CN104269940A - Building air conditioning equipment load monitoring system - Google Patents
Building air conditioning equipment load monitoring system Download PDFInfo
- Publication number
- CN104269940A CN104269940A CN201410593610.7A CN201410593610A CN104269940A CN 104269940 A CN104269940 A CN 104269940A CN 201410593610 A CN201410593610 A CN 201410593610A CN 104269940 A CN104269940 A CN 104269940A
- Authority
- CN
- China
- Prior art keywords
- building
- air conditioning
- conditioning equipment
- information
- power
- 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.)
- Granted
Links
- 238000004378 air conditioning Methods 0.000 title claims abstract description 95
- 238000012544 monitoring process Methods 0.000 title claims abstract description 20
- 230000005611 electricity Effects 0.000 claims description 15
- 238000004891 communication Methods 0.000 claims description 14
- 230000008859 change Effects 0.000 claims description 9
- 238000009472 formulation Methods 0.000 claims description 3
- 239000000203 mixture Substances 0.000 claims description 3
- 238000000034 method Methods 0.000 abstract description 3
- 239000011159 matrix material Substances 0.000 description 6
- 238000006243 chemical reaction Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000004134 energy conservation Methods 0.000 description 3
- 238000005070 sampling Methods 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 230000009466 transformation Effects 0.000 description 2
- 230000002457 bidirectional effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000011217 control strategy Methods 0.000 description 1
- 238000001816 cooling Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000005855 radiation Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Classifications
-
- 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
- Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
- Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
-
- 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
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
- Y04S20/20—End-user application control systems
- Y04S20/242—Home appliances
- Y04S20/244—Home appliances the home appliances being or involving heating ventilating and air conditioning [HVAC] units
Landscapes
- Air Conditioning Control Device (AREA)
Abstract
The invention relates to a building air conditioning equipment load monitoring system. According to a monitoring method, power utilization equipment of each air conditioning equipment in a building can be acquired in real time, a temperature of an environment where each air conditioning equipment is positioned can be acquired in real time and according to the acquired historical power utilization and environment temperature information, power utilization load of the air conditioning equipment of the building is predicted to obtain a power utilization curve of the air conditioning equipment of the building. According to the monitoring system, scheduling information of a power distribution network also can be acquired in real time and by combining the power utilization curve of the air conditioning equipment of the building and the scheduling information of the power distribution network, a power supply strategy of the air conditioning equipment of the building is determined so as to meet the power utilization requirement of the air conditioning equipment of the building and the scheduling requirement of the power distribution network to the greatest degree; power utilization efficiency, power utilization experience of users of the building and reliability of the power distribution network of the building are improved to a great degree.
Description
Technical Field
The invention relates to a load monitoring system of building air conditioning equipment.
Background
The reasonable control is carried out on the load of the power consumer, so that the shape of a load curve can be effectively improved, and the utilization rate of equipment can be improved. With the deepening of the intelligent power grid and the power demand side management concept, the operation mode of the power grid is developing towards a new direction of bidirectional interaction, multi-element service, intelligent response, energy conservation and high efficiency. The requirement of users on power utilization service is higher and higher, and the power utilization service mode which is dominant and passively accepted by the users in the past cannot meet the personalized and differentiated service requirements of the users.
The building intelligent power distribution system is a community power distribution terminal system developed aiming at the increasingly widely applied intelligent houses in the current city, and can be matched with a power distribution network automation main station and a power distribution network automation sub-station system to realize the functions of data acquisition, load detection, fault detection, automatic switching power supply and the like of a plurality of houses, a plurality of power consumption equipment and a plurality of power consumption lines, so that the energy conservation and the improvement of the power distribution efficiency and reliability are realized.
In a building intelligent power distribution system, prediction and monitoring of power load are always key factors for improving power distribution efficiency and saving energy, and are also one of difficult technologies in the building intelligent power distribution system. In a building, a significant portion of the energy consumption comes from the air conditioning system in the building. Meanwhile, in large buildings, a complex air conditioning system is generally required. Therefore, for the air conditioning system in the building, the electric energy management is carried out, so that the stable power utilization curve is obtained, and a user can obtain great benefit. In addition, people in buildings have higher and higher requirements on the performance of air conditioners, and mainly embody the aspects of intelligent control, energy conservation, environmental performance (such as low noise) and the like of the air conditioners.
Disclosure of Invention
In order to solve the problems, the invention provides a building air conditioning equipment load monitoring system, the monitoring method can acquire the electricity utilization information of each air conditioning equipment in a building in real time, acquire the environment temperature of each air conditioning equipment in real time, predict the electricity utilization load of the air conditioning equipment of the building according to the acquired historical electricity utilization and environment temperature information to obtain the electricity utilization curve of the building air conditioning equipment, the monitoring system can also acquire the dispatching information of a power distribution network in real time, and determine the power supply strategy of the building air conditioning equipment by combining the electricity utilization curve of the building air conditioning equipment and the dispatching information of the power distribution network so as to meet the electricity utilization requirement of the building air conditioning equipment and the dispatching requirement of the power distribution network to the maximum extent, and the electricity utilization efficiency, the electricity utilization experience of building users and the reliability of the building power distribution network are greatly improved.
In order to achieve the above object, the present invention provides a load monitoring system for a building air conditioner, the system comprising:
the air conditioning equipment power consumption information acquisition module is used for acquiring the power consumption information of each air conditioning equipment in the building in real time;
the indoor temperature acquisition module is used for acquiring the indoor environment temperature of the air conditioner;
the outdoor environment acquisition module is used for acquiring outdoor environment information such as temperature, humidity, wind power, sunlight intensity and the like outside the building;
the information storage module is used for storing the power utilization information acquired by the information acquisition module, the indoor environment temperature information acquired by the indoor temperature acquisition module and the outdoor environment information acquired by the outdoor environment acquisition module;
the distribution network communication module is used for acquiring scheduling information of the distribution network in real time and uploading relevant information of electricity utilization of the building air conditioning equipment to the distribution network;
air conditioning equipment load prediction and monitoring module includes:
the air conditioning equipment load prediction unit is used for predicting the air conditioning equipment load of the building to obtain a load curve of the air conditioning equipment;
the power supply strategy making unit is used for determining power supply strategies for all air-conditioning equipment according to the load curve and the scheduling information of the power distribution network; and
the display unit is used for displaying a power supply strategy, scheduling information and the running condition of the air conditioning equipment;
the power supply execution module is used for implementing specific power supply instructions to each air conditioning device according to the power supply strategy;
and the bus communication module is used for communication among the modules.
Furthermore, the air conditioning equipment load prediction unit comprises a building temperature prediction subunit, and the temperature prediction subunit predicts the temperature of each space of the building based on the indoor environment temperature information and the outdoor environment information acquired by the indoor temperature acquisition module.
Further, when the temperature of each space in the building is predicted, the building temperature prediction subunit first establishes a building thermal model according to the self characteristics of the building, then establishes a thermodynamic model under a state space according to the thermal model, and the thermodynamic model calculates the temperature change curve of each indoor space in a future period of time by taking the current indoor temperature information, the outdoor temperature, the humidity, the wind power, the sunlight intensity and other outdoor environment information as input quantities.
Further, the air conditioning equipment load prediction unit predicts the power utilization condition of each air conditioning equipment in a future period of time according to the indoor temperature change curve in the future period of time and the indoor historical temperature information of the building, and synthesizes the future power utilization conditions of all the air conditioning equipment to obtain the load curve of the air conditioning equipment in the building.
Further, the power supply strategy formulation unit determines the current power supply strategy according to the load curve, the power distribution network scheduling information and the real-time power utilization information of each air conditioning device: comparing the predicted value with a threshold value, sending an electric load regulation instruction according to the difference value between the predicted value and the threshold value, and if the predicted value is greater than or equal to the threshold value, sending the electric load regulation instruction to regulate the technical performance parameters of the air-conditioning equipment; and if the predicted value is smaller than the threshold value, supplying power to the air conditioning equipment according to the user requirement.
Furthermore, the air conditioning equipment power consumption information acquisition module acquires data of a plurality of air conditioning equipment, including voltage, current, active power, reactive power and electric energy.
Further, the threshold value is determined by the power distribution network scheduling information, the building distribution line parameters and the air conditioning equipment parameters.
The monitoring system of the invention has the following advantages: (1) accurately predicting the electrical load of the air conditioning equipment; (2) the power supply strategy takes the requirements of air conditioning equipment and the dispatching requirement of a power distribution network into account, meets the requirements of users, simultaneously takes the reliability of power utilization into account, and simultaneously improves the power utilization efficiency.
Drawings
FIG. 1 is a block diagram of a building air conditioning equipment load monitoring system used in the present invention;
FIG. 2 illustrates a detailed block diagram of the load prediction and monitoring module of the system of FIG. 1.
Detailed Description
Fig. 1 is a diagram illustrating an intelligent building power distribution system of the present invention, which includes: the system comprises an air conditioner electricity utilization information acquisition module 7, an indoor temperature acquisition module 9, an outdoor environment acquisition module 10, an information storage module 8, a distribution network contact module 3, an air conditioner load prediction and monitoring module 4, a power supply execution module 6 and a bus communication module 2.
And the air conditioning equipment electricity utilization information acquisition module 7 is used for acquiring the electricity utilization information of each air conditioning equipment 1-n in the building in real time. The air conditioning equipment power consumption information acquisition module 7 comprises a voltage transformer, a current transformer, an AD conversion circuit, a serial port communication circuit and a power supply, wherein the signal output ends of the voltage transformer and the current transformer are connected with the AD conversion circuit, the AD conversion circuit is connected with the serial port communication circuit, and the serial port communication circuit is connected with the bus communication module, so that the data of the air conditioning equipment information acquisition module are transmitted to other modules. The air conditioning equipment information acquisition module acquires data of a plurality of air conditioning equipment at the same time, wherein the data comprises voltage, current, active power, reactive power and electric energy.
And the indoor temperature acquisition module 9 is used for acquiring the indoor environment temperature of the air-conditioning equipment 1-n, and the indoor temperature acquisition module 9 can be realized by adopting a general temperature sensor and an analog-to-digital converter.
Outdoor environment collection module 10 for outdoor environment information such as the temperature outside the collection building, humidity, wind-force, sunshine intensity, this outdoor environment collection module can adopt general temperature sensor, humidity transducer, anemoscope, light sensor and adc to realize.
And the information storage module 8 is used for storing the power utilization information acquired by the power utilization information acquisition module 7 of the air conditioning equipment, the indoor temperature information acquired by the indoor temperature acquisition module 9 and the outdoor environment information such as the temperature, humidity, wind power and sunlight intensity outside the building acquired by the outdoor environment acquisition module. The information storage module 8 receives the information, decodes the information and stores the decoded information into the database system, and the air conditioning equipment load prediction and monitoring module 4 calls the air conditioning equipment power utilization information in the database system through the bus 2.
And the distribution network connection module 3 is used for acquiring the scheduling information of the superior distribution network 1 in real time and uploading the related information of the building intelligent distribution system to the superior distribution network 1.
And the power supply execution module 6 is used for implementing specific power supply instructions for each air conditioning device according to the power supply strategy. The power supply execution module 6 includes a power supply execution unit installed for each of the air conditioners 1 to n, and the power supply execution unit includes:
the power input interface is connected with the power transmission line;
the power output interface is connected with an electric load;
and the manual control switch FS and the bus control switch NS are connected in the load loop to control the on-off of the load.
And the bus communication module 2 is used for communication among the modules, and the bus communication module 2 is connected with other modules through a redundant double CAN bus.
The air conditioning equipment load prediction and monitoring module 4 comprises:
an air conditioning equipment load prediction unit 41, configured to predict an air conditioning equipment load of a building, so as to obtain a load curve of the air conditioning equipment;
the power supply strategy making unit 42 is used for determining power supply strategies for each air conditioning device according to the load curve and the scheduling information of the power distribution network; and
and the display unit 43 is used for displaying the power supply strategy, the scheduling information and the running condition of the air conditioning equipment.
The air conditioning equipment load prediction unit 41 includes a building temperature prediction subunit, and the temperature prediction subunit predicts the temperature of each space of the building based on the indoor environment temperature information and the outdoor environment information acquired by the indoor temperature acquisition module.
Specifically, when the temperature of each space in the building is predicted, the building temperature prediction subunit firstly establishes a building thermal model according to the self characteristics of the building, and then establishes a thermodynamic model under a state space according to the thermal model, wherein the thermodynamic model takes current indoor temperature information, outdoor temperature, humidity, wind power, sunlight intensity and other outdoor environment information as input quantities, and calculates the temperature change curve of each indoor space in a future period of time.
In buildings, heating and cooling systems are used to control the transfer of energy between different areas of the building (e.g., different rooms) or areas of the building and the external environment. The thermodynamics in each zone are influenced by three factors: outdoor environments (e.g., solar radiation to the exterior surfaces of buildings or heat exchange with the outside environment), indoor environments (e.g., heat generated by heaters or other equipment, human activity, etc.), inter-area energy (e.g., heat transferred from one room to another through walls).
The air conditioning equipment load prediction unit 41 predicts the electricity utilization condition of each air conditioning equipment in a future period of time according to the temperature change curve of the indoor air in the future period of time and the historical temperature information of each indoor air of the building, and synthesizes the future electricity utilization conditions of all the air conditioning equipment to obtain the load curve of the air conditioning equipment 1-n in the building.
In the step S3, when the building temperature prediction subunit predicts the temperature of each space in the building, it first establishes a building thermal model according to the building characteristics, and then establishes a thermodynamic model in the state space according to the thermal model, where the thermodynamic model takes the current indoor temperature information, the outdoor temperature, the humidity, the wind power, the sunlight intensity, and other outdoor environment information as input quantities, and calculates the temperature change curve of each indoor space for a period of time in the future.
As an embodiment, the temperature change curve may be obtained as follows.
Step 1: establishing a heat model in a building;
step 2: establishing a thermodynamic model in a state space;
and step 3: performing a schedulability test to determine if the temperature of the building has schedulability for a given electrical load budget;
and 4, step 4: running a heat control strategy based on Model Predictive Control (MPC), carrying out minimum solution on the formula (1), and solving a corresponding control output variable (temperature change condition);
wherein, the step 4 is as follows:
for the objective function represented by the formula (2), the solution is performed by adopting an integer least squares optimization problem of the solution standard under the condition that the constraint in the formula (1) is satisfied:
formula 1
Wherein,which represents a reference vector of the temperature,a matrix representing F zone air conditioner reference power input rates is shown,represents a budget reference vector, wc (k) represents a disturbance vector, L represents the number of air conditioners, N represents a prediction length,a prediction vector representing the state of the motion vector,representing state model parameters,representing the ON/OFF state of the ith air conditioning equipment in the jth area, and up (k) representing a new system input variable used after the transformation of the formula (1);
the meanings of the formulae in formula (1) are as follows:
wherein M denotes a prediction length, Yr (k + M | k) denotes a state prediction of an mth sampling interval after the kth time; x (k + M | k) represents the state prediction of the Mth sampling interval after the kth time, M represents the prediction length, N represents the prediction length, U (k + N-1| k) represents the state prediction of the N-1 th sampling interval after the kth time,representing the outside air temperature of the F-th area, X (k) representing the system state at the moment k, R representing the equivalent resistance between the two areas, psi representing the power input rate matrix of the air conditioning equipment of the F areas, G representing discrete state space model parameters, H representing discrete state space model parameters, phi representing discrete state space model parameters,representing a budget for the total power in the building,a power input rate vector representing the F-th zone air conditioning unit;
formula 2
Wherein,SQ denotes a square root matrix which is,denotes the SQ transpose matrix, SR denotes the square root matrix,represents an SR transposed matrix, and up (k) represents a new system input variable used after the transformation of the expression (10); q and R are compensation matrices for time error and input power, respectively.
The power supply strategy formulation unit 42 determines the current power supply strategy according to the load curve, the distribution network scheduling information and the real-time power consumption information of each air conditioning equipment 1-n: comparing the predicted value with a threshold value, sending an electric load regulation instruction according to the difference value between the predicted value and the threshold value, and if the predicted value is greater than or equal to the threshold value, sending the electric load regulation instruction to regulate the technical performance parameters of the air-conditioning equipment; and if the predicted value is smaller than the threshold value, supplying power to the air conditioning equipment according to the user requirement.
The air conditioning equipment power consumption information acquisition module acquires data of a plurality of air conditioning equipment simultaneously, wherein the data comprises voltage, current, active power, reactive power and electric energy.
The threshold value is determined by the power distribution network scheduling information, the building distribution line parameters and the air conditioning equipment parameters.
If the threshold is greater than the predicted value, the power supply strategy making unit specifically adopts the following method when determining the power load instruction of the air-conditioning equipment 1-n:
s11, obtaining control parameter information and power grid scheduling information, wherein the control parameter information comprises: air conditioning equipment parameter information set by a user;
s12, receiving a control instruction which is input by a user and contains a set control mode;
s13, generating a preset control instruction set according to the preset control mode, the control parameter information and the power grid scheduling information;
and S14, sending an electric load execution instruction of the air conditioning equipment according to the preset control instruction set.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several equivalent substitutions or obvious modifications, which are equivalent in performance or use, should be considered to fall within the scope of the present invention without departing from the spirit of the invention.
Claims (7)
1. A building air conditioning unit load monitoring system, the system comprising:
the air conditioning equipment power consumption information acquisition module is used for acquiring the power consumption information of each air conditioning equipment in the building in real time;
the indoor temperature acquisition module is used for acquiring the indoor environment temperature of the air conditioner;
the outdoor environment acquisition module is used for acquiring outdoor environment information such as temperature, humidity, wind power, sunlight intensity and the like outside the building;
the information storage module is used for storing the power utilization information acquired by the information acquisition module, the indoor environment temperature information acquired by the indoor temperature acquisition module and the outdoor environment information acquired by the outdoor environment acquisition module;
the distribution network communication module is used for acquiring scheduling information of the distribution network in real time and uploading relevant information of electricity utilization of the building air conditioning equipment to the distribution network;
air conditioning equipment load prediction and monitoring module includes:
the air conditioning equipment load prediction unit is used for predicting the air conditioning equipment load of the building to obtain a load curve of the air conditioning equipment;
the power supply strategy making unit is used for determining power supply strategies for all air-conditioning equipment according to the load curve and the scheduling information of the power distribution network; and
the display unit is used for displaying a power supply strategy, scheduling information and the running condition of the air conditioning equipment;
the power supply execution module is used for implementing specific power supply instructions to each air conditioning device according to the power supply strategy;
and the bus communication module is used for communication among the modules.
2. The system of claim 1, wherein the air conditioning load prediction unit comprises a building temperature prediction sub-unit that predicts the temperature of each space of the building based on the indoor environment temperature information and the outdoor environment information collected by the indoor temperature collection module.
3. The system as claimed in claim 2, wherein the building temperature prediction subunit, when predicting the temperature of each space in the building, first establishes a building thermal model according to the building self-characteristics, and then establishes a thermodynamic model in the state space according to the thermal model, and the thermodynamic model calculates the temperature change curve of each room for a future period of time by taking the current indoor temperature information, outdoor temperature, humidity, wind power, sunlight intensity and other outdoor environment information as input quantities.
4. The system as claimed in claim 3, wherein the air conditioner load prediction unit predicts the power consumption of each air conditioner in a future period of time according to the temperature change curve of the indoor in the future period of time and the historical temperature information of each indoor of the building, and integrates the future power consumption of all the air conditioners to obtain the load curve of the air conditioners in the building.
5. The system of claim 4, wherein the power supply strategy formulation unit determines the current power supply strategy according to the load curve, the distribution network scheduling information and the real-time power consumption information of each air conditioner: comparing the predicted value with a threshold value, sending an electric load regulation instruction according to the difference value between the predicted value and the threshold value, and if the predicted value is greater than or equal to the threshold value, sending the electric load regulation instruction to regulate the technical performance parameters of the air-conditioning equipment; and if the predicted value is smaller than the threshold value, supplying power to the air conditioning equipment according to the user requirement.
6. The system of claim 5, wherein the air conditioner power consumption information collecting module collects data of a plurality of air conditioners simultaneously, including voltage, current, active power, reactive power and electric energy.
7. The system of claim 6, wherein the threshold is determined collectively by distribution network scheduling information, building distribution line parameters, and air conditioning equipment parameters.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410593610.7A CN104269940B (en) | 2014-10-29 | 2014-10-29 | A kind of building air-conditioner machine utilization supervisory control system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410593610.7A CN104269940B (en) | 2014-10-29 | 2014-10-29 | A kind of building air-conditioner machine utilization supervisory control system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104269940A true CN104269940A (en) | 2015-01-07 |
| CN104269940B CN104269940B (en) | 2016-03-02 |
Family
ID=52161442
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410593610.7A Active CN104269940B (en) | 2014-10-29 | 2014-10-29 | A kind of building air-conditioner machine utilization supervisory control system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104269940B (en) |
Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105042804A (en) * | 2015-09-06 | 2015-11-11 | 成都猴子软件有限公司 | Variable-frequency intelligent air conditioning system of building |
| CN105042732A (en) * | 2015-09-06 | 2015-11-11 | 成都猴子软件有限公司 | Ultraviolet ray photocatalytic air-conditioning system |
| CN105042805A (en) * | 2015-09-06 | 2015-11-11 | 成都猴子软件有限公司 | Building air conditioner intelligent system having anti-freezing warning function |
| CN105066371A (en) * | 2015-09-06 | 2015-11-18 | 成都猴子软件有限公司 | Plasma purification type air conditioner system |
| CN105091246A (en) * | 2015-09-06 | 2015-11-25 | 成都猴子软件有限公司 | Automatic adjustment type building air conditioner control system |
| CN105157174A (en) * | 2015-09-06 | 2015-12-16 | 成都猴子软件有限公司 | Multi-mode building air conditioner control system |
| CN105159259A (en) * | 2015-09-06 | 2015-12-16 | 成都猴子软件有限公司 | Intelligent system of building air conditioner |
| CN106203702A (en) * | 2016-07-11 | 2016-12-07 | 深圳库博能源科技有限公司 | A kind of cold load autocontrol method of electrically-based requirement prediction |
| CN109031940A (en) * | 2018-08-08 | 2018-12-18 | 中冶东方工程技术有限公司 | More set equipment or unit share the control method for coordinating and system of a power source drive |
| CN109028278A (en) * | 2018-07-17 | 2018-12-18 | 哈尔滨工业大学 | A kind of the area operation system and scheduling strategy of wind power heating |
| CN110348634A (en) * | 2019-07-12 | 2019-10-18 | 广东电网有限责任公司 | A kind of air conditioner load dispatching method based on formula Energy Management System of registering one's residence |
| CN110929909A (en) * | 2018-09-19 | 2020-03-27 | 清华大学 | Air source heat pump user electrical load prediction method |
| CN111600304A (en) * | 2020-06-17 | 2020-08-28 | 广东工业大学 | A building power dispatching method, device and equipment |
| CN112083746A (en) * | 2020-09-25 | 2020-12-15 | 陕西科技大学 | Intelligent power grid system |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5197666A (en) * | 1991-03-18 | 1993-03-30 | Wedekind Gilbert L | Method and apparatus for estimation of thermal parameter for climate control |
| CN102723785A (en) * | 2012-07-03 | 2012-10-10 | 广东电网公司电力科学研究院 | Electric power load prediction method and electric power load prediction system based on collection of larger user intelligent terminal |
| CN102721156A (en) * | 2012-06-30 | 2012-10-10 | 李钢 | Central air-conditioning self-optimization intelligent fuzzy control device and control method thereof |
| CN103268115A (en) * | 2013-06-14 | 2013-08-28 | 鲁电集团有限公司 | Power demand side monitoring system and method |
| CN103727639A (en) * | 2014-01-09 | 2014-04-16 | 山东建筑大学 | Central air conditioner energy saving system and method based on body |
| CN104035409A (en) * | 2014-06-04 | 2014-09-10 | 国家电网公司 | Demand response system oriented to building energy optimizing operation |
-
2014
- 2014-10-29 CN CN201410593610.7A patent/CN104269940B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5197666A (en) * | 1991-03-18 | 1993-03-30 | Wedekind Gilbert L | Method and apparatus for estimation of thermal parameter for climate control |
| CN102721156A (en) * | 2012-06-30 | 2012-10-10 | 李钢 | Central air-conditioning self-optimization intelligent fuzzy control device and control method thereof |
| CN102723785A (en) * | 2012-07-03 | 2012-10-10 | 广东电网公司电力科学研究院 | Electric power load prediction method and electric power load prediction system based on collection of larger user intelligent terminal |
| CN103268115A (en) * | 2013-06-14 | 2013-08-28 | 鲁电集团有限公司 | Power demand side monitoring system and method |
| CN103727639A (en) * | 2014-01-09 | 2014-04-16 | 山东建筑大学 | Central air conditioner energy saving system and method based on body |
| CN104035409A (en) * | 2014-06-04 | 2014-09-10 | 国家电网公司 | Demand response system oriented to building energy optimizing operation |
Cited By (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105042804A (en) * | 2015-09-06 | 2015-11-11 | 成都猴子软件有限公司 | Variable-frequency intelligent air conditioning system of building |
| CN105042732A (en) * | 2015-09-06 | 2015-11-11 | 成都猴子软件有限公司 | Ultraviolet ray photocatalytic air-conditioning system |
| CN105042805A (en) * | 2015-09-06 | 2015-11-11 | 成都猴子软件有限公司 | Building air conditioner intelligent system having anti-freezing warning function |
| CN105066371A (en) * | 2015-09-06 | 2015-11-18 | 成都猴子软件有限公司 | Plasma purification type air conditioner system |
| CN105091246A (en) * | 2015-09-06 | 2015-11-25 | 成都猴子软件有限公司 | Automatic adjustment type building air conditioner control system |
| CN105157174A (en) * | 2015-09-06 | 2015-12-16 | 成都猴子软件有限公司 | Multi-mode building air conditioner control system |
| CN105159259A (en) * | 2015-09-06 | 2015-12-16 | 成都猴子软件有限公司 | Intelligent system of building air conditioner |
| CN106203702B (en) * | 2016-07-11 | 2020-07-28 | 深圳库博能源科技有限公司 | Cold machine load automatic control method based on power demand prediction |
| CN106203702A (en) * | 2016-07-11 | 2016-12-07 | 深圳库博能源科技有限公司 | A kind of cold load autocontrol method of electrically-based requirement prediction |
| CN109028278A (en) * | 2018-07-17 | 2018-12-18 | 哈尔滨工业大学 | A kind of the area operation system and scheduling strategy of wind power heating |
| CN109028278B (en) * | 2018-07-17 | 2020-07-31 | 哈尔滨工业大学 | A regional operation system and scheduling strategy for wind power heating |
| CN109031940A (en) * | 2018-08-08 | 2018-12-18 | 中冶东方工程技术有限公司 | More set equipment or unit share the control method for coordinating and system of a power source drive |
| CN109031940B (en) * | 2018-08-08 | 2022-02-15 | 中冶东方工程技术有限公司 | Coordination control method and system for multiple sets of equipment or units to share one power source for driving |
| CN110929909A (en) * | 2018-09-19 | 2020-03-27 | 清华大学 | Air source heat pump user electrical load prediction method |
| CN110348634A (en) * | 2019-07-12 | 2019-10-18 | 广东电网有限责任公司 | A kind of air conditioner load dispatching method based on formula Energy Management System of registering one's residence |
| CN111600304A (en) * | 2020-06-17 | 2020-08-28 | 广东工业大学 | A building power dispatching method, device and equipment |
| CN111600304B (en) * | 2020-06-17 | 2021-11-26 | 广东工业大学 | Building power scheduling method, device and equipment |
| CN112083746A (en) * | 2020-09-25 | 2020-12-15 | 陕西科技大学 | Intelligent power grid system |
| CN112083746B (en) * | 2020-09-25 | 2022-01-28 | 陕西科技大学 | Intelligent power grid system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104269940B (en) | 2016-03-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104319766B (en) | A kind of building air-conditioner equipment is powered and method for supervising | |
| CN104269940B (en) | A kind of building air-conditioner machine utilization supervisory control system | |
| CN104279715B (en) | A kind of building air-conditioner machine utilization Forecasting Methodology | |
| Zong et al. | Application of model predictive control for active load management in a distributed power system with high wind penetration | |
| CN109063925B (en) | An optimal operation method of regional comprehensive energy system considering load aggregators | |
| US9037307B2 (en) | Supply-and-demand control apparatus, supply-and-demand control method, and supply-and-demand control system | |
| Kondoh et al. | An evaluation of the water heater load potential for providing regulation service | |
| Zong et al. | Model predictive controller for active demand side management with PV self-consumption in an intelligent building | |
| WO2022018680A1 (en) | Micro-grid, energy management system and method | |
| CN104184214B (en) | A kind of building power load method for supervising | |
| CN215724029U (en) | Central air conditioning adaptive control system | |
| CN106096747B (en) | A solar-assisted home energy management method considering multiple uncertain factors in a real-time electricity price environment | |
| KR20190067476A (en) | System and method for routing of heat and electric energy, and energy router | |
| CN115342409A (en) | Control and regulation method and system for heating system | |
| Lu et al. | The potential of thermostatically controlled appliances for intra-hour energy storage applications | |
| CN103855807A (en) | System, method and execution module capable of achieving domestic intelligent power grid management | |
| Marinova et al. | IoT Approach for Improving the Energy Efficiency in the Durres Port Authority Buildings | |
| Martirano et al. | Smart micro grids for nearly zero energy buildings | |
| CN113531512A (en) | Load side electricity heat accumulation boiler frequency modulation system | |
| JP2015169351A (en) | Information processing device and information processing system | |
| CN112436607A (en) | Town energy Internet system | |
| KR20220026387A (en) | Energy management system using environment and status information of renewable power generation | |
| Vašak et al. | Upgrade of a typical office building automation system for enabling open energy management services | |
| Molina-Garcia et al. | Application of wireless sensor network to direct load control in residential areas | |
| CN212808940U (en) | Regional multi-energy supply system with demand response capability |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant |