TW201816337A - Method for dynamically forecasting external air and load intelligent energy saving control - Google Patents

Abstract

An embodiment of the invention discloses a method for dynamically forecasting external air and load intelligent energy saving control. According to the method of the invention, according to a machine room air conditioning load requirement, by means of data which are supplied from an external data platform, an external weather change trend is obtained through analytical calculation; a machine room load rate trend is analyzed through load trend analysis; and finally through evaluating and analyzing whether the external weather condition satisfies a requirement, external air cooling benefit optimization is realized. According to the method, by means of the forecast external air weather condition, a fact that an external air cooling system should be started or stopped is obtained through calculation and analysis so that a machine room air conditioning load effectively utilizes external air cooling for reducing operation energy consumption of an air conditioning host. Furthermore according to the machine room equipment load change trend, the equipment load change amount is intelligently learned, thereby improving external air cooling benefit.

Description

   Intelligent energy-saving control method for dynamically predicting outside air and load   

The invention is a method for intelligently controlling external air equipment based on real-time environmental information, and particularly is a smart energy-saving control method for dynamically predicting external air and load.

With the increasing awareness of energy conservation and carbon reduction, how to improve the power usage efficiency (PUE) of the computer room is an important energy saving measure. At present, in the prior art, the outside air cooling system is turned on or off by the outside air sensor to determine that the outside air condition does not meet the requirements. When the outside air temperature rises rapidly, it is easy to increase the load of the air conditioning in the machine room, and the air conditioning system is started to stabilize. Cooling takes a period of time (about 20 to 30 minutes), making the machine room more energy-consuming and less stable.

It can be seen that the above-mentioned conventional methods still have poor system availability, which is not a convenient and easy-to-use design that needs to be improved.

The invention provides a smart energy-saving control method for dynamically predicting outside air and load, which includes: a device and a sensor communication module receiving environmental information of a connected computer room electricity and environmental monitoring module; and transmitting environmental information Enter a smart load trend analysis module to generate a load forecast value; a climate prediction analysis module receives climate observation information and generates a first time outside air climate condition and a second time outside air climate condition; one outside The air cooling benefit evaluation module compares the load forecast value, the outside air climatic conditions at the first time and the outside air climatic conditions at the second time; when an outdoor air cooling device is already started, if the load prediction value meets the first time When the outside air climate conditions, the outside air cooling equipment is kept in the starting state, if the load forecast value does not meet the first outside air climate conditions, the outside air cooling equipment is disabled; and when the outside air cooling equipment is in the disabled state If the load forecast value meets both the first-time outside air weather conditions and the second-time outside air climate conditions, start the outdoor air cooling equipment. When any of the first-time outside air climate conditions and the second-time outside air climate conditions are met, the outside air cooling device is kept in a disabled state.

The environmental information is the set temperature for the temperature, humidity, and requirements of the computer room.

The smart load trend analysis module performs a regression analysis on environmental information to generate a load forecast value.

The climatic conditions at the second time are larger than those at the first time.

The first time outside air climatic conditions are predicted for 30 minutes, and the second time outside air climatic conditions are predicted for 60 minutes.

The invention receives the meteorological data provided by an external meteorological data platform (such as the Meteorological Bureau), uses the predicted climate as the boundary value, and uses the calculation function analysis to obtain the outside air climate conditions after 30 minutes and 60 minutes. The group used the regression analysis formula of the load rate to predict the change trend of the load rate after 30 minutes. Provide the predicted outside air climatic conditions and load prediction values to the outside air cooling benefit evaluation module. Based on the current predicted outside air climatic conditions and load prediction values, evaluate and analyze that the current outside air cooling equipment should be on or off. This increases the utilization rate of the outdoor air cooling equipment to reduce the operating hours of the air conditioning host. At the same time, before the outdoor climate is not suitable for operation, turn off the outdoor air cooling equipment and turn on the air conditioning host in advance to respond to the air conditioning load in the computer room, and improve the overall equipment operation stability. Match equipment room equipment load trends, and intelligently learn equipment load changes and evaluate and predict load load trends.

The above detailed description is a specific description of a feasible embodiment of the present invention, but this embodiment is not intended to limit the patent scope of the present invention. Any equivalent implementation or change that does not depart from the technical spirit of the present invention should be included in Within the scope of the patent in this case.

To sum up, this case is not only innovative in terms of space type, but also enhances the above-mentioned multiple effects over conventional items. It should have fully met the requirements for statutory invention patents that are novel and progressive. Apply for it in accordance with the law and ask your office for approval. This invention patent application is designed to encourage inventions, and it is a matter of virtue.

101‧‧‧ Climate Forecast Analysis Module

102‧‧‧Smart Load Trend Analysis Module

103‧‧‧Outdoor air cooling benefit evaluation module

104‧‧‧device and sensor communication module

105‧‧‧machine room electricity and environmental monitoring module

S201 ~ S203‧‧‧step flow

S301 ~ S306‧‧‧step flow

FIG. 1 is a schematic diagram of a module for a smart energy-saving control method of dynamic outdoor air and load according to the present invention.

FIG. 2 is a schematic diagram of a process for predicting the outside weather conditions of the present invention.

FIG. 3 is a schematic flowchart of a dynamic energy-saving control method for external air and load according to the present invention.

In order for the reviewing committee members to understand the technical features, contents and advantages of the present invention and the effects that can be achieved, the present invention is described in detail with the accompanying drawings in the form of embodiments, and the drawings used therein are The main purpose is only for the purpose of illustration and supplementary description. It may not be the actual proportion and precise configuration after the implementation of the invention. Therefore, the attached drawings should not be interpreted and limited to the scope of rights of the present invention in actual implementation. He Xianming.

Please refer to FIG. 1, which is a schematic diagram of a module for a method for controlling intelligent energy conservation of dynamic outdoor air and load according to the present invention, which includes a climate prediction analysis module 101, a smart load trend analysis module 102, an external air cooling benefit evaluation module 103, and equipment. The communication module 104 with the sensor and the electricity consumption and environmental monitoring module 105 in the equipment room. Among them, the equipment and sensor communication module 104 is used to collect all the equipment room electricity and environment information (machine room temperature, room humidity) through the equipment room electricity and environment monitoring module 105 and provide the environmental information to the intelligent load trend analysis. The module 102 generates a load prediction value and provides it to the external air cooling benefit evaluation module 103. The climate prediction analysis module 101 provides the analysis and prediction of the outside air weather conditions to the outside air cooling benefit evaluation module 103. The outside air cooling benefit evaluation module 103 controls the startup or deactivation of the outside air cooling device through the device and sensor communication module 104 based on the analysis and judgment results.

Please refer to FIG. 2, which is a schematic diagram of the process of forecasting outside-climate conditions of the present invention. The steps are as follows: S201: Retrieve the XML data of meteorological observation data and the weather of the town from the weather forecast analysis module to an external weather data platform (such as the Meteorological Bureau) XML file of forecast data; S202: filtering and analyzing the observed climate conditions, filtering the XML file data of meteorological observation data and the XML file data of township weather forecast data; S203: using the forecast data time as the boundary value, and using 30 minutes and 60 minutes later Interpolate the ratio of time to predicted boundary value time to predict climatic conditions (such as temperature and humidity), predict climatic conditions after 30 minutes, and climatic conditions after 60 minutes.

An example of filtering analysis is as follows: Filter the weather observation data XML file to find the climate data (temperature and humidity values of the measurement record at 9:30) measured at the current time (9:30). From the township weather forecast data XML file data Filter to find the climate data and time in the future (12:00) (predict the temperature and humidity values at 12:00), and finally use the analysis and forecast climate calculation to take the forecast data time as the boundary value, and use 30 minutes and 60 minutes Interpolate the ratio of the later time to the predicted boundary value time to predict the climate conditions (such as temperature and humidity), and predict the climate conditions after 30 or 60 minutes. The analysis function relationship is as follows: W _{P, 30 min} = f _{P, 30 min} ( x, y , t _now )

W _{P, 60 min} = f _{P, 60 min} ( x, y , t _now )

x : Observed climatic conditions after filtering analysis; y: Forecast climatic conditions after filtering analysis; t _now : DateTime; f _{P, 30 min} : Climate prediction function after 30 minutes; f _{P, 60 min} : 60 minutes Post-climate prediction function; W _{P, 30 min} : predicts climatic conditions after 30 minutes; W _{P, 60 min} : predicts climatic conditions after 60 minutes.

Based on the intelligent load trend analysis module of the present invention, the power consumption information of the computer room received in a certain period of time (5 minutes) is stored in the database, and the regression analysis load factor prediction formula (multivariate linear equation) is performed. Finally, The current values of P _IT , P _{IT, Max} , and t _now are substituted into the multivariate linear equations from the regression analysis, and the predicted load value after 30 minutes is predicted. The functional relationship is as follows: R _{IT, P} = f _P ( P _IT , P _{IT, Max} , t _now )

R _{IT, P} : Predict the load factor (%) after 30 minutes; f _P : Load rate prediction function; P _IT : Equipment power consumption (kW); P _{IT, Max} : Maximum equipment power consumption (kW); t _now : Now Time (DateTime).

The external air cooling benefit evaluation module is based on the temperature setting of the computer room, the humidity of the computer room, the predicted outside air climatic conditions, the load forecast value, and the temperature required by the computer room. The functional relationship is as follows: T _{wb, OA 100%} = T _{IT, Need} - T _const

T _{wb, OA 100%} : Wet bulb temperature (℃) outside the IT load factor 100%

T _{IT, Need} : set value of required temperature of machine room (℃)

T _const : temperature constant (° C)

T _{wb, OA Need} ： Required wet air condition (℃)

R _{IT, P} : predict IT load rate after 30 minutes (%)

T _ds : IT load rate temperature step (℃)

T _IT : current temperature of the equipment room (℃)

Climate meets definition: T _{wb, OA Need} - T _{wb, OA Prediction} > 0.5 ℃

T _{wb, OA Need} ： Required wet air condition (℃)

T _{wb, OA Prediction} : _Unexpected gas wet bulb temperature (° C).

Please refer to FIG. 3, which is a schematic flow chart of the dynamic energy-saving control method for outdoor air and load according to the present invention, including: S301: a device and sensor communication module receives a computer room electricity and environment monitoring module One environmental information; S302: sending environmental information to a smart load trend analysis module to generate a load forecast value; S303: a climate prediction analysis module receives climate observation information and generates a first-time outside air climate Conditions and a second time outside air climate condition; S304: an external air cooling benefit evaluation module that compares the load prediction value, the first time outside air climate condition and the second time outside air climate condition; S305: When a The outdoor air cooling equipment is already in the starting state. If the predicted load value meets the outdoor air weather conditions at the first time, the external air cooling equipment is kept in the startup state. If the predicted load value does not meet the outdoor air weather conditions at the first time, the equipment will be stopped. Use external air to cool the equipment; and S306: When the external air cooling equipment is in the disabled state, if the load prediction value meets both the first-time outer-air climate conditions and the second-time outer-air climate When the conditions are met, the outdoor air cooling equipment is started. If the load prediction value does not meet any of the first time outdoor air climate conditions and the second time outdoor air climate conditions, the outdoor air cooling equipment is kept in a disabled state.

The following is a practical example: A house is located in Zhongli District, Taoyuan City, and the time is now 09:30.

The program of the climate prediction analysis module 101 retrieves the XML file of the meteorological observation data and the XML data file of the weather forecast data of the township from the meteorological bureau data platform, and filters and analyzes the weather conditions to perform the XML observation data XML. The data was filtered to obtain the current temperature and humidity of Zhongli District at the location of the crime scene at 09:30. The current temperature and humidity were 22.5 ° C and 76%. The township weather forecast was filtered by filtering and forecasting the climate conditions. The humidity and humidity were 26 ℃ and 62%, and the climate information obtained by filtering was used to analyze and predict the temperature and humidity of the climate area after 30 minutes and 60 minutes, respectively, at 23.2 ℃ and 73.2%; 23.9 ℃ and 70.4%.

The intelligent load trend analysis module 102 obtains the current IT equipment power consumption from the computer room is 23.9kW, the maximum equipment power consumption is 36kW, and the current time. The obtained power consumption information is stored in the database, and the program automatically uses the historical power consumption of the database. The information is used to perform regression analysis on the load rate prediction formula. According to the current time of 09:30 and the current power consumption information of the computer room, the load rate is predicted to be 63.8% after 30 minutes.

Set the demand temperature of the equipment room to 27 ° C, and obtain the forecast data of the climate forecast analysis module 101 (the temperature and humidity of Zhongli District after 30 minutes and 60 minutes are 23.2 ° C, 73.2%; 23.9 ° C, 70.4%), wisdom Load trend analysis module 102 forecast data (load rate is 63.8% after 30 minutes) and room temperature and humidity (temperature and humidity are 28.3 ° C and 48.4%, respectively). The demand outside air wet bulb temperature is calculated to analyze the demand outside air. The wet-bulb temperature is 20.45 ° C. After 30 minutes, the outside-air wet-bulb temperature is 19.77 ° C. After 60 minutes, the outside-air wet-bulb temperature is 20.02 ° C. If the state A external air cooling system is currently operating, the state B external air cooling system is currently stopped. in.

In the A state, because the outside air climate is compatible after 30 minutes, a signal to activate the outside air cooling system is sent.

In state B, the outside air climate is compatible after 30 minutes but the outside air climate is not compatible after 60 minutes, so a signal is sent to disable the outside air cooling system.

To sum up, this case is not only innovative in terms of technical ideas, but also has many of the above-mentioned effects that are not used by traditional methods. It has fully met the requirements of statutory invention patents that are novel and progressive. To approve this invention patent application, to encourage invention, to the utmost convenience.

Claims (5)

    A smart energy-saving control method for dynamically predicting outside air and load includes: a device and sensor communication module receives environmental information of a computer room electricity and environmental monitoring module; and sends the environmental information to a The intelligent load trend analysis module generates a load forecast value; a climate prediction analysis module receives climate observation information and generates a first time outside air climate condition and a second time outside air climate condition; an outside air cooling The benefit evaluation module compares the load forecast value, the first-time outside air climatic conditions, and the second-time outside air climatic conditions; when an outdoor air cooling device is already started, if the load prediction value meets the first When the outdoor air weather conditions are maintained for a period of time, the outdoor air cooling equipment is maintained in a startup state, and if the load prediction value does not meet the first time outdoor air weather conditions, the outdoor air cooling equipment is disabled; and When the cooling device is in a disabled state, if the load prediction value simultaneously meets the first-time outside-air climate conditions and the second-time outside-air climate conditions, the outside-air cooling device is started. , If the predicted load value does not match the first time outside air climatic conditions, the second time outside air climatic conditions wherein any one time, the outside air is maintained in a deactivated state cooling device.         The intelligent energy-saving control method for dynamic prediction of outside air and load as described in item 1 of the scope of patent application, wherein the environmental information is the temperature of the computer room, the humidity of the computer room, and the required temperature of the computer room.         The intelligent energy-saving control method for dynamically predicting outside air and load as described in the first item of the patent application scope, wherein the intelligent load trend analysis module performs regression analysis on the environmental information to generate the load prediction value.         As described in item 1 of the scope of the patent application, the intelligent energy-saving control method for predicting outside air and load intelligently, wherein the second time outside air climate condition is greater than the first time outside air climate condition.         As described in item 4 of the scope of the patent application, the intelligent energy-saving control method for predicting outside air and load intelligently, wherein the first time outside air climate condition is a 30-minute climate condition prediction, and the second time outside air climate condition is 60. Climate conditions forecast in minutes.