, in conjunction with the externality factor that machine room is different, draw the key factor affecting machine room humiture change, calculate the humiture variation tendency of machine room in advance, and then make humiture accurately by supervisory system and configure adjustment, reduce electricity to reach, improve air-conditioning serviceable life and promote temperature and humidity monitor effect.
Summary of the invention
In order to overcome the deficiencies in the prior art, the invention provides a kind of machine room humiture Forecasting Methodology based on principal component analysis (PCA) and BP neural network, solve in prior art because of unpredictable go out in machine room humiture variation tendency cause the technical matters of temperature and humidity monitor weak effect.
The technical solution adopted for the present invention to solve the technical problems is: a kind of machine room humiture Forecasting Methodology based on principal component analysis (PCA) and BP neural network, comprises step:
Step one, analyzes and chooses the key factor affecting machine room humiture change;
Step 2, principal component analysis (PCA), analyzes the main one-tenth factor affecting humiture change; Comprise:
1) choose the key factor sample that many groups affect machine room humiture change, set up the linear equation of factor of influence;
2) standardization is carried out to the matrix of described linear equation;
3) correlation matrix of normalized matrix;
4) by correlation matrix, the contribution rate of each factor of influence, contribution rate of accumulative total is calculated;
5) the main one-tenth factor is chosen according to contribution rate of accumulative total;
Step 3, sets up BP neural network model and trains, and using obtained main one-tenth factor historical data as input variable, machine room humiture is as output variable;
Step 4, by the BP neural network after the main one-tenth factor real data input training in moment to be predicted, dopes humiture.
The described key factor affecting machine room humiture change comprises weather condition, machine room floor, location & layout, equipment cooling, power, indoor and outdoor temperature difference and air-conditioning maintenance situation.
5 of described step 2) in, choose the factor of contribution rate of accumulative total between 80%-90% as the main one-tenth factor.
The invention has the beneficial effects as follows,
1, the present invention can go out the variation tendency of humiture in machine room by Accurate Prediction, during the system configuration cycle, adjustment System is arranged about the configuration of machine room humiture threshold values and the humiture of air conditioner in machine room intelligently, alleviate the configuration error problem that may cause because of experience problem of monitor staff, and can accomplish that the corresponding machine room humiture of adjustment according to intelligence such as geographic position, weather condition, plant capacity, seasonal climates monitors the setting of configuration and air conditioner in machine room, electricity can be reduced, promote air-conditioning serviceable life and promote temperature and humidity monitor effect.
2, principal component analysis (PCA) chooses the factor of contribution rate of accumulative total between 80%-90% as the main one-tenth factor, namely as the main affecting factors of machine room humiture change, reduce the dimension of influence factor, predict according to the change of the main one-tenth factor during prediction machine room humiture, decrease workload, improve forecasting efficiency.
Accompanying drawing explanation
Fig. 1 is machine room humiture Forecasting Methodology process flow diagram of the present invention.
Embodiment
Below in conjunction with accompanying drawing, the invention will be further described.Following examples only for technical scheme of the present invention is clearly described, and can not limit the scope of the invention with this.
As shown in Figure 1, a kind of humiture Forecasting Methodology based on principal component analysis (PCA) and BP neural network, comprises the following steps:
Step one, analyzes and chooses the key factor affecting machine room humiture change;
The factor of humiture in machine room that affects comprises all key elements such as the performance power consumption of equipment in weather condition, the geographic position of machine room, machine room, the present embodiment is analyzed according to extracts machine room humiture supervisory system upper part machine room humiture historical information situation, as shown in table 1, choose 5 groups of samples, from historical data analysis, the present invention chooses 7 major influence factors of humiture change in machine room, comprises weather condition, machine room floor, location & layout, equipment cooling, power, indoor and outdoor temperature difference and air-conditioning maintenance situation.
Table 1 machine room humiture influence factor sample
Step 2, principal component analysis (PCA) PCA, analyzes the main one-tenth factor affecting humiture change, comprises step;
1) linear equation of the key factor affecting humiture change is set up;
Can be set up the data matrix X (5 × 7) of 5 groups of samples, 7 key factors by table 1,7 key influence factors are designated as X1, X2 respectively ... X7, note X=(X1, X2 ..., X7)
t.
2) standardization is carried out to the matrix of described linear equation;
X matrix is carried out standardization, be designated as Y=(Y1, Y2 ..., Y7)
t, the matrix Y data record after standardization is as shown in table 2:
Data after table 2 standardization
3) correlation matrix of normalized matrix;
The correlation matrix of normalized matrix Y, be designated as Z=(Z1, Z2 ..., Z7)
t, data are as shown in table 3:
The data of the correlation matrix of table 3 normalized matrix
4) by correlation matrix, the contribution rate of each factor of influence, contribution rate of accumulative total is determined;
Contribution rate refers to eigenwert accounting, and contribution rate is larger, and represent that the information that this major component comprises is more, generally maximum for contribution rate is called first principal component, what contribution rate was second largest is called Second principal component, by that analogy.The contribution rate sum (contribution rate of accumulative total) of a front n major component is referred to as Z1, Z2, Z3 ..., Zn contribution rate of accumulative total, i.e. current major component and principal component contributor rate sum before.So first contribution rate of accumulative total is first principal component contribution; Second contribution rate of accumulative total is first principal component contribution rate and Second principal component, contribution rate sum; 3rd contribution rate of accumulative total is first principal component contribution rate, Second principal component, contribution rate and the 3rd principal component contributor rate three sum, by that analogy.According to correlation matrix Z, calculate the eigenwert of each factor of influence, and then calculate contribution rate and contribution rate of accumulative total, in the present embodiment, the eigenwert of 7 factors of influence, contribution rate and contribution rate of accumulative total are as shown in table 4:
The eigenwert of table 4 factor of influence, contribution rate and contribution rate of accumulative total
5) the main one-tenth factor is found out according to contribution rate of accumulative total;
Usually choose contribution rate of accumulative total and reach front n major component of predetermined threshold value to replace original factor, make original factor reach the object of dimensionality reduction.The present invention choose contribution rate of accumulative total between 80%-90% before n the major component factor as the main one-tenth factor, as shown in Table 4: front 2 Main composition factor 1 weather conditions and factor 2 machine room floor can be chosen to replace original factor X1, X2, X7, the dimension reducing original influence factor can be reached, reduce the workload of prediction humiture, raise the efficiency.
Step 3, sets up BP neural network model and trains, and using obtained main one-tenth factor historical data as input variable, machine room humiture is as output variable;
BP (BackPropagation) neural network is a kind of Multi-layered Feedforward Networks by Back Propagation Algorithm training, is one of current most widely used neural network model.BP network can learn and store the mapping relations of a large amount of input-output patterns, uses the learning rules of method of steepest descent, is constantly adjusted the weights and bias of network, make the error sum of squares of network minimum by backpropagation.BP neural network model comprises its input/output model, action function model, error calculating and self learning model.
For machine room humiture model, build BP neural network, the main one-tenth factor historical data obtained in step 2, as input vector, using corresponding machine room humiture as output vector, obtains training sample; Utilize this training sample to train BP neural network, obtain the BP neural network after training.
Step 4, by the BP neural network after the main one-tenth factor real data input training in moment to be predicted, dopes humiture.
The real data sample in the main one-tenth factor moment to be predicted chosen in step 2 is inputted BP neural network, BP neural network is by self study process, weight setting between adjustment connection lower level node and upper layer node and error correction, until within error to limited field, dope humiture numerical value.
The present invention utilizes the machine room humiture Forecasting Methodology based on principal component analysis (PCA) and BP neural network, accurately can calculate the humiture variation tendency in machine room in advance, according to this predicted value, adjustment System is arranged about the configuration of machine room humiture threshold values and the humiture of air conditioner in machine room intelligently, humiture in accurate measurements machine room can be reached, alleviate the configuration error problem that may cause because of experience problem of monitor staff, reduce electricity, promote air-conditioning serviceable life and promote temperature and humidity monitor effect.
Above-described embodiment is the present invention's preferably embodiment; but embodiments of the present invention are not restricted to the described embodiments; change, the modification done under other any does not deviate from Spirit Essence of the present invention and principle, substitute, combine, simplify; all should be the substitute mode of equivalence, be included within protection scope of the present invention.
Claims (3)
1., based on the machine room humiture Forecasting Methodology of principal component analysis (PCA) and BP neural network, it is characterized in that, comprise step:
Step one, analyzes and chooses the key factor affecting machine room humiture change;
Step 2, principal component analysis (PCA), analyzes the main one-tenth factor affecting humiture change; Comprise:
1) choose the key factor sample that many groups affect machine room humiture change, set up the linear equation of factor of influence;
2) standardization is carried out to the matrix of described linear equation;
3) correlation matrix of normalized matrix;
4) by correlation matrix, the contribution rate of each factor of influence, contribution rate of accumulative total is calculated;
5) the main one-tenth factor is chosen according to contribution rate of accumulative total;
Step 3, sets up BP neural network model and trains, and using obtained main one-tenth factor historical data as input variable, machine room humiture is as output variable;
Step 4, by the BP neural network after the main one-tenth factor real data input training in moment to be predicted, dopes humiture.
2. the machine room humiture Forecasting Methodology based on principal component analysis (PCA) and BP neural network according to claim 1, it is characterized in that, the described key factor affecting machine room humiture change comprises weather condition, machine room floor, location & layout, equipment cooling, power, indoor and outdoor temperature difference and air-conditioning maintenance situation.
3. the machine room humiture Forecasting Methodology based on principal component analysis (PCA) and BP neural network according to claim 1, is characterized in that, 5 of described step 2) in, choose the factor of contribution rate of accumulative total between 80%-90% as the main one-tenth factor.
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| CN105892387A (en) * | 2016-05-30 | 2016-08-24 | 国网江苏省电力公司信息通信分公司 | Cross-platform multi-point data acquisition MPCA (multi-way principal component analysis) model-based computer room hidden danger automatic reporting device and method |
| CN106228010A (en) * | 2016-07-22 | 2016-12-14 | 上海海洋大学 | A kind of North Pacific squid resource magnitude of recruitment Forecasting Methodology |
| CN106709169A (en) * | 2016-12-12 | 2017-05-24 | 南京富岛信息工程有限公司 | Property estimation method for crude oil processing process |
| CN106839288A (en) * | 2017-01-13 | 2017-06-13 | 赵建杰 | A kind of control method of computer floor air-conditioning system |
| CN107844868A (en) * | 2017-11-27 | 2018-03-27 | 中山路得斯空调有限公司 | A kind of load prediction system for merging PLC technology and pivot analysis BP neural network |
| CN110907319A (en) * | 2019-11-07 | 2020-03-24 | 中国科学院遥感与数字地球研究所 | Attribution analysis method for near-surface fine particulate matters |
| CN111221880A (en) * | 2020-04-23 | 2020-06-02 | 北京瑞莱智慧科技有限公司 | Feature combination method, device, medium, and electronic apparatus |
| CN111416744A (en) * | 2020-03-24 | 2020-07-14 | 北京百度网讯科技有限公司 | Method and device for monitoring and alarming on Internet line |
| CN113552855A (en) * | 2021-07-23 | 2021-10-26 | 重庆英科铸数网络科技有限公司 | Industrial equipment dynamic threshold setting method and device, electronic equipment and storage medium |
| CN114021449A (en) * | 2021-10-29 | 2022-02-08 | 江苏方天电力技术有限公司 | Prediction method for coal mill safety evaluation |
| CN114967804A (en) * | 2022-07-11 | 2022-08-30 | 国网江苏省电力有限公司泰州供电分公司 | Power distribution room temperature and humidity regulation and control method |
| CN116128382A (en) * | 2023-04-14 | 2023-05-16 | 深圳市宇芯数码技术有限公司 | Chip quality detection system and method |
| CN117040137A (en) * | 2023-10-09 | 2023-11-10 | 国网山东省电力公司聊城供电公司 | Ring main unit temperature rise early warning method, system, terminal and medium based on multi-source data |
| CN117632664A (en) * | 2024-01-11 | 2024-03-01 | 深圳市柏特瑞电子有限公司 | Machine room equipment monitoring method and system based on automatic comparison |
| CN117668531A (en) * | 2023-12-07 | 2024-03-08 | 无锡中科光电技术有限公司 | EMMD-BP neural network atmospheric pollutant forecasting method based on principal component analysis |
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Application publication date: 20160427 |