Air conditioner energy control method and device based on weather load, and medium

Abstract

The application provides a weather load-based air conditioner energy control method and device and a storage medium, wherein the method comprises the following steps: saving PLC bottom layer instrument data, indoor temperature sensor data and public data to a cloud platform server; processing the above data, calculating first index data in units of predetermined working time, calculating an optimized load amount according to the first index data; selecting a date closest to a predicted load day comprehensive temperature T in historical operation data as a reference day, taking the load of the reference day as a reference day load; and adjusting air conditioner energy according to the optimized load amount through two preset optimization conditions and the reference day load. The application predicts future load change rules by mining the corresponding relationship among climate, indoor temperature and load change rules through historical data, so as to realize the purpose of precise energy supply and effectively avoid energy waste.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of air conditioning energy regulation, and in particular to an air conditioning energy control method based on weather load, equipment and medium. BACKGROUND

[0002] To achieve the goal of "carbon peak" and "carbon neutral", in the field of heating, optimizing the allocation of heating resources and adjusting the mode of the heating system are particularly important. In the future, the only energy that can be used for heating is low-carbon or even zero-carbon energy, such as green electricity, waste heat, solar heat, and geothermal heat. Green electricity is used as the basic energy source to adjust the heating system. In practical applications, air conditioning heating accounts for more than 40% of building energy consumption. How to accurately supply energy and adjust on demand will play a decisive role in energy saving and emission reduction. With the development of digital energy, how to deeply utilize data and tap the value of data has become a new revolutionary breakthrough in the energy field.

[0003] Under this background, considering the variability of building heating demand and the numerous disturbance factors, an air conditioning energy control method is urgently needed to solve the above problems. SUMMARY

[0004] The embodiment of the present application provides an air conditioning energy control method based on weather load, equipment and medium, which aims to solve the above problems.

[0005] The present application provides an air conditioning energy control method based on weather load, comprising:

[0006] S1, saving PLC bottom layer instrument data, indoor temperature sensor data and public data to a cloud platform server;

[0007] S2, processing the data obtained in step S1 to calculate first index data in units of predetermined working time, and calculating an optimized load according to the first index data;

[0008] S3, selecting the date closest to the predicted load day comprehensive temperature T in the historical operation data as a reference day, and taking the load of the reference day as the reference day load;

[0009] S4, adjusting the air conditioning energy according to the optimized load through the two preset optimization conditions and the reference day load.

[0010] The present application provides a computer device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to realize the steps of the above-mentioned air conditioning energy control method based on weather load.

[0011] The present application provides a computer readable storage medium, the computer readable storage medium stores a computer program, characterized in that, the computer program is executed by the processor to realize the steps of the above-mentioned air conditioner energy control method based on weather load.

[0012] The embodiment of the present application predicts the future load change rule by mining the corresponding relationship among the climate, indoor temperature and load change rule through historical data, so as to realize the purpose of precise energy supply and effectively avoid energy waste. The potential value of historical data is fully utilized to analyze the influence of outdoor temperature change of the main climate factor on the energy demand of the air conditioning system, so as to calculate the adjustment amount of the load according to the climate change and predict the future energy output strategy. BRIEF DESCRIPTION OF DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the one or more embodiments of the present specification or the prior art, the drawings needed to be used in the embodiment or prior art description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments described in the present specification, and other drawings can be obtained by those skilled in the art without creative labor.

[0014] Figure 1 The flowchart of the air conditioner energy control method based on weather load of the embodiment of the present application is shown in the figure.

[0015] Figure 2 The flowchart of the air conditioner energy control method based on weather load of the specific example of the embodiment of the present application is shown in the figure.

[0016] Figure 3 The comparison quantity of the number of compressors before and after improvement of the embodiment of the present application. DETAILED DESCRIPTION

[0017] In order to make the person skilled in the art better understand the technical solutions in the one or more embodiments of the present specification, the technical solutions in the one or more embodiments of the present specification will be described clearly and completely below in combination with the drawings in the one or more embodiments of the present specification. Obviously, the described embodiments are only some embodiments of the present specification, not all embodiments. Based on the one or more embodiments of the present specification, all other embodiments obtained by those skilled in the art without creative labor should belong to the protection scope of the present document.

[0018] Method embodiment

[0019] The embodiment of the present application provides an air conditioner energy control method based on weather load, Figure 1 The flowchart of the air conditioner energy control method based on weather load of the embodiment of the present application is shown in the figure, according toFigure 1 As shown, the air conditioner energy control method based on weather load of the embodiment of the application specifically comprises:

[0020] S1, save the PLC bottom layer instrument data, indoor temperature sensor data and public data to the cloud platform server, and the step S1 of the embodiment of the application specifically comprises:

[0021] The PLC unifies the bottom layer instrument data to the PLC controller, establishes a special network on the public network through the VPN router, carries out encrypted communication, ensures the safe transmission of data, establishes data interaction with the cloud platform through the special network, saves the data to the cloud platform server, and realizes the accumulation of data. The indoor temperature sensor can directly establish connection with the cloud platform through WiFi or 4G network card. Public meteorological data is grabbed through web crawler, such as China Meteorological Network, Baidu Weather, etc.

[0022] S2, process the data obtained in step S1 to calculate the first index data in a predetermined working time unit, and calculate the optimized load according to the first index data, and the step S2 of the embodiment of the application specifically comprises:

[0023] The data obtained in step S1 is processed, and the first index data is calculated according to the system working time unit, and the first index data includes: indoor average temperature t n , outdoor average temperature t w , and energy output value Q. According to the needs of the application scene, the data index calculation time period can be adjusted, such as calculating according to hour or day unit.

[0024] The optimized load is calculated, and the optimized load is calculated twice, and is optimized for different indexes. The indoor temperature deviation needs to be optimized once, and is recorded as Q1, and the calculation formula is: The energy that needs to be adjusted for the current indoor index deviation is marked as the first optimization load Q1; the weather deviation needs to be optimized twice, and is recorded as Q2, and the calculation formula is: The energy for adjusting the predicted day for the weather change is marked; the indoor temperature and the weather temperature are both deviated, and both need to be optimized twice, that is, first optimized, the formula is unchanged, and the second optimization is recorded as Q3, and the calculation formula is: t n1 Indicates the indoor target value, t w1 Indicates the outdoor average temperature of the predicted day.

[0025] S3, select the date closest to the comprehensive temperature T of the predicted load day in the historical running data as the reference day, and take the load of the reference day as the reference day load; the step S3 of the embodiment of the application specifically comprises:

[0026] According to the weather temperature, the prediction day is filtered, and the filtering rule is according to the calculation formula T = AT + BT g d , T represents the comprehensive weather temperature; Tg and Td represent the maximum and minimum temperature of the predicted load day weather forecast; A and B represent the weather coefficient, which can be empirically adjusted according to different regions and climate types. By filtering the historical operation data closest to the value T of the predicted load day as the reference day load, the load output trend chart of the day is called as the adjustment basis. With the increase of data volume, the filtering rule is a kind of ascending dimension analogy reasoning model, that is, when the data sample is small, only the weather temperature can be used for historical data filtering, when the data volume increases, multiple historical data meeting the requirements appear, and other filtering conditions such as humidity and irradiance can be added for fine filtering.

[0027] S4, the air conditioning energy is adjusted according to the two optimization conditions and the reference day load. The step S4 of the embodiment of the application specifically comprises:

[0028] The two optimization conditions are respectively: n The optimization condition 1: the heat supply of the reference day is reasonable, that is, the indoor average temperature value t n falls within the indoor target value tn1, the interval is 18-20℃, and the interval can be adjusted; the optimization condition 2: the weather forecast temperature of today is consistent with the weather temperature of the reference day, that is, any one of the maximum temperature and the minimum temperature is within ±0.5℃. The judgment optimization rule is that: if the optimization conditions 1 and 2 are met, the system does not need to be optimized, and the predicted day load is directly executed according to the reference day; if the optimization condition 2 is met and the optimization condition 1 is not met, optimization is carried out once; if the optimization condition 1 is met and the optimization condition 2 is not met, secondary optimization is carried out; if the optimization condition 1 and the optimization condition 2 are not met, primary optimization and secondary optimization are needed.

[0029] The energy adjustment amount in the embodiment of the application adopts different calculation methods according to different optimization indexes, the calculation formula of the primary optimization energy is ΔQ1 = Q1-Q; and the calculation formula of the secondary optimization energy is ΔQ2 = Q2-Q.

[0030] The energy adjustment amount in the embodiment of the application can be converted into the adjustment of the number of compressors, and the calculation formula is as follows: respectively represent the primary optimization compressor adjustment amount and the secondary optimization compressor adjustment amount, q represents the heat or refrigeration capacity of a single compressor, and if the adjustment amount has a decimal point, the principle of rounding off is followed. The compressor adjustment amount adopts the uniform distribution mode to adjust the number of compressor starts and stops in the optimization time period, so as to ensure the stable operation of the system. The main feature is that in the start-up period, the compressor adjustment amount is uniformly distributed, but when the calculation adjustment amount cannot be uniformly distributed, the principle of more before and less after for incremental adjustment and less before and more after for decremental adjustment needs to be followed, that is, the overall operation is configured in a gradient mode.

[0031] The following will be specifically described with specific examples, Figure 2 The flow chart of the air conditioner energy control method based on weather load is a specific example of the embodiment of the present application.

[0032] Step one: is to obtain parameters, the main parameters obtained are: the highest weather temperature Tg of the prediction day, the lowest weather temperature, which are used to calculate the comprehensive weather temperature.

[0033] Step two: calculate the comprehensive weather temperature of the prediction day, the calculation formula is: T = 0.6T g + 0.4T d , which is used for historical data comparison.

[0034] Step three: obtain the historical data parameters, that is, calculate the comprehensive weather temperature of each day from the historical data according to the calculation method of step two, which is used for historical data comparison.

[0035] Step four: parameter comparison. That is, by comparing the comprehensive weather temperature of the prediction day with the comprehensive weather temperature in the historical data, the historical data closest to the weather type is obtained as the reference data or model data. Among them, the parameter comparison can gradually improve the comparison accuracy according to the data volume, such as when the comprehensive weather temperature is completely consistent and there are multiple reference data, other comparison factors such as humidity, light intensity, etc. can be added, and the data with the highest similarity is obtained as the reference data through the comparison of each parameter. The priority of the added comparison factors can be confirmed according to the project or regional factors.

[0036] Step five: obtain the data index in the reference data, the main indexes are: the number of compressor starts N of the reference data, the cumulative heat Q, the indoor average temperature tn, the outdoor average temperature tw, the indoor target temperature tn1, the highest weather temperature Tg1, and the lowest weather temperature Td1.

[0037] Step six: judge whether the absolute value of the indoor temperature deviation is less than or equal to 1, the calculation formula is: |tn1-tn|.

[0038] Step seven: the absolute value of the indoor temperature deviation is less than or equal to 1, judge whether the absolute value of the deviation of the highest weather temperature Tg and the lowest weather temperature Td of the prediction day and the reference data meets the deviation requirement of 0.5℃, the calculation formula is: |Tg-Tg1|<=0.5or|Td-Td1|<=0.5.

[0039] Step eight: according to the judgment result of step seven, if the deviation requirement is met, the number of compressor starts N in the reference data is directly extracted, and the energy is output at the time point of the reference data. For example, the number of compressor starts at 8:00 of the reference day is 10, so the number of compressor starts at 8:00 of the prediction day is also 10.

[0040] Step nine: according to the result of step seven, if the deviation requirement is not met, then the reference data needs to be optimized twice, and the optimized load Q2 is obtained, the calculation formula is:

[0041] Step ten: according to the calculation result of step nine, the secondary optimization amount N2 is obtained, and the calculation formula is: q is the output heat of a single compressor.

[0042] Step eleven: according to the gradient structure, the adjustment amount N2 is evenly distributed in the reference data, if it cannot be evenly distributed in the entire running time, then follow the principle of more in front and less in back, and the remaining compressor units after part of the distribution are spread out in the initial stage of operation. Refer to the attached Figure 3 .

[0043] Step twelve: according to the optimized compressor arrangement rule in step eleven, the energy output is executed at a fixed time on the forecast day.

[0044] Step thirteen: according to step six, the absolute value of indoor temperature deviation is not less than 1, and the absolute value of the deviation of the maximum weather temperature Tg and the minimum weather temperature Td from the reference data is not less than 0.5℃, that is, the deviation requirement is not met, the calculation formula is:

[0045] Step fourteen: according to the result of step thirteen, if the deviation requirement is met, then the reference data needs to be optimized once, and the optimized load Q1 is obtained, the calculation formula is:

[0046] Step fifteen: according to the calculation result of step fourteen, the first optimization amount N1 is obtained, and the calculation formula is: q is the output heat of a single compressor.

[0047] Step sixteen: according to the gradient structure, the adjustment amount N1 is evenly distributed in the reference data, if it cannot be evenly distributed in the entire running time period, then follow the principle of more in front and less in back, and the remaining compressor units after part of the distribution are spread out in the initial stage of operation. Refer to the attached Figure 3 .

[0048] Step seventeen: according to the optimized compressor arrangement rule in step sixteen, the energy output is executed at a fixed time on the forecast day.

[0049] Step eighteen: according to the results of step six and step thirteen, that is, the absolute value of indoor temperature deviation is not less than 1 and the deviation requirement is not met, then two optimizations are needed. First, the optimized load Q1 is obtained by one-time optimization, and the calculation formula is: Second, the optimized load Q3 is obtained by two-time optimization, and the calculation formula is:

[0050] Step nineteen: according to the calculation result of step eighteen, the first optimization amount N1 is obtained, and the calculation formula is: The second optimization amount N2 is calculated, and the calculation formula is: The total optimization amount N3=N1+N2 is calculated.

[0051] Step twenty: the adjustment amount N3 is evenly distributed in the reference data according to the echelon structure, if the entire operation time period cannot be evenly distributed, then the principle of more in the front and less in the back is followed, and the remaining compressor units after the partial distribution are laid out in the initial operation stage. Refer to the attached Figure 3 .

[0052] Step twenty-one: according to the optimized compressor arrangement rule of step twenty, the energy output is executed at a fixed time on the prediction day.

[0053] The content to be solved by the present application is an energy adjustment method for predicting the load change with weather temperature change in the future time period through a simple model.

[0054] The present application relies on historical operation data, mainly selects two variables affecting load change, i.e. weather temperature and indoor temperature, two important indexes, selects the data closest to the operation load of the prediction day as the reference load through analogy of historical operation data, greatly reduces the difficulty of load prediction. And with the increase of data amount, other influencing factors such as weather humidity, illumination, etc. can be continuously added in the rule of selecting reference day load.

[0055] The present application is divided into two times of optimization according to the two indexes of weather temperature and indoor temperature, i.e. the first energy output amount deviation is calculated according to the deviation of indoor temperature and target temperature; and the second energy output deviation is calculated according to the deviation of weather temperature of the prediction day and the weather temperature of the reference day, so as to determine the energy to be output on the final prediction day.

[0056] The present application confirms the adjustment amount by energy deviation calculation and unit compressor energy output, and distributes the total adjustment amount to each time period of the reference day in an even distribution mode. In addition, the present application can be flexibly adjusted according to the actual project, i.e. to realize energy adjustment in different time periods.

[0057] The present application avoids the traditional water temperature adjustment mode, and adopts the direct compressor quantity control mode to adjust the energy, which can realize direct energy control.

[0058] The present application does not limit the system form and region, and can be applied to an office building of a fan coil system, and can be applied to a floor radiation type residential heating project. If the residential project has a peak-valley electricity price policy, the adjustment rule of the compressor will be adjusted, and the principle of maximum load output in valley electricity, reduced output in flat electricity, and no output in peak electricity is followed to realize peak-shaving operation. Alternatively, the principle of proportional distribution is followed, and the adjustment amount of the energy deviation final value in the peak, flat and valley periods is distributed according to the principle of 1:2:3, and the adjustment proportion can be adjusted according to the project.

[0059] By adopting the embodiment of the present application, the following beneficial effects are achieved:

[0060] Device embodiment one

[0061] The embodiment of the present application provides a computer device, which comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the method embodiment when executing the computer program.

[0062] Device embodiment two

[0063] The embodiment of the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the steps of the method embodiment.

[0064] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A weather load-based air conditioning energy control method, characterized by, The method comprises the following steps: S1, saving PLC bottom layer instrument data, indoor temperature sensor data and public data to a cloud platform server; S2, processing the data obtained in step S1 to calculate first index data in a predetermined working time unit, and calculating an optimized load quantity according to the first index data; S3, selecting a date closest to the predicted load day comprehensive temperature T in the historical running data as a reference day, and taking the load of the reference day as the reference day load; S4, adjusting the air conditioning energy according to the optimized load quantity through two preset optimization conditions and the reference day load; The first index data calculating the optimized load quantity specifically comprises: The first index data is calculated in units of predetermined working time, and includes values of: an indoor average temperature , an outdoor average temperature , and an energy output amount ; performing first indoor temperature deviation optimization according to formula 1: Formula 1 ; performing second weather deviation optimization according to formula 2 and formula 3: Formula 2: Equation 3; wherein Q1 is an indoor temperature deviation optimization value, 2 is a first weather deviation optimization value, 3 is an optimization value calculated when both indoor temperature and weather temperature deviate, t n1 represents an indoor target value, t w1 represents a predicted daily outdoor average temperature.

2. The method of claim 1, wherein, The step S1 specifically comprises: The PLC collects bottom layer instrument data to the PLC controller, establishes a special network on the public network through a VPN router for encrypted communication, establishes data interaction with the cloud platform through the special network, and saves the bottom layer instrument data to the cloud platform server; The indoor temperature sensor and the public meteorological data obtained by the network crawler directly establish a connection with the cloud platform through a WiFi or 4G network card.

3. The method of claim 1, wherein, The step S3 specifically comprises: screening historical running data according to formula 4: Formula 4; T represents the comprehensive weather temperature, T g and T d represent the maximum and minimum temperatures of the predicted load day weather forecast; A and B represent the weather coefficients; If there are multiple historical data of the predicted load day comprehensive temperature T, fine screening is performed by adding screening conditions, and the added screening conditions include humidity and irradiance.

4. The method of claim 1, wherein, The two preset optimization conditions in step S4 include: The optimization condition 1 includes: the indoor average temperature value in the working hours of the reference day Falling in the indoor target value t n1 Inside; Optimization condition 2 includes that the temperature of the day's weather forecast and the highest and lowest temperatures of the reference day differ by ±0.5℃ or less.

5. The method of claim 4, wherein, The step S4 specifically comprises: If the conditions optimization condition 1 and the optimization condition 2 are met, the system does not need to be optimized, and the predicted day load is directly executed according to the reference day; If condition 2 is met and condition 1 is not met, first indoor temperature deviation optimization is performed; If condition 1 is met and condition 2 is not met, second weather deviation optimization is performed; If neither condition 1 nor condition 2 is met, indoor temperature deviation optimization and second weather deviation optimization are needed.

6. The method of claim 1, wherein, The energy adjustment quantity after the air conditioning energy adjustment is further calculated as first optimization energy and second optimization energy through formula 5 and formula 6: Equation 5; Equation 6.

7. The method of claim 6, wherein, The method further comprises converting the energy adjustment quantity into compressor quantity adjustment through formula 7 and formula 8, Formula 7: Formula 8: N1 and N2 respectively represent the primary optimized compressor adjustment amount and the secondary optimized compressor adjustment amount, and q represents the heat or refrigeration amount of a single compressor. The compressor adjustment quantity adopts an even distribution method to adjust the number of compressor starts and stops in the optimization time period. In the start time period, the compressor adjustment quantity is evenly distributed. When the calculated adjustment quantity cannot be evenly distributed, the principle of more before and less after for incremental adjustment and less before and more after for decremental adjustment is followed.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to realize the steps of the air conditioning energy control method based on weather load in any one of claims 1-7.

9. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 8. The computer program is executed by the processor to realize the steps of the air conditioning energy control method based on weather load in any one of claims 1-7.

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