CN102563808A - Automatic control method of indoor environment comfort level - Google Patents
Automatic control method of indoor environment comfort level Download PDFInfo
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Abstract
The invention relates to the technical field of indoor environment regulation, and provides an automatic control method of the indoor environment comfort level. According to the method, a three-layer BP (back-propagation) neural network is selected as a model. The method comprises the following steps of: 1), collecting the current actual environment data as sample data; 2), building the model by the sample data based on the three-layer feed-forward BP neural network; 3), setting an optimal value and a range of an SET* index by using model-based prediction and control; 4), controlling an air-conditioning system, combining with the optical value and the range of the SET* index to process the current data collected in real time, and generating a signal for controlling the air-conditioning system, so as to realize real-time control of an environment control device. According to the automatic control method, the defect of complicated iterative operation in the traditional model is overcome; the convergence rate is increased by an improved L-M algorithm, and the prediction mode has high validity and small error.
Description
Technical field
The invention belongs to indoor environment regulation technology field, particularly a kind of autocontrol method of indoor environment comfort level.
Background technology
The modern society people spend indoor the time more than 85%, and the quality of indoor environment all has direct influence to physical and mental health, comfort level and the operating efficiency of human body, so people are also more and more high for environment requirement.The SET* index (standard effective temperature (SET)) of extensive use ASHRAE standard is the basis with two nodal analysis method theories of human body temperature adjusting at present; Physical process analysis at people's body heat transferring draws comprehensive hot comfort index; The heat reflection of human body under the prediction thermal environment, weigh with the control room in environmental degree of comfort.
The SET* index is the hot comfort index according to the physiological reaction model, and influence factor mainly comprises air themperature, air humidity, air velocity and radiation temperature.These environmental factors are not variablees fully independently, but interact, inseparable.Have non-linear, the time complicated characteristic such as become.The prior art mainly computing that iterates of the influences such as temperature and humidity through parameters on human skins such as air themperature, humidity, wind speed, mean radiant temperatures is confirmed the SET* index; Calculation of complex; Therefore can not be real-time confirm be difficult to satisfy the air-conditioning system requirement of control in real time.It all is that the value within the specific limits of supposing sample data obtains that a lot of researchs are arranged, but the sample data that actual measurement obtains more helps the training to the SET* index model.
Summary of the invention
The objective of the invention is to overcome the shortcoming and defect of prior art, a kind of weighed energy loss and indoor comfort degree are provided, to reach a kind of method that best optimal policy realizes automatic indoor environmental condition control.
The object of the invention is realized through following technical proposals:
A kind of method for establishing model based on the indoor environment comfort level may further comprise the steps:
1) environmental data of gathering current reality is as sample data;
This part mainly comprises the data acquisition of outdoor temperature, relative humidity, mean radiant temperature and wind speed envirment factor, indoor temperature, relative humidity, mean radiant temperature, wind speed envirment factor data acquisition.Corresponding SET* index is then used traditional iterative algorithm and is calculated.
2) utilize sample data to set up based on three layers of feed-forward type BP neural network model;
Three layers of feed-forward type BP network that said model is set up comprise input layer, hidden layer and three parts of output layer.The input of model comprises control input quantity and disturbance input quantity, and the control input quantity is indoor environmental factor, comprises indoor temperature, relative humidity, mean radiant temperature, wind speed; The disturbance input quantity is outdoor environmental factor, comprises outdoor temperature, relative humidity, mean radiant temperature, wind speed.Output is the SET* desired value.The node of hidden layer is elected 6 as.
3) employing is set SET* index optimal value and scope based on the PREDICTIVE CONTROL of model;
Model Predictive Control mainly comprises process model building, definition target function, optimizes target function and finite time-domain rolling calculating.Process model building is the behavior that utilizes the following output of the data prediction signal of input signal and output signal; Optimization in the PREDICTIVE CONTROL is the rolling optimization of a kind of limited period, and it is not that an off-line carries out, but online repeatedly carrying out, promptly so-called rolling optimization.In each sampling instant, optimize index and only relate to, and to next sampling instant, this optimization is passed forward during the period simultaneously from this following limited constantly time.
4) air-conditioning system control: combine SET* index optimal value and scope that the current data that collects is in real time handled, produce the signal of control air-conditioning system, realize the automatic control of indoor comfort degree.
The data that part of data acquisition will collect in real time are through after processing, storage, demonstration and the management; Model is according to each item data analysis control algolithm; Produce one and optimize comfort level and the minimum control signal of energy consumption; This control signal is sent to slave computer, and slave computer selects for use single-chip microcomputer to control air-conditioning system and fan system, realizes the control of indoor comfort degree.
The present invention selects the SET* index to replace the hot comfort of human body to be the control target; Set up air themperature, humidity, wind speed and mean radiant temperature environmental variance functional relation to SET*; Thereby confirm the model structure of the indoor environment factor; And utilize least square method of recursion that the indoor environment temperature system under gravity-flow ventilation condition and the air-conditioning effect is carried out the identification of model parameter, set up the reliable Mathematical Modeling of reaction indoor environment, thereby reach suitable comfort level and minimum energy consumption.
The relative prior art of the present invention has fast convergence rate, and forecast model validity is high, error is little, can the realization system in real time with the advantage of control automatically.
The specific embodiment
Below in conjunction with embodiment enforcement of the present invention is further described, but enforcement of the present invention is not limited thereto.
A kind of method for establishing model based on the indoor environment comfort level may further comprise the steps:
1) environmental data of gathering current reality is as sample data;
2) utilize sample data to set up based on three layers of feed-forward type BP neural network model;
3) employing is set SET* index optimal value and scope based on the PREDICTIVE CONTROL of model;
4) air-conditioning system control: combine SET* index optimal value and scope that the current data that collects is in real time handled, produce the signal of control air-conditioning system, realize the automatic control of indoor comfort degree.
The environmental data of said step 1) comprises indoor temperature, relative humidity, mean radiant temperature and wind speed, also comprises outdoor temperature, relative humidity, mean radiant temperature and wind speed and current SET* desired value.
Said step 2) BP neutral net comprises input layer, hidden layer and output layer.Input layer comprises control input quantity and disturbance input quantity; The control input quantity is indoor temperature, relative humidity, mean radiant temperature and wind speed; The disturbance input quantity is outdoor temperature, relative humidity, mean radiant temperature and wind speed.Output layer is the SET* index.The node of hidden layer is 6.
Adopt the least square method of recursion algorithm to the BP neutral net is trained, output layer and corresponding input layer are compared, reach requirement, confirm the weights and the threshold value of each layer up to the mean square error of network training.
Process model building is following behavior of data prediction output signal that utilizes input signal and output signal; Confirm prediction time domain Np; Define corresponding performance function through output signal, reference signal and control signal then,, confirm control time domain Nu in order control signal to be applied to the PREDICTIVE CONTROL process; Limit and optimize equation through control signal and output variable being carried out some; The control signal that calculates through equation is applied in the middle of the real process, and in next step, all algorithms repeat to roll and calculate.
Optimization in the PREDICTIVE CONTROL is the rolling optimization of a kind of limited period, and it is not that an off-line carries out, but online repeatedly carrying out, promptly so-called rolling optimization.In each sampling instant, optimize index and only relate to, and to next sampling instant, this optimization is passed forward during the period simultaneously from this following limited constantly time.Make computation optimization more accurate like this.So PREDICTIVE CONTROL is not to adopt the optimization index identical to the overall situation, but each the time be carved with an optimization aim function with respect to this moment.Relative form at different time optimization object functions is identical, but its absolute form is inequality, because included time zone is different.
The indoor environment system is non-linear a, close coupling, strongly disturbing dynamical system; When always being in, its input and output become state; Derive based on thermal balance and to analyze the Mathematical Modeling obtain, be difficult to confirm model parameter, be difficult to be applied to the requirement of indoor environmental condition control.The experiment modeling is the system's inputoutput data according to experiment measuring; It is carried out analyzing and processing obtain reflecting system model static state and dynamic characteristic; With some linearization technique derivation structure of models, carry out match with a model then, model parameter is carried out identification.
The indoor environment hot comfort depends mainly on the indoor climate condition, the heat exchange of human body and environment.Indoor environment is regarded dynamic many single-input single-output system (SISO system)s (MIMO) as; Comprise the heat exchange of human body and environment; Indoor air temperature, wind speed, relative humidity, mean radiant temperature, outdoor temperature, humidity, wind speed, intensity of illumination or mean radiant temperature or the like.
Owing to be difficult to accurately measure the energy of human body and environment heat exchange; Therefore only when calculating human comfort index S ET*, consider the variable relevant with human body; Characteristics to the indoor environment factor; With the temperature is the main factor of environment, sets up the model of the room main environment factor of gravity-flow ventilation and air-conditioning system effect.
Model structure identification subject matter is confirming of rank, comprises model structure parameter definite of each polynomial order and pure hysteresis in the indoor environment factor model.According to the data that measure, selected different orders carry out parameter Estimation, obtain the model equation of different orders, utilize the statistical property of least-squares estimation to confirm the true order of model.
Claims (9)
1. method for establishing model based on the indoor environment comfort level is characterized in that may further comprise the steps:
1) environmental data of gathering current reality is as sample data;
2) utilize sample data to set up based on three layers of feed-forward type BP neural network model;
3) adopt the PREDICTIVE CONTROL (MBPC) based on above-mentioned model to be applied in the middle of the indoor environmental condition control system, set comfort level SET* index optimal value and SET* value scope respectively;
4) air-conditioning system control: combine SET* index optimal value and setting SET* value scope that the current data that collects is in real time handled, produce the signal of control air-conditioning system, realize the automatic control of indoor comfort degree.
2. the method for establishing model based on the indoor environment comfort level according to claim 1 is characterized in that the environmental data of said step 1) comprises indoor temperature, relative humidity, mean radiant temperature and wind speed.
3. the method for establishing model based on the indoor environment comfort level according to claim 2 is characterized in that said environmental data also comprises outdoor temperature, relative humidity, mean radiant temperature and wind speed.
4. the method for establishing model based on the indoor environment comfort level according to claim 3; It is characterized in that said sample data also comprises based on current indoor temperature, relative humidity, mean radiant temperature and wind speed, the current SET* desired value of utilizing traditional iterative algorithm to calculate.
5. the method for establishing model based on the indoor environment comfort level according to claim 1 is characterized in that said step 2) the BP neutral net comprise input layer, hidden layer and output layer.
6. the method for establishing model based on the indoor environment comfort level according to claim 5 is characterized in that said input layer comprises control input quantity and disturbance input quantity; The control input quantity is indoor temperature, relative humidity, mean radiant temperature and wind speed; The disturbance input quantity is outdoor temperature, relative humidity, mean radiant temperature and wind speed.
7. the method for establishing model based on the indoor environment comfort level according to claim 6 is characterized in that said output layer is the SET* index.
8. the method for establishing model based on the indoor environment comfort level according to claim 5, the node that it is characterized in that said hidden layer is 6.
9. according to the described method for establishing model of one of claim 1 ~ 8 based on the indoor environment comfort level; It is characterized in that adopting the least square method of recursion algorithm that the BP neutral net is trained; Output layer and corresponding input layer are compared; Mean square error up to network training reaches requirement, confirms the weights and the threshold value of each layer.
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| CN103196206A (en) * | 2013-04-12 | 2013-07-10 | 南京物联传感技术有限公司 | Indoor simulation method for generating natural air |
| CN103207564A (en) * | 2012-12-03 | 2013-07-17 | 北京华亿九州科技有限公司 | Sliding-mode variable-structure model-based method and system for energy-saving composite analysis for buildings |
| CN104075402A (en) * | 2014-06-19 | 2014-10-01 | 珠海格力电器股份有限公司 | Intelligent air conditioner control method and system |
| CN104359192A (en) * | 2014-11-19 | 2015-02-18 | 山东建筑大学 | Data-based personalized control system and method for energy conservation and comfort of indoor environment |
| CN104490371A (en) * | 2014-12-30 | 2015-04-08 | 天津大学 | Heat comfort detection method based on physiological parameters of human body |
| CN106530649A (en) * | 2016-10-25 | 2017-03-22 | 北京物资学院 | Early warning model establishment method and device and early warning method and system |
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| CN108260332A (en) * | 2018-01-26 | 2018-07-06 | 江苏泽镔信息科技有限公司 | Thermal management control device and method |
| CN108426349A (en) * | 2018-02-28 | 2018-08-21 | 天津大学 | Air-conditioning personalized health management method based on complex network and image recognition |
| CN108459886A (en) * | 2017-12-18 | 2018-08-28 | 珠海格力电器股份有限公司 | Method and device for determining indoor environment state |
| CN108763730A (en) * | 2018-05-24 | 2018-11-06 | 浙江农林大学 | Shade tree screening technique, system, terminal and medium based on thermal comfort index |
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| CN112947637A (en) * | 2021-01-29 | 2021-06-11 | 安徽佳美瑞物联科技有限公司 | Office environment intelligent regulation system |
| CN113485498A (en) * | 2021-07-19 | 2021-10-08 | 北京工业大学 | Indoor environment comfort level adjusting method and system based on deep learning |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1414313A (en) * | 2002-12-05 | 2003-04-30 | 上海交通大学 | Individuality air conditioner |
| CN1584433A (en) * | 2004-06-04 | 2005-02-23 | 广东科龙电器股份有限公司 | Noise source identifying method for air-conditioner based on nervous network |
| CN1760596A (en) * | 2004-10-12 | 2006-04-19 | 株式会社日立制作所 | Air conditioning system |
| CN102110243A (en) * | 2009-12-23 | 2011-06-29 | 新奥特（北京）视频技术有限公司 | Method for predicting human comfort |
-
2012
- 2012-01-11 CN CN201210007218.0A patent/CN102563808B/en not_active Expired - Fee Related
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1414313A (en) * | 2002-12-05 | 2003-04-30 | 上海交通大学 | Individuality air conditioner |
| CN1584433A (en) * | 2004-06-04 | 2005-02-23 | 广东科龙电器股份有限公司 | Noise source identifying method for air-conditioner based on nervous network |
| CN1760596A (en) * | 2004-10-12 | 2006-04-19 | 株式会社日立制作所 | Air conditioning system |
| CN102110243A (en) * | 2009-12-23 | 2011-06-29 | 新奥特（北京）视频技术有限公司 | Method for predicting human comfort |
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| CN103207564B (en) * | 2012-12-03 | 2015-09-09 | 北京华亿九州科技有限公司 | Based on building energy-saving compounding analysis method and the system of sliding moding structure model |
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| CN104359192B (en) * | 2014-11-19 | 2016-12-07 | 山东建筑大学 | The energy-conservation comfortable personalized control system of a kind of indoor environment based on data and method |
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