CN111025895A - Building energy-saving control system based on artificial intelligence - Google Patents
Building energy-saving control system based on artificial intelligence Download PDFInfo
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- CN111025895A CN111025895A CN201910321018.4A CN201910321018A CN111025895A CN 111025895 A CN111025895 A CN 111025895A CN 201910321018 A CN201910321018 A CN 201910321018A CN 111025895 A CN111025895 A CN 111025895A
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- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
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Abstract
The invention discloses a building energy-saving control system based on artificial intelligence, which comprises a building management system, internet data, an artificial intelligence module and an energy station control system. The intelligent energy station system is characterized in that historical and real-time data such as the setting and operation states of a building management system, an external internet and energy station equipment are obtained through artificial intelligence, and optimal setting and control instructions of the energy station equipment are learned through an artificial intelligence module under the conditions of past building use and external weather conditions. Meanwhile, the artificial intelligence predicts the future use condition of the building based on the data. The trained artificial intelligence is combined with the future service condition prediction and weather prediction data of the building, optimized equipment setting and operation commands are sent to the energy station control system, and the running state of the energy station equipment is adjusted, so that the purpose of energy conservation is achieved.
Description
Technical Field
The invention relates to the technical field of building energy conservation, in particular to a building energy conservation control system based on artificial intelligence.
Background
In modern buildings, centralized energy stations are frequently adopted to supply air conditioners, heating, hot water and the like to the whole building so as to ensure the comfort of the interior of the building. The most important energy consumption in such buildings comes from energy stations: consumption of electric power and natural gas energy of electric refrigerating machines, gas generators, lithium bromide refrigerating machines, gas boilers and auxiliary equipment. At present, the control mode of the energy station equipment is mainly manual setting and operation instruction sending. At present, energy-saving measures are usually carried out manually according to historical electricity and natural gas data, and energy station equipment setting and operation instructions are set by combining working experience.
However, in the conventional method, the operation parameters and the operation instructions set for the energy station equipment cannot take the influence of various factors such as the use condition of the building and the external weather into consideration, and cannot effectively predict the weather based on future weather prediction data.
Disclosure of Invention
The invention aims to solve the problems and provides an artificial intelligence-based building energy-saving control system, which can realize the energy saving of a building by improving the control level of an energy source station while ensuring the comfort level in the building.
In order to achieve the purpose, the invention is realized by the following technical scheme: the invention provides an artificial intelligence-based building energy-saving control system, which comprises a building management system, internet data, an artificial intelligence module and an energy station control system. The intelligent energy station control system is characterized in that the artificial intelligence module obtains historical and real-time data of the building management system, the external Internet and the energy station control system, and learns the optimal equipment setting and control command of the energy station under the conditions of the building management system and the external Internet data by using an artificial intelligence algorithm. The artificial intelligence algorithm generates forecast data for future building usage based on historical data of the building management system. And the artificial intelligence module calculates the optimal control data of the energy station according to the prediction data of the building management system and the future weather prediction data, sends an instruction and adjusts the working state of the energy station equipment so as to achieve the aim of saving energy.
Further, the building management system data includes data such as occupancy, temperature, humidity, and power consumption of each area.
Further, the internet data comprises historical weather, wind speed, temperature and humidity data of the area where the building is located, and temperature, humidity, rainfall and wind speed data of weather forecast.
Further, the input of the energy station control system to the artificial intelligence system should include the setting and operation data of an electric refrigerator, a gas generator, a lithium bromide refrigerator, a gas boiler, a chilled water pump and a cooling water pump in the energy station system.
Further, the electric refrigerator operation data should include the present apparatus: current, load factor, chilled water inlet and outlet temperature and flow, and cooling water inlet and outlet temperature and flow data.
Further, the gas generator operation data should include: generating capacity, natural gas flow, cooling water inlet and outlet temperature and flow, load rate and other parameters.
Further, the lithium bromide refrigerator operating data should include the following data for the present apparatus: flue gas inlet temperature, flow, power consumption, chilled water inlet and outlet flow and temperature, and cooling water inlet and outlet flow and temperature data.
Further, the gas boiler operation data should contain the following data of the present apparatus: natural gas consumption, load factor, cold water inlet temperature flow, hot water outlet temperature flow and steam outlet temperature flow.
Further, the cooling water pump and the chilled water pump comprise the following data of the equipment: power, current, start-stop status data.
Furthermore, the artificial intelligence module establishes calculable relation data according to the operation states of the building management system data, the internet data and the energy station at each moment. And according to the computable data, learning optimal setting and operation instructions for the equipment in the energy station.
Furthermore, the artificial intelligence module forms prediction data of building use for the building use condition of the past building management system.
Further, the artificial intelligence module forms an optimal setting and operation instruction for the energy station equipment according to the building use prediction data and the weather prediction data, and sends the optimal setting and operation instruction to the energy station control or directly sends the optimal setting and operation instruction to the equipment controller in the energy station to change the setting and operation state of the equipment.
Drawings
FIG. 1 is a diagram of a building energy-saving control system based on artificial intelligence.
Detailed Description
Referring to the attached drawings, the invention discloses a building energy saving system based on artificial intelligence. The system comprises an artificial intelligence module 3, a building management system 1 for providing input data for artificial intelligence calculation, internet data 2 and an energy station control system 4. The energy station control system 4 has the functions of sending setting and running commands to the electric refrigerator 5, the gas generator 6, the lithium bromide refrigerator 7, the gas turbine boiler 8 and the auxiliary equipment 9, and recording setting and running instructions of the system equipment.
The building management system 1 is connected with the artificial intelligence module 3 through the internet, and signals transmitted to the artificial intelligence module 3 comprise building check-in rate, service conditions of all rooms of the building, temperature and humidity of all areas of the building, data of electric meters of all areas, monthly electric charge bills, monthly gas bills and switching data of illumination of all areas.
The internet data 2 is connected with the artificial intelligence module 3, and the data transmitted to the artificial intelligence module 3 comprises weather data of the past ten years of the local area, including data of temperature, humidity, wind speed and the like in each hour; and weather data of 15 days in the future, such as temperature, humidity, rainfall, wind speed, illumination condition and the like.
The energy station control system 4 is connected with the artificial intelligence module 3. The inputs to the artificial intelligence system should include the following data for the electric refrigerator 5: current, load factor, chilled water inlet and outlet temperature, flow, cooling water inlet and outlet temperature, flow and the like; the following data for the gas generator 6: generating capacity, natural gas flow, cooling water inlet and outlet temperature flow, load rate and the like; the following data for lithium bromide refrigerator 7: flue gas inlet temperature, flow, electricity consumption, chilled water inlet and outlet flow, temperature, cooling water inlet and outlet flow, temperature and the like; the following data for the combustion engine boiler 8: natural gas consumption, load rate, cold water inlet temperature flow, hot water outlet temperature flow, steam outlet temperature flow and the like; the following data for the auxiliary device 9: the power, the current, the starting and stopping conditions and the like of the freezing water pump and the cooling water pump.
The artificial intelligence module 3 collects data of the building management system 1, the internet data 2 and the energy station control system 4. The steps of the artificial intelligence module 3 adopting the neural network prediction model to calculate comprise: and establishing calculable relation data aiming at the building management system data, the internet data and the running state of the energy station in the past every hour. And according to the computable data, learning optimal setting and operation instructions for the equipment in the energy station. It should be understood that the description of the parameter optimization implementation by using the artificial intelligence module 3 as an example of the parameter optimization control module and the neural network prediction model as an artificial intelligence calculation model in the present embodiment is only exemplary, and other artificial intelligence models, such as a vector machine-based statistical learning system and a deep learning system, are all suitable for the present invention.
The artificial intelligence module 3 calculates prediction data of future building use conditions of the building management system 1 in each hour in the past, including the check-in rate, the temperature, the humidity, the electric power and the natural gas consumption data of each area. And the artificial intelligence calculates the optimal setting and operation instruction of the energy station equipment based on the prediction data of the future building use condition and the weather prediction data, and sends the optimal setting and operation instruction to the energy station control system. It should be understood that the example in which the artificial intelligence module 3 sends the setting and operation command to the energy station control system 4 to change the operation state of the equipment is only exemplary, and other methods, such as the artificial intelligence directly sending the setting and operation command to the energy station equipment, changing the operation state of the equipment, etc., are all suitable for the present invention.
The command of the artificial intelligence module 3 to the energy station control system 4 is a setting and running instruction for each device, and comprises the following steps: the load factor of the electric refrigerator 5, the chilled water outlet temperature, and the like; the load factor, the natural gas flow rate, the air intake amount and the like of the gas generator 6; the electric quantity, the load factor, the chilled water outlet temperature, and the like of the lithium bromide refrigerator 7; the natural gas flow rate, the air supply amount, the load factor, the temperature of the heating water, the pressure of the heating steam, and the like of the gas boiler 8; the number of the start and stop of the freezing water pump, the number of the start and stop of the cooling water pump and the like in the auxiliary equipment 9.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (8)
1. A building energy-saving control system based on artificial intelligence comprises a building management system, Internet data, an artificial intelligence module and an energy station control system, and is characterized in that the artificial intelligence module acquires historical and real-time data of the building management system, an external Internet and the energy station control system, and learns the optimal equipment setting and operation instructions of an energy station under the conditions of the building management system and the external Internet data by using an artificial intelligence algorithm; the artificial intelligence algorithm generates prediction data for future building use conditions based on the building management system data; and the artificial intelligence module calculates the optimal setting and operation instruction of the energy station according to the prediction data of the building management system and the future weather prediction data, sends the instruction and adjusts the working state of the energy station equipment so as to achieve the aim of saving energy.
2. The artificial intelligence based building energy conservation control system of claim 1, wherein: the building is provided with a centralized energy station, and the energy station is provided with at least equipment such as an electric refrigerator, a chilled water pump, a cooling water pump and the like.
3. The artificial intelligence based building energy conservation control system of claim 1, wherein: the building management system data comprises data such as building check-in rate, electricity consumption of electric meters in each area, temperature and humidity of each area and the like.
4. The artificial intelligence based building energy conservation control system of claim 1, wherein: the internet data comprises historical weather data such as the temperature, the humidity and the wind speed of the area and predicted data of the temperature, the humidity, the rainfall and the wind speed of future weather prediction.
5. The artificial intelligence based building energy conservation control system of claim 1, wherein: the artificial intelligence module learns the optimal control mode of the energy station equipment through the building use data, the internet data and the setting and operation instructions of the energy station equipment of the building management system; the artificial intelligence module calculates the forecast data of the building management system according to the historical data of the building management system; and the artificial intelligence module calculates the optimal setting and operation instruction of the energy station according to the forecast data of the building management system and the forecast data of the future weather, so as to realize the optimal control of the equipment.
6. The energy station set up and operation instructions of claim 5, wherein the set up and operation instructions comprise: the chilled water outlet temperature, the load factor and other data of the electric refrigerator; data such as load factor, natural gas flow and air inflow of the gas generator; data such as the electric quantity, the load factor, the outlet temperature of the chilled water and the like of the lithium bromide refrigerator; data such as natural gas flow, air supply quantity, load factor, temperature of heating water, pressure of heating steam and the like of the gas-fired boiler; and the power of a freezing water pump, the power of a cooling water pump and the like in the auxiliary equipment.
7. The artificial intelligence based building energy conservation control system of claim 1, wherein: the energy station operation data comprises electric refrigerator current, load rate, chilled water inlet and outlet temperature and flow, cooling water inlet and outlet temperature and flow and the like; the energy station operating data comprises a gas generator: generating capacity, natural gas flow, cooling water inlet and outlet temperature and flow, load rate and the like; the energy station operating data comprises a lithium bromide refrigerator: the temperature and the flow of a flue gas inlet, the electricity consumption, the temperature and the flow of a chilled water inlet and outlet, the temperature and the flow of a cooling water inlet and outlet and the like; the energy station operating data comprises a gas boiler: natural gas flow, load factor, cold water inlet temperature and flow, hot water outlet temperature and flow, steam outlet temperature and flow, and the like; the energy station operating data comprises auxiliary systems: the power, the current, the starting and stopping conditions and the like of the cooling water pump and the freezing water pump.
8. The artificial intelligence based building energy conservation control system of claim 1, wherein: the artificial intelligence module can send the setting and the operation instruction of the energy station equipment to the energy station management system and can also directly send the controller of the energy station equipment to adjust the working state of the energy station equipment so as to achieve the aim of saving energy.
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| CN201910321018.4A CN111025895A (en) | 2019-04-21 | 2019-04-21 | Building energy-saving control system based on artificial intelligence |
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| CN201910321018.4A CN111025895A (en) | 2019-04-21 | 2019-04-21 | Building energy-saving control system based on artificial intelligence |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112996090A (en) * | 2021-01-21 | 2021-06-18 | 西藏先锋绿能环保科技股份有限公司 | Energy-saving management system and method |
| CN113194652A (en) * | 2021-03-11 | 2021-07-30 | 浙江中创节能科技有限公司 | Assembly type intelligent high-performance source station and configuration method |
| CN113219897A (en) * | 2021-05-28 | 2021-08-06 | 沈阳恒久安泰环保与节能科技有限公司 | Cold and heat combined supply intelligent regulation and control system and method based on big data and artificial intelligence |
| CN113776122A (en) * | 2021-09-29 | 2021-12-10 | 北京百度网讯科技有限公司 | Heating control method, device, equipment, medium and product |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN106020036A (en) * | 2016-06-24 | 2016-10-12 | 山东华旗新能源科技有限公司 | Smart energy management system and method |
| CN106295900A (en) * | 2016-08-19 | 2017-01-04 | 中节能（常州）城市节能研究院有限公司 | A kind of city intelligent energy management system |
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2019
- 2019-04-21 CN CN201910321018.4A patent/CN111025895A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN106020036A (en) * | 2016-06-24 | 2016-10-12 | 山东华旗新能源科技有限公司 | Smart energy management system and method |
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112996090A (en) * | 2021-01-21 | 2021-06-18 | 西藏先锋绿能环保科技股份有限公司 | Energy-saving management system and method |
| CN112996090B (en) * | 2021-01-21 | 2022-08-23 | 西藏先锋绿能环保科技股份有限公司 | Energy-saving management system and method |
| CN113194652A (en) * | 2021-03-11 | 2021-07-30 | 浙江中创节能科技有限公司 | Assembly type intelligent high-performance source station and configuration method |
| CN113219897A (en) * | 2021-05-28 | 2021-08-06 | 沈阳恒久安泰环保与节能科技有限公司 | Cold and heat combined supply intelligent regulation and control system and method based on big data and artificial intelligence |
| CN113776122A (en) * | 2021-09-29 | 2021-12-10 | 北京百度网讯科技有限公司 | Heating control method, device, equipment, medium and product |
| CN113776122B (en) * | 2021-09-29 | 2022-08-12 | 北京百度网讯科技有限公司 | Heat supply control method, device, equipment, medium and product |
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