CN110348638A - A kind of low power consuming smart home system of predictable energy regeneration - Google Patents
A kind of low power consuming smart home system of predictable energy regeneration Download PDFInfo
- Publication number
- CN110348638A CN110348638A CN201910633617.XA CN201910633617A CN110348638A CN 110348638 A CN110348638 A CN 110348638A CN 201910633617 A CN201910633617 A CN 201910633617A CN 110348638 A CN110348638 A CN 110348638A
- Authority
- CN
- China
- Prior art keywords
- energy
- module
- data
- sensor
- door
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 230000008929 regeneration Effects 0.000 title claims abstract description 13
- 238000011069 regeneration method Methods 0.000 title claims abstract description 13
- 238000012544 monitoring process Methods 0.000 claims abstract description 35
- 230000007613 environmental effect Effects 0.000 claims abstract description 8
- 238000004146 energy storage Methods 0.000 claims abstract description 4
- YHXISWVBGDMDLQ-UHFFFAOYSA-N moclobemide Chemical compound C1=CC(Cl)=CC=C1C(=O)NCCN1CCOCC1 YHXISWVBGDMDLQ-UHFFFAOYSA-N 0.000 claims description 31
- 238000000354 decomposition reaction Methods 0.000 claims description 24
- 238000005286 illumination Methods 0.000 claims description 24
- 238000004134 energy conservation Methods 0.000 claims description 20
- 239000007789 gas Substances 0.000 claims description 12
- 230000002159 abnormal effect Effects 0.000 claims description 9
- 230000005611 electricity Effects 0.000 claims description 9
- 238000001514 detection method Methods 0.000 claims description 8
- 238000005096 rolling process Methods 0.000 claims description 6
- 239000000779 smoke Substances 0.000 claims description 6
- 238000013459 approach Methods 0.000 claims description 5
- 238000005265 energy consumption Methods 0.000 claims description 5
- 238000000034 method Methods 0.000 claims description 4
- 101100127891 Caenorhabditis elegans let-4 gene Proteins 0.000 claims description 3
- 238000005311 autocorrelation function Methods 0.000 claims description 3
- 230000005540 biological transmission Effects 0.000 claims description 3
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 239000000567 combustion gas Substances 0.000 claims description 3
- 230000000694 effects Effects 0.000 claims description 3
- 238000013210 evaluation model Methods 0.000 claims description 3
- 238000000465 moulding Methods 0.000 claims description 3
- 238000012545 processing Methods 0.000 claims description 3
- 238000012549 training Methods 0.000 claims description 3
- 238000004519 manufacturing process Methods 0.000 abstract 2
- 241000208340 Araliaceae Species 0.000 description 2
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 description 2
- 235000003140 Panax quinquefolius Nutrition 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000001914 filtration Methods 0.000 description 2
- 235000008434 ginseng Nutrition 0.000 description 2
- 230000006698 induction Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B15/00—Systems controlled by a computer
- G05B15/02—Systems controlled by a computer electric
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/26—Pc applications
- G05B2219/2642—Domotique, domestic, home control, automation, smart house
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/02—Preprocessing
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B10/00—Integration of renewable energy sources in buildings
- Y02B10/30—Wind power
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Quality & Reliability (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Automation & Control Theory (AREA)
- General Engineering & Computer Science (AREA)
- Economics (AREA)
- Tourism & Hospitality (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- General Business, Economics & Management (AREA)
- Development Economics (AREA)
- Theoretical Computer Science (AREA)
- Marketing (AREA)
- Entrepreneurship & Innovation (AREA)
- Manufacturing & Machinery (AREA)
- Selective Calling Equipment (AREA)
Abstract
The present invention proposes that a kind of low power consuming smart home system of predictable energy regeneration, the system include intelligent control module, energy forecast module, sensor network module, video monitoring module, execution module, energy module, monitoring module.The intelligent control module includes the control and management to the energy, lighting system, home theater, security alarm, door and window, the energy forecast module is for predicting production of renewable energy resources amount, the sensor network module is used for monitoring environmental data, the video monitoring module is acquired for environmental images, the execution module includes household electrical appliances, audio-visual, door and window etc., the energy module includes energy-storage units, household wind turbine, photovoltaic array, and the monitoring module includes mobile phone, local display, home network server.The present invention is characterized in that: production of energy amount is predicted by energy forecast module, selects corresponding working energy mode, to realize that source benefit maximizes.
Description
Technical field
It a kind of is combined the present invention relates to a kind of field of intelligent monitoring more particularly to based on wavelet transformation and ARIMA model
Energy source monitoring system.
Background technique
Intelligent dwelling is defined as modernization induction house, has various integrated systems, is able to carry out long-range control and phase
The problem of mutual communication, and in recent years, energy conservation becomes the concern of intelligent dwelling, people are intended to establish the building of one zero energy consumption,
But it is not concerned with the concept of energy management.
The present invention proposes that history is generated, made by a kind of low power consuming smart home system of predictable energy regeneration, the system
The energy used is input to energy conservation module output prediction as sample, which removes the disturbance of sample using wavelet decomposition,
Difference integrates rolling average autoregression model ARIMA output prediction, and ARIMA model prediction result is carried out wavelet reconstruction, output
Prediction energy source value selects corresponding working energy mode by intelligent control module, realizes source benefit to intelligent control module
It maximizes.
Summary of the invention
The present invention proposes that history is generated, made by a kind of low power consuming smart home system of predictable energy regeneration, the system
The energy used is input to energy conservation module output prediction as sample, which removes the disturbance of sample using wavelet decomposition,
Difference integrates rolling average autoregression model ARIMA output prediction, and ARIMA model prediction result is carried out wavelet reconstruction, output
Prediction energy source value selects corresponding working energy mode by intelligent control module, realizes source benefit to intelligent control module
It maximizes.
The scheme that the present invention solves the use of its technical problem is: the low power consuming smart home system of the predictable energy regeneration
System, comprising: intelligent control module, energy forecast module, sensor network module, video monitoring module, execution module, energy mould
Block, monitoring module.
The intelligent control module include to the energy, lighting system, home theater, security alarm, door and window control and
Management.Concrete mode, which includes: (1) intelligent control module, sent the energy that the upper time generates, uses to energy conservation module,
The energy needed for predicting by energy conservation module simultaneously sends the required energy of generation in system, is controlled according to input data intelligent
There are four types of possible energy modes for module: 1) providing electric energy from renewable energy and battery；2) it is mentioned from renewable energy source station
It charges the battery for electric energy if there is dump energy；3) electric energy is provided in renewable energy source station, if there is dump energy, and electricity
Pond fills with electricity, then electricity is sold to power grid；4) electric energy is provided from renewable energy, battery and network.(2) lighting system
Ambient brightness is controlled, including detects and changes brightness of illumination under someone and unmanned environment, daytime is utilized by linkage door and window
Natural light reaches the optimal comfort lighting environment of people, and night closes partial illumination.(3) home audio-visual can by mobile phone or computer into
Row control, and the illumination in corresponding room is set in conjunction with suitable environment when opening or closing audio-visual and door and window is adjusted.(4) pacify
Anti- alarm system judges whether combustion gas in environment, smog content etc. exceed threshold value by sensor network and video monitoring data,
Occur informing user when non-database storage face and opening warning mode in video monitoring.(5) by sensor network to room
The monitoring of internal and external environment, as Outdoor Air Quality PM2.5Lower than 2.5 μ g/m3、CO2Gas concentration has lower than 0.09%, general volatile
Machine object TVOC concentration is lower than 0.2mg/m3When door and window, and judge the degree of door and window in conjunction with wind sensor data, together
When, comfortably door and window is also adjusted according to energy conservation and user.
The energy conservation module is based on the combination for combining small echo and difference to integrate rolling average autoregression model ARIMA
Model carries out short-term forecast to the energy is generated.
The sensor network module includes: temperature sensor, humidity sensor, CO2Gas sensor, TVOC sensing
Device, PM2.5Sensor, smoke sensor device, gas security, video sensor, infrared detection sensor, wind sensor, illumination
Sensor, door and window magnetic detector sensor, sensor network are attached using Zigbee protocol.Wherein ZigBee is adopted with household electrical appliances
With starlike connection type, formed using a coordinator and multiple routings and terminal, all data are periodically sent to coordination
Device, coordinator receive data, transmit data to intelligent control module by gateway.
The video monitoring module is made of video acquisition, video processing and conversion, data transmission.Video monitoring is used for
Abnormal conditions are monitored, as old man, baby monitor, stranger swarm into, household appliance is abnormal.
The execution module receives intelligent control module information by Zigbee coordinator, and search device address, which is sent, to be referred to
It enables, including video module, door and window module, lighting module, security module, comprising: (1) open audio-visual instruction and be transmitted to audio-visual set
Standby, room lighting brightness changes, and link lighting module and door and window module, carries out illumination to corresponding room and door and window is adjusted
Section；(2) indoor and outdoor temperature, air quality and wind-force are detected by sensor, data is sent into intelligent control module, by intelligently controlling
Molding block instruction door and window；(3) illumination sensor detection environmental illumination intensity, daytime is by the linkage of control door and window as far as possible using certainly
Illumination sensor data are passed to Intelligent management module by right light, assign instruction to light switch to illumination by Intelligent management module
It is adjusted；(4) it is carried out by detection environmental smoke content, gas content, in conjunction with the abnormal data in video monitoring data tight
It is anxious to close and open corresponding equipment, and open or close door and window and lighting apparatus etc..
The energy module includes energy-storage units, household wind turbine, photovoltaic array.Intelligent control module receives
Energy forecast module data calculates and opens energy consumed by this period household wind turbine, photovoltaic array and generate
Energy comparison, if open household wind turbine, photovoltaic array and power storage.
The monitoring module includes mobile phone, local display, home network server, and wherein home network server will
All data are stored.
The built-up pattern of the energy forecast module based on small echo and ARIMA carried out short-term forecast to the energy, with one day
ARIMA model is established as one group of training sample at interval of energy caused by one hour within 24 hours, by a upper stage energy
Data are input to energy conservation module by intelligent control module, and output predicts intelligent control module, by intelligent control module
Degree energy regeneration module is adjusted, particular content include the following:
(1) wavelet decomposition
Input is that the discrete function of f (w) can be decomposed into the linear combination of four function different scales and position
Wherein approximation coefficient and detail coefficients are expressed as follows:
cj,k=< f (w), φj,k(w) (2) >
Scaling function indicates are as follows:
The present invention carries out 3 layers of decomposition to original yield using sym5 small echo,It is equivalent to a low-pass filter, by f
(w) it is decomposed into a low-frequency approximation component and a high frequency detail component, the algorithm of decomposition has:
Wherein H is low-pass filter, and G is high-pass filter, and each layer is by signal decomposition at low-frequency approximation component and high frequency
Upper one layer of high frequency detail component is decomposed into low-frequency approximation component and high frequency detail component, by three layers points by details coefficients again
F (w) is resolved into four components, respectively three-level approximation component, level-one details coefficients, second level details coefficients, three-level by solution
Details coefficients.
(2) it will be predicted by the four of wavelet decomposition component input ARIMA models, input be subjected to single order and is smoothly filtered
Wave, ARIMA (p, 1, q) model s mathematic(al) representation are as follows:
Wherein p is autoregression model coefficient, and q is the order of moving average model, wtFor observation, θi(j=1,2 ...,
Q), For residual error.Since the activity of main body has periodicity and randomness, so sample data is suitable for
The model prediction of ARIMA is inputted respectively with each component and establishes respective ARIMA model, the specific steps of which are as follows: (2.1) are sharp
Auto-correlation function, the deviation―related function of sample are obtained with Box-Jenkins modelling, statistical property tentatively judges that sequence is adapted to
Types of models；
(2.2) using redization information (AIC to determine the order of model), evaluation model quality is carried out with following formula:
AIC=2K/n-2L/n (7)
Wherein L is log-likelihood, and n is observation number, and K is estimative number of parameters.AIC criterion shows that K is smaller
Model is more succinct, and L more large-sized model is more accurate, calculates p, the optimum value of q further according to AIC criterion.To calculate the ginseng of each model
Number.
(3) wavelet reconstruction
Wavelet reconstruction is the inverse process of wavelet decomposition, and the low frequency details coefficients of decomposition and high frequency detail component are passed through respectively
It is added after low-pass filter and high-pass filter, restructing algorithm are as follows:
Wherein H*、G*For the conjugate matrices of H, G.By the three-level approximation component by the prediction of ARIMA model, level-one details
Data reconstruction after component, second level details coefficients, three-level details coefficients, specifically: first the three-level approximation component of prediction is passed through
Low-pass filter and the three-level details coefficients of prediction pass through the two stage approach component that high-pass filter is predicted, then by prediction
Two stage approach component is close by the level-one that low-pass filter and the second level details coefficients of prediction are predicted by high-pass filter
Like component, the first approximation component of prediction is finally passed through into high-pass filtering by low-pass filter and the level-one details coefficients of prediction
Device obtains prediction data.
(4) output data
Prediction data is output to intelligent control module, intelligent control module receives data, and prediction data, which is greater than, opens again
Then opening device adds the actual value prediction next stage of a period to next period for raw energy source device energy consumption
Energy output.
Detailed description of the invention
It, below will be to institute in embodiment or description of the prior art for the clearer technology for illustrating the existing example of the present invention
Attached drawing to be used is needed to be briefly described.
Fig. 1 is to show that column example shows a kind of schematic diagram of the low power consuming smart home system of predictable energy regeneration
Fig. 2 is to show that column example shows a kind of energy forecast of the low power consuming smart home system of predictable energy regeneration
Schematic diagram
Specific embodiment
Here is that the specific embodiment of the invention is described in conjunction with attached drawing, to facilitate those skilled in the art can be more
The good understanding present invention.
Intelligent dwelling is defined as modernization induction house, has various integrated systems, is able to carry out long-range control and phase
The problem of mutual communication, and in recent years, energy conservation becomes the concern of intelligent dwelling, people are intended to establish the building of one zero energy consumption,
But it is not concerned with the concept of energy management.
The present invention proposes a kind of low power consuming smart home system of predictable energy regeneration, the work that history is generated, is used
It is input to energy conservation module for sample, using the disturbance of wavelet decomposition removal sample, difference integrates rolling average autoregression mould
ARIMA model prediction result is carried out wavelet reconstruction by type ARIMA prediction output, output prediction energy value to intelligent control module,
Corresponding working energy mode is selected by intelligent control module, realizes the maximization of source benefit.
In order to make it easy to understand, to a kind of low power consuming smart home system of predictable energy regeneration of implementation column of the invention into
Row detailed description:
Shown in Figure 1, the present invention includes: intelligent control module, energy forecast module, sensor network module, video
Monitoring module, execution module, energy module, monitoring module.
The intelligent control module include to the energy, lighting system, home theater, security alarm, door and window control and
Management.Concrete mode, which includes: (1) intelligent control module, sent the energy that the upper time generates, uses to energy conservation module,
The energy needed for predicting by energy conservation module simultaneously will predict that the required energy is sent in system, be controlled according to input data intelligent
There are four types of possible energy modes for module: 1) providing electric energy from renewable energy and battery；2) it is mentioned from renewable energy source station
It charges the battery for electric energy if there is dump energy；3) electric energy is provided in renewable energy source station, if there is dump energy, and electricity
Pond fills with electricity, then electricity is sold to power grid；4) electric energy is provided from renewable energy, battery and network.(2) lighting system
Ambient brightness is controlled, including detects and changes brightness of illumination under someone and unmanned environment, daytime is utilized by linkage door and window
Natural light reaches the optimal comfort lighting environment of people, and night closes partial illumination.(3) home audio-visual can by mobile phone or computer into
Row control, and the illumination in corresponding room is set in conjunction with suitable environment when opening or closing audio-visual and door and window is adjusted.(4) pacify
Anti- alarm system judges whether combustion gas in environment, smog content etc. exceed threshold value by sensor network and video monitoring data,
Occur informing user when non-database storage face and opening warning mode in video monitoring.(5) by sensor network to room
The monitoring of internal and external environment, as Outdoor Air Quality PM2.5Lower than 2.5 μ g/m3、CO2Gas concentration has lower than 0.09%, general volatile
Machine object TVOC concentration is lower than 0.2mg/m3When door and window, and judge the degree of door and window in conjunction with wind sensor data, together
When, comfortably door and window is also adjusted according to energy conservation and user.
The energy conservation module is based on the combination for combining small echo and difference to integrate rolling average autoregression model ARIMA
Model carries out short-term forecast to the energy is generated.
1. sensor network module described in includes: temperature sensor, humidity sensor, CO2Gas sensor, TVOC are passed
Sensor, PM2.5Sensor, smoke sensor device, gas security, video sensor, infrared detection sensor, wind sensor, photograph
Sensor, door and window magnetic detector sensor are spent, sensor network is attached using Zigbee protocol.Wherein ZigBee and household electrical appliances
Using starlike connection type, formed using a coordinator and multiple routings and terminal, all data are periodically sent to association
Device is adjusted, coordinator receives data, transmits data to intelligent control module by gateway.
The video monitoring module is made of video acquisition, video processing and conversion, data transmission.Video monitoring is used for
Abnormal conditions are monitored, as old man, baby monitor, stranger swarm into, household appliance is abnormal.
The execution module receives intelligent control module information by Zigbee coordinator, and search device address, which is sent, to be referred to
It enables, including video module, door and window module, lighting module, security module, comprising: (1) open audio-visual instruction and be transmitted to audio-visual set
Standby, room lighting brightness changes, and link lighting module and door and window module, carries out illumination to corresponding room and door and window is adjusted
Section；(2) indoor and outdoor temperature, air quality and wind-force are detected by sensor, data is sent into intelligent control module, by intelligently controlling
Molding block instruction door and window；(3) illumination sensor detection environmental illumination intensity, daytime is by the linkage of control door and window as far as possible using certainly
Illumination sensor data are passed to Intelligent management module by right light, assign instruction to light switch to illumination by Intelligent management module
It is adjusted；(4) it is carried out by detection environmental smoke content, gas content, in conjunction with the abnormal data in video monitoring data tight
It is anxious to close and open corresponding equipment, and open or close door and window and lighting apparatus etc..
The energy module includes energy-storage units, household wind turbine, photovoltaic array.Intelligent control module receives
Energy forecast module data calculates and opens energy consumed by this period household wind turbine, photovoltaic array and generate
Energy comparison, if having and open household wind turbine, photovoltaic array and power storage.
The monitoring module includes mobile phone, local display, home network server, and wherein home network server will
All data are stored.
Shown in Figure 2, the built-up pattern of the energy forecast module based on small echo and ARIMA carries out the energy short-term
Prediction established ARIMA model as one group of training sample at interval of energy caused by one hour using 24 hours one day, by upper one
A stage multi-energy data is input to energy conservation module by intelligent control module, and output predicts intelligent control module, by intelligence
Energy control module degree energy regeneration module is adjusted, particular content include the following:
(1) wavelet decomposition
Input is that the discrete function of f (w) can be decomposed into the linear combination of four function different scales and position
Wherein approximation coefficient and detail coefficients are expressed as follows:
cj,k=< f (w), φj,k(w) (2) >
Scaling function indicates are as follows:
The present invention carries out 3 layers of decomposition to original yield using sym5 small echo,It is equivalent to a low-pass filter, by f
(w) it is decomposed into a low-frequency approximation component and a high frequency detail component, the algorithm of decomposition has:
Wherein H is low-pass filter, and G is high-pass filter, and each layer is by signal decomposition at low-frequency approximation component and high frequency
Upper one layer of high frequency detail component is decomposed into low-frequency approximation component and high frequency detail component, by three layers points by details coefficients again
F (w) is resolved into four components, respectively three-level approximation component, level-one details coefficients, second level details coefficients, three-level details by solution
Component.
(2) it will be predicted by the four of wavelet decomposition component input ARIMA models, input be subjected to single order and is smoothly filtered
Wave, ARIMA (p, 1, q) model s mathematic(al) representation are as follows:
Wherein p is autoregression model coefficient, and q is the order of moving average model, wtFor observation, θi(j=1,2 ...,
Q), For residual error.Since the activity of main body has periodicity and randomness, so sample data is suitable for
The model prediction of ARIMA is inputted respectively with each component and establishes respective ARIMA model, the specific steps of which are as follows: (2.1) are sharp
Auto-correlation function, the deviation―related function of sample are obtained with Box-Jenkins modelling, statistical property tentatively judges that sequence is adapted to
Types of models；
(2.2) using redization information (AIC to determine the order of model), evaluation model quality is carried out with following formula:
AIC=2K/n-2L/n (7)
Wherein L is log-likelihood, and n is observation number, and K is estimative number of parameters.AIC criterion shows that K is smaller
Model is more succinct, and L more large-sized model is more accurate, calculates p, the optimum value of q further according to AIC criterion.To calculate the ginseng of each model
Number.
(3) wavelet reconstruction
Wavelet reconstruction is the inverse process of wavelet decomposition, and the low frequency details coefficients of decomposition and high frequency detail component are passed through respectively
It is added after low-pass filter and high-pass filter, restructing algorithm are as follows:
Wherein H*、G*For the conjugate matrices of H, G.By the three-level approximation component by the prediction of ARIMA model, level-one details
Data reconstruction after component, second level details coefficients, three-level details coefficients, specifically: first the three-level approximation component of prediction is passed through
Low-pass filter and the three-level details coefficients of prediction pass through the two stage approach component that high-pass filter is predicted, then by prediction
Two stage approach component is close by the level-one that low-pass filter and the second level details coefficients of prediction are predicted by high-pass filter
Like component, the first approximation component of prediction is finally passed through into high-pass filtering by low-pass filter and the level-one details coefficients of prediction
Device obtains prediction data.
(4) output data
Prediction data is output to intelligent control module, intelligent control module receives data, and prediction data, which is greater than, opens again
Then opening device adds the actual value prediction next stage of a period to next period for raw energy source device energy consumption
Energy output.
Claims (9)
1. a kind of smart home system, comprising: intelligent control module, energy forecast module, sensor network module, video monitoring
Module, execution module, energy module, monitoring module.
2. intelligent control module according to claim 1 includes to the energy, lighting system, home theater, security alarm, door
The control and management of window.Concrete mode include: (1) intelligent control module to energy conservation module send the upper time generate,
The energy used, the energy needed for predicting by energy conservation module simultaneously will predict that the required energy is sent in system, according to input
There are four types of possible energy modes for data intelligence control module: 1) providing electric energy from renewable energy and battery；2) Cong Kezai
Electric energy is provided in raw energy source station, if there is dump energy, is charged the battery；3) electric energy is provided in renewable energy source station, if there is
Dump energy, and battery fills with electricity, then electricity is sold to power grid；4) electricity is provided from renewable energy, battery and power grid
Energy.(2) lighting system controls ambient brightness, including detects and change brightness of illumination under someone and unmanned environment, and daytime is logical
The optimal comfort lighting environment that linkage door and window reaches people using natural light is crossed, night closes partial illumination.(3) home audio-visual can lead to
It crosses mobile phone or computer is controlled, and illumination and the door and window in corresponding room are set when opening or closing audio-visual in conjunction with suitable environment
It is adjusted.(4) safety alarm system judges combustion gas in environment, smog content etc. by sensor network and video monitoring data
Whether exceed threshold value, occurs informing user when non-database storage face and opening warning mode in video monitoring.(5) pass through biography
Monitoring of the sensor network to indoor and outdoor surroundings, as Outdoor Air Quality PM2.5Lower than 2.5 μ g/m3、CO2Gas concentration is lower than
0.09%, total volatile organism TVOC concentration is lower than 0.2mg/m3When door and window, and combine wind sensor data judge
The degree of door and window, meanwhile, comfortably door and window is also adjusted according to energy conservation and user.
3. energy conservation module according to claim 1 is to integrate rolling average autoregression model in conjunction with small echo and difference
The built-up pattern of ARIMA carries out short-term forecast to the energy is generated.
4. sensor network module according to claim 1 includes: temperature sensor, humidity sensor, CO2Gas sensing
Device, TVOC sensor, PM2.5Sensor, smoke sensor device, gas security, video sensor, infrared detection sensor, wind-force
Sensor, illuminance transducer, door and window magnetic detector sensor, sensor network are attached using Zigbee protocol.Wherein
ZigBee and household electrical appliances use starlike connection type, are formed using a coordinator and multiple routings and terminal, all data periods
Property be sent to coordinator, coordinator receives data, transmits data to intelligent control module by gateway.
5. video monitoring module according to claim 1 is made of video acquisition, video processing and conversion, data transmission.
Video monitoring is for monitoring abnormal conditions, as old man, baby monitor, stranger swarm into, household appliance is abnormal.
6. execution module according to claim 1 receives intelligent control module information by Zigbee coordinator, search is set
Standby address sends instruction, including video module, door and window module, lighting module, security module, comprising: (1) opens audio-visual instruction
Audio-visual devices are transmitted to, room lighting brightness changes, and link lighting module and door and window module, illuminates to corresponding room
It is adjusted with door and window；(2) indoor and outdoor temperature, air quality and wind-force are detected by sensor, data is sent into intelligent control
Module instructs door and window by intelligent control module；(3) illumination sensor detects environmental illumination intensity, passes through control door and window connection daytime
It is dynamic to utilize natural light as far as possible, illumination sensor data are passed to Intelligent management module, by Intelligent management module assign instruction to
Illumination is adjusted in light switch；(4) by detection environmental smoke content, gas content, in conjunction in video monitoring data
Abnormal data carries out emergency cut-off equipment corresponding with unlatching, and open or close door and window and lighting apparatus etc..
7. energy module according to claim 1 includes energy-storage units, household wind turbine, photovoltaic array.Intelligence control
Molding block receives energy forecast module data, calculates and opens this period household wind turbine, photovoltaic array is consumed
Energy and generate energy comparison, if open household wind turbine, photovoltaic array and power storage.
8. monitoring module according to claim 1 includes mobile phone, local display, home network server, wherein family
Network server stores all data.
9. energy forecast module according to claim 3, the built-up pattern based on small echo and ARIMA carries out the energy short-term
Prediction established ARIMA model as one group of training sample at interval of energy caused by one hour using 24 hours one day, by upper one
A stage multi-energy data is input to energy conservation module by intelligent control module, and output predicts intelligent control module, by intelligence
Energy control module degree energy regeneration module is adjusted, content include the following:
(1) wavelet decomposition
Input is that the discrete function of f (w) can be decomposed into the linear combination of four function different scales and position
Wherein approximation coefficient and detail coefficients are expressed as follows:
cj,k=< f (w), φj,k(w) (2) >
Scaling function indicates are as follows:
The present invention carries out 3 layers of decomposition to original yield using sym5 small echo,It is equivalent to a low-pass filter, by f (w)
It is decomposed into a low-frequency approximation component and a high frequency detail component, the algorithm of decomposition has:
Wherein H is low-pass filter, and G is high-pass filter, and each layer is by signal decomposition at low-frequency approximation component and high frequency detail
Upper one layer of high frequency detail component is decomposed into low-frequency approximation component and high frequency detail component by component again, is decomposed by three layers by f
(w) four components, respectively three-level approximation component, level-one details coefficients, second level details coefficients, three-level details coefficients are resolved into.
(2) ARIMA model will be separately input to by the four of wavelet decomposition components to predict, input is subjected to single order and is smoothly filtered
Wave, ARIMA (p, 1, q) model s mathematic(al) representation are as follows:
Wherein p is autoregression model coefficient, and q is the order of moving average model, wtFor observation, θi(j=1,2 ..., q), For residual error.Since the activity of main body has periodicity and randomness, so sample data is suitable for
The model prediction of ARIMA is inputted respectively with each component and establishes respective ARIMA model, the specific steps of which are as follows:
(2.1) auto-correlation function, the deviation―related function of sample are obtained using Box-Jenkins modelling, statistical property is tentatively sentenced
The types of models that disconnected sequence is adapted to；
(2.2) using redization information (AIC to determine the order of model), evaluation model quality is carried out with following formula:
AIC=2K/n-2L/n (7)
Wherein L is log-likelihood, and n is observation number, and K is estimative number of parameters.AIC criterion shows that K gets over mini Mod
More succinct, L more large-sized model is more accurate, calculates p, the optimum value of q further according to AIC criterion.To calculate the parameter of each model.
(3) wavelet reconstruction
Wavelet reconstruction is the inverse process of wavelet decomposition, and the low frequency details coefficients of decomposition and high frequency detail component are passed through low pass respectively
It is added after filter and high-pass filter, restructing algorithm are as follows:
Wherein H*、G*For the conjugate matrices of H, G.Will by the three-level approximation component of prediction of ARIMA model, level-one details coefficients,
Data reconstruction after second level details coefficients, three-level details coefficients, specifically: the three-level approximation component of prediction is first passed through into low pass filtered
Wave device and the three-level details coefficients of prediction pass through the two stage approach component that high-pass filter is predicted, then the second level of prediction is close
Pass through the first approximation component that high-pass filter is predicted by low-pass filter and the second level details coefficients of prediction like component,
Finally the first approximation component of prediction is obtained by low-pass filter and the level-one details coefficients of prediction by high-pass filter
Prediction data.
(4) output data
Prediction data is output to intelligent control module, intelligent control module receives data, and prediction data, which is greater than, opens Regenerated energy
Then opening device adds the energy of the actual value prediction next stage of a period to next period for source device energy consumption
Output.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910633617.XA CN110348638A (en) | 2019-07-15 | 2019-07-15 | A kind of low power consuming smart home system of predictable energy regeneration |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910633617.XA CN110348638A (en) | 2019-07-15 | 2019-07-15 | A kind of low power consuming smart home system of predictable energy regeneration |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110348638A true CN110348638A (en) | 2019-10-18 |
Family
ID=68176246
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910633617.XA Pending CN110348638A (en) | 2019-07-15 | 2019-07-15 | A kind of low power consuming smart home system of predictable energy regeneration |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110348638A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115100796A (en) * | 2022-06-16 | 2022-09-23 | 慧之安信息技术股份有限公司 | Cinema safety monitoring system based on edge calculation |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH1049791A (en) * | 1996-08-05 | 1998-02-20 | Nippon Telegr & Teleph Corp <Ntt> | Method and device for processing time sequential signal |
| CN106707778A (en) * | 2016-12-06 | 2017-05-24 | 长沙理工大学 | Model predictive control-based home integrated energy intelligent optimization and management system |
| CN107154626A (en) * | 2017-06-02 | 2017-09-12 | 张飞飞 | Distributed photovoltaic intelligent energy manages SaaS systems with energy storage |
-
2019
- 2019-07-15 CN CN201910633617.XA patent/CN110348638A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH1049791A (en) * | 1996-08-05 | 1998-02-20 | Nippon Telegr & Teleph Corp <Ntt> | Method and device for processing time sequential signal |
| CN106707778A (en) * | 2016-12-06 | 2017-05-24 | 长沙理工大学 | Model predictive control-based home integrated energy intelligent optimization and management system |
| CN107154626A (en) * | 2017-06-02 | 2017-09-12 | 张飞飞 | Distributed photovoltaic intelligent energy manages SaaS systems with energy storage |
Non-Patent Citations (2)
| Title |
|---|
| 何伟强: "基于小波分析的我国居民生活能源消费预测", 《科技广场》 * |
| 蒋心泽等: "多电源***在智能家居中的协调优化应用", 《低压电器》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115100796A (en) * | 2022-06-16 | 2022-09-23 | 慧之安信息技术股份有限公司 | Cinema safety monitoring system based on edge calculation |
| CN115100796B (en) * | 2022-06-16 | 2023-08-25 | 慧之安信息技术股份有限公司 | Cinema safety monitoring system based on edge calculation |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Jabbar et al. | Design and implementation of IoT-based automation system for smart home | |
| CN103608991B (en) | Intelligent node | |
| CN203117757U (en) | Household monitoring system | |
| Nacer et al. | Smart Home, Smart HEMS, Smart heating: An overview of the latest products and trends | |
| CN103389710A (en) | Internet of Things environment control method and device based on context-aware | |
| CN106997649A (en) | A kind of network intelligence open flame detector control system and its control method | |
| CN112728727A (en) | Intelligent adjusting system for indoor environment comfort level based on edge calculation | |
| CN109932918A (en) | A kind of intelligent home control system | |
| CN112491668A (en) | Intelligence house thing networking safety monitoring device | |
| CN105204358A (en) | Smart home electric energy online management device based on remote communication of CDMA network | |
| Patil et al. | A consumer-based smart home and health monitoring system | |
| CN203338106U (en) | Wireless intelligent housing system based on wifi | |
| CN110348638A (en) | A kind of low power consuming smart home system of predictable energy regeneration | |
| Hasan et al. | Smart Home Automation System Powered by Renewable Energy | |
| CN113890024A (en) | Non-invasive load intelligent decomposition and optimization control method | |
| US11372033B1 (en) | Electric power monitoring system | |
| CN205862184U (en) | A kind of Intelligent window system | |
| Iwayemi et al. | Energy management for intelligent buildings | |
| CN110554613A (en) | Intelligent household control system | |
| Bhatt et al. | RS-485/MODBUS based intelligent building automation system using LabVIEW | |
| CN110336722A (en) | A kind of smart home center management system | |
| Tamezheneal et al. | Design and Development of IoT Oriented Solar Powered Smart Home Controlling Mechanism | |
| US20220140639A1 (en) | Smart generator | |
| Murdan | From Smart to Intelligent: How Internet of Things and Artificial Intelligence are Enhancing the Modern Home | |
| CN107065699A (en) | One kind is based on photovoltaic generation intelligent door and window managing and control system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20191018 |
|
| RJ01 | Rejection of invention patent application after publication |