CN117366833A - Data analysis and acquisition system for heating ventilation air conditioner - Google Patents
Data analysis and acquisition system for heating ventilation air conditioner Download PDFInfo
- Publication number
- CN117366833A CN117366833A CN202311291855.XA CN202311291855A CN117366833A CN 117366833 A CN117366833 A CN 117366833A CN 202311291855 A CN202311291855 A CN 202311291855A CN 117366833 A CN117366833 A CN 117366833A
- Authority
- CN
- China
- Prior art keywords
- data
- heating
- neural network
- ventilation
- network model
- 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
- 238000010438 heat treatment Methods 0.000 title claims abstract description 130
- 238000009423 ventilation Methods 0.000 title claims abstract description 75
- 238000007405 data analysis Methods 0.000 title claims abstract description 20
- 238000012545 processing Methods 0.000 claims abstract description 37
- 238000004378 air conditioning Methods 0.000 claims abstract description 31
- 238000004458 analytical method Methods 0.000 claims abstract description 24
- 238000003062 neural network model Methods 0.000 claims description 45
- 238000012549 training Methods 0.000 claims description 42
- 238000012360 testing method Methods 0.000 claims description 27
- 238000009499 grossing Methods 0.000 claims description 12
- 238000004422 calculation algorithm Methods 0.000 claims description 11
- 230000002159 abnormal effect Effects 0.000 claims description 8
- 238000013528 artificial neural network Methods 0.000 claims description 7
- 238000011217 control strategy Methods 0.000 claims description 7
- 238000011156 evaluation Methods 0.000 claims description 7
- 238000001514 detection method Methods 0.000 claims description 6
- 230000003993 interaction Effects 0.000 claims description 6
- 238000004891 communication Methods 0.000 claims description 4
- 238000010276 construction Methods 0.000 claims description 3
- 238000005457 optimization Methods 0.000 claims description 3
- 238000007781 pre-processing Methods 0.000 claims description 3
- 238000013480 data collection Methods 0.000 claims 3
- 230000003111 delayed effect Effects 0.000 abstract description 5
- 230000001105 regulatory effect Effects 0.000 abstract description 3
- 238000005265 energy consumption Methods 0.000 description 8
- 238000000034 method Methods 0.000 description 7
- 238000012544 monitoring process Methods 0.000 description 4
- 230000009286 beneficial effect Effects 0.000 description 3
- 238000006243 chemical reaction Methods 0.000 description 3
- 230000001276 controlling effect Effects 0.000 description 2
- 238000007796 conventional method Methods 0.000 description 1
- 238000007418 data mining Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000004134 energy conservation Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 238000002309 gasification Methods 0.000 description 1
- 239000007788 liquid Substances 0.000 description 1
- 238000004806 packaging method and process Methods 0.000 description 1
- 238000007637 random forest analysis Methods 0.000 description 1
- 239000003507 refrigerant Substances 0.000 description 1
- 238000005057 refrigeration Methods 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Classifications
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/89—Arrangement or mounting of control or safety devices
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/50—Control or safety arrangements characterised by user interfaces or communication
- F24F11/52—Indication arrangements, e.g. displays
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/50—Control or safety arrangements characterised by user interfaces or communication
- F24F11/56—Remote control
- F24F11/58—Remote control using Internet communication
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
- F24F11/63—Electronic processing
- F24F11/64—Electronic processing using pre-stored data
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/88—Electrical aspects, e.g. circuits
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/082—Learning methods modifying the architecture, e.g. adding, deleting or silencing nodes or connections
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F2110/00—Control inputs relating to air properties
- F24F2110/10—Temperature
- F24F2110/12—Temperature of the outside air
Landscapes
- Engineering & Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Combustion & Propulsion (AREA)
- Mechanical Engineering (AREA)
- Chemical & Material Sciences (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Mathematical Physics (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Computation (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Signal Processing (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Human Computer Interaction (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Physics & Mathematics (AREA)
- Software Systems (AREA)
- Fuzzy Systems (AREA)
- Air Conditioning Control Device (AREA)
Abstract
The invention relates to the technical field of heating ventilation and air conditioning, and discloses a data analysis and acquisition system for heating ventilation and air conditioning, which comprises the following components: a central processing unit; the temperature acquisition system is used for acquiring indoor temperature, heating load data and outdoor temperature and sending the indoor temperature, the heating load data and the outdoor temperature to the analysis processing system. According to the data analysis and acquisition system for the heating ventilation air conditioner, the future change of the outdoor temperature, the heating load and the indoor temperature are analyzed, so that the heating load which is required to be provided under the condition of keeping the indoor temperature unchanged is obtained, and the heating load is synchronously regulated when the outdoor temperature is changed, so that the change of the heating load is no longer delayed from the change of the outdoor temperature, the indoor temperature can be effectively kept unchanged, the stability of the indoor temperature is ensured, and the comfort of indoor personnel is improved.
Description
Technical Field
The invention relates to the technical field of heating ventilation and air conditioning, in particular to a data analysis and acquisition system for heating ventilation and air conditioning.
Background
The central air conditioner is composed of one or more cold and heat source systems and a plurality of air conditioning systems, is different from the traditional refrigerant type air conditioner, and is used for intensively processing air to meet comfort requirements, and the liquid gasification refrigeration principle is adopted to provide required cold energy for the air conditioning systems so as to offset the cold load of indoor environment; the heating system provides the air conditioning system with required heat to offset the indoor environmental heat load, and the refrigerating system is a vital part of the central air conditioner, and adopts types, operation modes, structural forms and the like to directly influence the economy, high efficiency and rationality of the central air conditioner in operation.
The household central air conditioner is also called as household central air conditioner and household central air conditioner, is a miniaturized independent air conditioning system, and is suitable for large-space households, office buildings and the like. With the development of cloud database technology, a large amount of high-dimensional real-time energy consumption data is accumulated in the running energy consumption metering of a central air conditioning system, and knowledge contained in the data is difficult to discover and summarize by a conventional method. The boruta feature selection algorithm in the data mining is a packaging algorithm of random forests, extracts an energy consumption feature subset for the original energy consumption features of the air conditioner, reduces redundancy of the energy consumption feature subset by adopting a pearson correlation coefficient method, and finally can effectively predict the energy consumption of the central air conditioner by taking the feature subset as an input parameter of a heuristic BP neural network, so that the method has great significance in modeling the energy consumption of the central air conditioner.
The publication number is: the Chinese patent of CN115059985A discloses a heating ventilation air conditioner energy consumption data analysis acquisition device, which comprises a data acquisition module, an air conditioner management module, a monitoring module, a data processing module, a digital-to-analog conversion module, a central processing unit, a storage unit, a communication unit, an analysis unit and a display unit, wherein the output end of the data acquisition module is connected to the input end of the air conditioner management module, the output end of the monitoring module is connected to the input end of the air conditioner management module, the output end of the air conditioner management module is connected to the input end of the data processing module, the output end of the data processing module is connected to the input end of the digital-to-analog conversion module, and the output end of the digital-to-analog conversion module is connected to the input end of the central processing unit. The beneficial effects are that: the data of the heating ventilation air conditioner are analyzed in real time through monitoring and adjusting the temperature, the humidity and the ventilation, the data are collected in real time, the temperature, the humidity and the pressure of the heating ventilation air conditioner are monitored in real time through the monitoring module, the monitored results are subjected to characteristic analysis, the analysis is accurate, and the collection is stable.
However:
at present, most of the existing heating load adjusting methods automatically increase or decrease the heating load according to indoor temperature change, however, the adjusting mode is delayed to indoor temperature change, so that when the method is operated, indoor temperature can be frequently fluctuated, discomfort is caused to indoor personnel, meanwhile, the delayed heating load change can also cause frequent high-load operation and heating stop, and the frequent starting and stopping of heating equipment can consume a large amount of energy, so that energy conservation is not facilitated.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects of the prior art, the invention provides a data analysis and acquisition system for heating ventilation and air conditioning.
(II) technical scheme
In order to achieve the above purpose, the present invention provides the following technical solutions: a data analysis and acquisition system for a heating ventilation air conditioner, comprising:
a central processing unit;
the temperature acquisition system is used for acquiring indoor temperature, heating load data and outdoor temperature and sending the indoor temperature, the heating load data and the outdoor temperature to the analysis processing system;
the environment prediction system is used for acquiring air temperature change data in the future 24 hours through a network, drawing an air temperature change curve according to the air temperature change data and sending the air temperature change curve to the analysis processing system;
the analysis processing system is used for calculating a heating capacity value through indoor temperature, heating load data and outdoor temperature, calculating heating load data change according to an air temperature change curve and the heating capacity value, generating a heating load change signal, and sending the heating load change signal to the central processing unit;
the data communication system is used for transmitting the operation state information data of the heating and ventilation equipment to the central processing unit;
and the optimal control system generates an optimal control strategy according to the load prediction result to obtain final control parameters for controlling the operation state of the heating ventilation air conditioner.
Preferably, when acquiring air temperature change data of 24 hours in the future, the environment prediction system acquires a temperature data point every other hour, wherein the temperature data point is outdoor temperature data, and draws an air temperature change curve by taking time as an X axis and taking temperature as a Y axis.
Preferably, the analysis processing system records the indoor temperature as T, the outdoor temperature as T, the heating load data as Q, and obtains the heating capacity value through formula analysisAnd k is a preset heat exchange coefficient, and according to the outdoor temperature of each point in the air temperature change curve, calculating estimated heating load data under the condition of keeping the indoor temperature unchanged, and sending the air temperature change curve and the estimated heating load data to the central processor.
Preferably, the optimization control system comprises a data receiving unit, a database unit, a data interaction unit, an evaluation unit and an alarm unit;
the data receiving unit is used for receiving the operation state information data of the heating and ventilation equipment sent by the state detection module;
the database unit is used for storing the running state information data of the heating and ventilation equipment and providing inquiry, updating, searching and storage services for users;
the data interaction unit is used for carrying out data exchange among the state detection module, the server module and the client module;
the evaluation unit is used for carrying out safety state analysis according to the operation state information data of the heating and ventilation equipment and the preset safety operation state information data and judging the current safety state of the heating and ventilation equipment; when the operation state information data of the heating and ventilation equipment is outside a preset safe operation state information data interval, judging that the heating and ventilation equipment is in an abnormal state currently;
and the alarm unit is used for alarming when judging that the heating and ventilation equipment is in an abnormal state.
Preferably, the alarm unit alarms by sending text or voice messages to the client APP.
Preferably, the optimizing control system further comprises a load prediction model for optimizing after the analysis processing system sends the load change signal to the central processing unit;
the load prediction model adopts a combined prediction algorithm combining a secondary exponential smoothing algorithm and a neural network model:
respectively inputting the preprocessed system characteristic data, weather forecast data and historical system characteristic data into a secondary exponential smoothing model and a neural network model to obtain the load demand of the heating ventilation air conditioning system at the next moment predicted by the secondary exponential smoothing and the load demand of the heating ventilation air conditioning system at the next moment predicted by the neural network;
and (3) carrying out weighted average algorithm on the load demand of the heating, ventilation and air conditioning system at the next moment predicted by the secondary index smoothing and the load demand of the heating, ventilation and air conditioning system at the next moment predicted by the neural network to obtain a load prediction result.
Preferably, the method for constructing the neural network model comprises the following steps: acquiring data set information for preprocessing, and randomly distributing the data set information into training data and test data; establishing an initial structure and an initial weight of the neural network model, respectively training by using training data, and testing by using testing data; based on training time and test errors, adjusting the width and depth of the neural network model to an optimized value, and then storing the adjusted neural network model structure and weight.
Preferably, the method for constructing the neural network model specifically comprises the following steps:
creating a data input layer according to the dimension of the preprocessed data set information; creating a data hiding layer, wherein the initial width number of the data hiding layer is 10, and the initial layer number is 1;
creating an output layer, wherein the dimension number of the output layer is 1, and obtaining an initial structure of the neural network model;
training an initial structure of the neural network model by using training data, recording training time t1, testing by using test data, and recording prediction mean square error mse1;
if the training time t1 is smaller than the set training time limit, or the prediction mean square error mse1 is larger than the prediction mean square error plus the set mean square error tolerance in the previous cycle, adding one to the data hiding layer depth, and retraining and testing the neural network model after adding one to the data hiding layer depth;
otherwise, saving the neural network model structure and the weight at the moment;
after the width of the hidden layer of the stored neural network model structure is increased by one, training is carried out by using training data, and training time t2 is recorded; testing by using test data, and recording a prediction mean square error mse2; if the training time t2 is smaller than the set training time limit, or the prediction mean square error mse2 is larger than the prediction mean square error plus the set mean square error tolerance in the previous cycle, the width of the data hiding layer is plus one, and the neural network model after the data hiding layer width plus one is retrained and tested;
otherwise, saving the neural network model structure and the weight at the moment to obtain the constructed neural network model.
(III) beneficial effects
Compared with the prior art, the invention provides a data analysis and acquisition system for heating ventilation and air conditioning, which has the following beneficial effects:
1. according to the data analysis and acquisition system for the heating ventilation air conditioner, the future change of the outdoor temperature, the heating load and the indoor temperature are analyzed, so that the heating load which is required to be provided under the condition of keeping the indoor temperature unchanged is obtained, and the heating load is synchronously regulated when the outdoor temperature is changed, so that the change of the heating load is no longer delayed from the change of the outdoor temperature, the indoor temperature can be effectively kept unchanged, the stability of the indoor temperature is ensured, and the comfort of indoor personnel is improved.
2. According to the data analysis and acquisition system for the heating ventilation air conditioner, the safety evaluation is carried out on the running state of the heating ventilation equipment, when the heating ventilation equipment is in a problem, timely processing is found timely, and resource waste and abnormal use of the heating ventilation equipment caused by damage due to untimely processing are avoided.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a block diagram of a system of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments.
As shown in fig. 1, the present invention provides a data analysis and acquisition system for a heating ventilation air conditioner, including:
a central processing unit;
the temperature acquisition system is used for acquiring indoor temperature, heating load data and outdoor temperature and sending the indoor temperature, the heating load data and the outdoor temperature to the analysis processing system;
the environment prediction system is used for acquiring air temperature change data in the future 24 hours through a network, drawing an air temperature change curve according to the air temperature change data and sending the air temperature change curve to the analysis processing system;
the analysis processing system is used for calculating a heating capacity value through indoor temperature, heating load data and outdoor temperature, calculating heating load data change according to an air temperature change curve and the heating capacity value, generating a heating load change signal, and sending the heating load change signal to the central processing unit;
the data communication system is used for transmitting the operation state information data of the heating and ventilation equipment to the central processing unit;
and the optimal control system generates an optimal control strategy according to the load prediction result to obtain final control parameters for controlling the operation state of the heating ventilation air conditioner.
In the embodiment of the invention, when the environment prediction system acquires air temperature change data of 24 hours in the future, a temperature data point is acquired every other hour, wherein the temperature data point is outdoor temperature data, the time is taken as an X axis, and the temperature is taken as a Y axis to draw an air temperature change curve.
In the embodiment of the invention, the analysis processing system records the indoor temperature as T, the outdoor temperature as T, the heating load data as Q, and the heating capacity value is obtained through formula analysisAnd k is a preset heat exchange coefficient, and according to the outdoor temperature of each point in the air temperature change curve, calculating estimated heating load data under the condition of keeping the indoor temperature unchanged, and sending the air temperature change curve and the estimated heating load data to the central processor.
The future change of the outdoor temperature, the heating load and the indoor temperature are analyzed, so that the heating load which is required to be provided under the condition of maintaining the indoor temperature unchanged is obtained, and the heating load is synchronously regulated when the outdoor temperature is changed, so that the change of the heating load is no longer delayed from the change of the outdoor temperature, the indoor temperature can be effectively kept unchanged, the stability of the indoor temperature is ensured, and the comfort of indoor personnel is improved.
In the embodiment of the invention, the optimization control system comprises a data receiving unit, a database unit, a data interaction unit, an evaluation unit and an alarm unit;
the data receiving unit is used for receiving the operation state information data of the heating and ventilation equipment sent by the state detection module;
the database unit is used for storing the running state information data of the heating and ventilation equipment and providing inquiry, updating, searching and storage services for users;
the data interaction unit is used for carrying out data exchange among the state detection module, the server module and the client module;
the evaluation unit is used for carrying out safety state analysis according to the operation state information data of the heating and ventilation equipment and the preset safety operation state information data and judging the current safety state of the heating and ventilation equipment; when the operation state information data of the heating and ventilation equipment is outside a preset safe operation state information data interval, judging that the heating and ventilation equipment is in an abnormal state currently;
and the alarm unit is used for alarming when judging that the heating and ventilation equipment is in an abnormal state.
After the operation state information data of the heating and ventilation equipment is received, the operation state information data of the heating and ventilation equipment is compared with the preset safe operation state information data, safety state analysis is carried out on the heating and ventilation equipment, the current safety state of the heating and ventilation equipment is judged, when the operation state information data of the heating and ventilation equipment is outside the preset safe operation state information data interval, the heating and ventilation equipment is judged to be in an abnormal state currently, and an alarm is carried out by sending characters or voice information to a client APP, so that a user can conveniently and intuitively know the operation state of the heating and ventilation equipment.
In the embodiment of the invention, the alarm unit alarms by sending text or voice messages to the client APP.
In the embodiment of the invention, the optimizing control system further comprises an optimizing step of utilizing a load prediction model after the analyzing and processing system sends the load change signal to the central processing unit;
the load prediction model adopts a combined prediction algorithm combining a secondary exponential smoothing algorithm and a neural network model:
respectively inputting the preprocessed system characteristic data, weather forecast data and historical system characteristic data into a secondary exponential smoothing model and a neural network model to obtain the load demand of the heating ventilation air conditioning system at the next moment predicted by the secondary exponential smoothing and the load demand of the heating ventilation air conditioning system at the next moment predicted by the neural network;
and (3) carrying out weighted average algorithm on the load demand of the heating, ventilation and air conditioning system at the next moment predicted by the secondary index smoothing and the load demand of the heating, ventilation and air conditioning system at the next moment predicted by the neural network to obtain a load prediction result.
In the embodiment of the invention, the method for constructing the neural network model comprises the following steps: acquiring data set information for preprocessing, and randomly distributing the data set information into training data and test data; establishing an initial structure and an initial weight of the neural network model, respectively training by using training data, and testing by using testing data; based on training time and test errors, adjusting the width and depth of the neural network model to an optimized value, and then storing the adjusted neural network model structure and weight.
In the embodiment of the invention, the construction method of the neural network model specifically comprises the following steps:
creating a data input layer according to the dimension of the preprocessed data set information; creating a data hiding layer, wherein the initial width number of the data hiding layer is 10, and the initial layer number is 1;
creating an output layer, wherein the dimension number of the output layer is 1, and obtaining an initial structure of the neural network model;
training an initial structure of the neural network model by using training data, recording training time t1, testing by using test data, and recording prediction mean square error mse1;
if the training time t1 is smaller than the set training time limit, or the prediction mean square error mse1 is larger than the prediction mean square error plus the set mean square error tolerance in the previous cycle, adding one to the data hiding layer depth, and retraining and testing the neural network model after adding one to the data hiding layer depth;
otherwise, saving the neural network model structure and the weight at the moment;
after the width of the hidden layer of the stored neural network model structure is increased by one, training is carried out by using training data, and training time t2 is recorded; testing by using test data, and recording a prediction mean square error mse2; if the training time t2 is smaller than the set training time limit, or the prediction mean square error mse2 is larger than the prediction mean square error plus the set mean square error tolerance in the previous cycle, the width of the data hiding layer is plus one, and the neural network model after the data hiding layer width plus one is retrained and tested;
otherwise, saving the neural network model structure and the weight at the moment to obtain the constructed neural network model.
The optimized control strategy output is combined with the control strategy output, and on the basis that the basic control strategy guarantees the safe operation of the system, the optimized parameters are superposed, the optimized control strategy is realized, and finally the energy-saving operation of the heating ventilation air conditioning system is controlled.
Claims (8)
1. A data analysis and acquisition system for a heating ventilation air conditioner, comprising:
a central processing unit;
the temperature acquisition system is used for acquiring indoor temperature, heating load data and outdoor temperature and sending the indoor temperature, the heating load data and the outdoor temperature to the analysis processing system;
the environment prediction system is used for acquiring air temperature change data in the future 24 hours through a network, drawing an air temperature change curve according to the air temperature change data and sending the air temperature change curve to the analysis processing system;
the analysis processing system is used for calculating a heating capacity value through indoor temperature, heating load data and outdoor temperature, calculating heating load data change according to an air temperature change curve and the heating capacity value, generating a heating load change signal, and sending the heating load change signal to the central processing unit;
the data communication system is used for transmitting the operation state information data of the heating and ventilation equipment to the central processing unit;
and the optimal control system generates an optimal control strategy according to the load prediction result to obtain final control parameters for controlling the operation state of the heating ventilation air conditioner.
2. The data analysis and acquisition system for heating ventilation and air conditioning according to claim 1, wherein: when acquiring temperature change data of 24 hours in the future, the environment prediction system acquires a temperature data point every other hour, wherein the temperature data point is outdoor temperature data, the time is taken as an X axis, and the temperature is taken as a Y axis to draw a temperature change curve.
3. The data analysis and acquisition system for heating ventilation and air conditioning according to claim 1, wherein: the analysis processing system records the indoor temperature as T, the outdoor temperature as T, the heating load data as Q, and obtains the heating capacity value through formula analysisAnd k is a preset heat exchange coefficient, and according to the outdoor temperature of each point in the air temperature change curve, calculating estimated heating load data under the condition of keeping the indoor temperature unchanged, and sending the air temperature change curve and the estimated heating load data to the central processor.
4. The data analysis and acquisition system for heating ventilation and air conditioning according to claim 1, wherein: the optimization control system comprises a data receiving unit, a database unit, a data interaction unit, an evaluation unit and an alarm unit;
the data receiving unit is used for receiving the operation state information data of the heating and ventilation equipment sent by the state detection module;
the database unit is used for storing the running state information data of the heating and ventilation equipment and providing inquiry, updating, searching and storage services for users;
the data interaction unit is used for carrying out data exchange among the state detection module, the server module and the client module;
the evaluation unit is used for carrying out safety state analysis according to the operation state information data of the heating and ventilation equipment and the preset safety operation state information data and judging the current safety state of the heating and ventilation equipment; when the operation state information data of the heating and ventilation equipment is outside a preset safe operation state information data interval, judging that the heating and ventilation equipment is in an abnormal state currently;
and the alarm unit is used for alarming when judging that the heating and ventilation equipment is in an abnormal state.
5. The data analysis and collection system for heating ventilation and air conditioning according to claim 4, wherein: the alarm unit alarms by sending text or voice messages to the client APP.
6. The data analysis and acquisition system for heating ventilation and air conditioning according to claim 1, wherein: the optimizing control system further comprises a load prediction model for optimizing after the analyzing and processing system sends the load change signal to the central processing unit;
the load prediction model adopts a combined prediction algorithm combining a secondary exponential smoothing algorithm and a neural network model:
respectively inputting the preprocessed system characteristic data, weather forecast data and historical system characteristic data into a secondary exponential smoothing model and a neural network model to obtain the load demand of the heating ventilation air conditioning system at the next moment predicted by the secondary exponential smoothing and the load demand of the heating ventilation air conditioning system at the next moment predicted by the neural network;
and (3) carrying out weighted average algorithm on the load demand of the heating, ventilation and air conditioning system at the next moment predicted by the secondary index smoothing and the load demand of the heating, ventilation and air conditioning system at the next moment predicted by the neural network to obtain a load prediction result.
7. The data analysis and collection system for heating ventilation and air conditioning according to claim 6, wherein: the construction method of the neural network model comprises the following steps: acquiring data set information for preprocessing, and randomly distributing the data set information into training data and test data; establishing an initial structure and an initial weight of the neural network model, respectively training by using training data, and testing by using testing data; based on training time and test errors, adjusting the width and depth of the neural network model to an optimized value, and then storing the adjusted neural network model structure and weight.
8. The data analysis and collection system for heating ventilation and air conditioning according to claim 6, wherein: the construction method of the neural network model specifically comprises the following steps:
creating a data input layer according to the dimension of the preprocessed data set information; creating a data hiding layer, wherein the initial width number of the data hiding layer is 10, and the initial layer number is 1;
creating an output layer, wherein the dimension number of the output layer is 1, and obtaining an initial structure of the neural network model;
training an initial structure of the neural network model by using training data, recording training time t1, testing by using test data, and recording prediction mean square error mse1;
if the training time t1 is smaller than the set training time limit, or the prediction mean square error mse1 is larger than the prediction mean square error plus the set mean square error tolerance in the previous cycle, adding one to the data hiding layer depth, and retraining and testing the neural network model after adding one to the data hiding layer depth;
otherwise, saving the neural network model structure and the weight at the moment;
after the width of the hidden layer of the stored neural network model structure is increased by one, training is carried out by using training data, and training time t2 is recorded; testing by using test data, and recording a prediction mean square error mse2; if the training time t2 is smaller than the set training time limit, or the prediction mean square error mse2 is larger than the prediction mean square error plus the set mean square error tolerance in the previous cycle, the width of the data hiding layer is plus one, and the neural network model after the data hiding layer width plus one is retrained and tested;
otherwise, saving the neural network model structure and the weight at the moment to obtain the constructed neural network model.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311291855.XA CN117366833A (en) | 2023-10-08 | 2023-10-08 | Data analysis and acquisition system for heating ventilation air conditioner |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311291855.XA CN117366833A (en) | 2023-10-08 | 2023-10-08 | Data analysis and acquisition system for heating ventilation air conditioner |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117366833A true CN117366833A (en) | 2024-01-09 |
Family
ID=89388418
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311291855.XA Pending CN117366833A (en) | 2023-10-08 | 2023-10-08 | Data analysis and acquisition system for heating ventilation air conditioner |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117366833A (en) |
-
2023
- 2023-10-08 CN CN202311291855.XA patent/CN117366833A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20210123625A1 (en) | Low-cost commissioning method for the air-conditioning systems in existing large public buildings | |
| US6937946B1 (en) | System and method for remote identification of energy consumption systems and components | |
| US7356548B1 (en) | System and method for remote monitoring and controlling of facility energy consumption | |
| CN113435657B (en) | Data integration processing method, system, energy management system, electronic device and computer readable storage medium | |
| CN111539652A (en) | Building energy management system and method based on Internet of things | |
| CN111191851B (en) | Knowledge graph-based data center energy efficiency optimization method | |
| Song et al. | An IoT-based smart controlling system of air conditioner for high energy efficiency | |
| CN104777798A (en) | Energy source monitoring method, device and system based on ZigBee | |
| CN104063766A (en) | Building energy efficiency management system based on cloud computing and big data technology | |
| CN114239972A (en) | Campus energy efficiency and electrical safety management method and system based on artificial intelligence technology | |
| CN114358555A (en) | Rail transit wisdom energy management system | |
| CN111780384A (en) | Central air-conditioning control system | |
| CN111158245A (en) | Building energy-saving monitoring and management method based on Internet of things | |
| CN114489307A (en) | Energy efficiency optimization method and device for internet data center | |
| WO2019109582A1 (en) | Non-intrusive energy consumption detection method and system | |
| CN112987617A (en) | Near-zero energy consumption building digital management system and energy efficiency monitoring method | |
| CN114234374B (en) | Split air conditioning electric control system, power saving controller and power saving amount calculation method thereof | |
| CN110579011A (en) | Energy efficiency precise control management system for unattended intelligent central air conditioner | |
| CN117490193A (en) | Group control method for central air-conditioning water system | |
| CN117366833A (en) | Data analysis and acquisition system for heating ventilation air conditioner | |
| CN111968008A (en) | Comprehensive energy monitoring system and method based on multi-energy cooperation | |
| CN111144628A (en) | Distributed energy supply type cooling, heating and power load prediction model system and method | |
| CN115906396A (en) | Passive building energy management system and use method thereof | |
| CN115437876A (en) | Data center management method and device, electronic equipment and storage medium | |
| CN111720954A (en) | Energy-saving method and energy-saving operation system for product |
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 |