CN116182428B - Optimal control method and system for solar heat pump - Google Patents
Optimal control method and system for solar heat pump Download PDFInfo
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- CN116182428B CN116182428B CN202310450121.5A CN202310450121A CN116182428B CN 116182428 B CN116182428 B CN 116182428B CN 202310450121 A CN202310450121 A CN 202310450121A CN 116182428 B CN116182428 B CN 116182428B
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F25—REFRIGERATION OR COOLING; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS; MANUFACTURE OR STORAGE OF ICE; LIQUEFACTION SOLIDIFICATION OF GASES
- F25B—REFRIGERATION MACHINES, PLANTS OR SYSTEMS; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS
- F25B27/00—Machines, plants or systems, using particular sources of energy
- F25B27/002—Machines, plants or systems, using particular sources of energy using solar energy
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F25—REFRIGERATION OR COOLING; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS; MANUFACTURE OR STORAGE OF ICE; LIQUEFACTION SOLIDIFICATION OF GASES
- F25B—REFRIGERATION MACHINES, PLANTS OR SYSTEMS; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS
- F25B49/00—Arrangement or mounting of control or safety devices
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Abstract
The disclosure provides an optimal control method and system for a solar heat pump, and relates to the technical field of intelligent control, wherein the method comprises the following steps: obtaining sun sunshine time, sun sunshine intensity and temperature information; obtaining heat prediction information; constructing a temperature change curve based on the temperature information; determining a fusion deviation temperature value; when the fusion deviation temperature value is a negative value, judging whether the fusion deviation temperature value reaches a preset adjustment threshold value or not; when the control parameter combination is reached, an optimization space is constructed, and an optimization control parameter combination is obtained; the control instruction is generated based on the optimized control parameter combination to control the operation of the heat pump, so that the technical problems that the control effect of the solar heat pump is poor and the phenomenon that the water pipe is frozen is difficult to timely and effectively avoid in the prior art due to inaccurate analysis of influence factors on the water temperature are solved, the solar energy is utilized to the maximum extent, the water pipe is prevented from being frozen, and the technical effects of energy conservation and consumption reduction are achieved.
Description
Technical Field
The disclosure relates to the technical field of intelligent control, in particular to an optimal control method and system for a solar heat pump.
Background
The solar heat pump water heater is a novel, efficient and energy-saving water heater, combines the heat pump technology with solar energy to supply domestic hot water, can reduce the use of fossil fuel, simultaneously reduces carbon emission, and realizes energy conservation and consumption reduction.
At present, the technical problems that the control effect of the solar heat pump is poor, and the phenomenon that a water pipe is frozen is difficult to timely and effectively avoid due to the fact that the influence factor analysis on the water temperature is not accurate enough exist in the prior art.
Disclosure of Invention
The disclosure provides an optimal control method and system for a solar heat pump, which are used for solving the technical problems that in the prior art, the control effect on the solar heat pump is poor due to inaccurate analysis of influence factors on water temperature, and the phenomenon that a water pipe is frozen is difficult to timely and effectively avoid.
According to a first aspect of the present disclosure, there is provided an optimal control method for a solar heat pump, comprising: acquiring weather information through a big data platform, and acquiring sun sunshine time, sun sunshine intensity and temperature information based on the weather information; taking the sun sunshine time and the sun sunshine intensity as input variables, and predicting through a heat prediction model to obtain heat prediction information; constructing a temperature change curve based on the temperature information; carrying out alignment heat fusion analysis on the heat prediction information by utilizing the temperature change curve to determine a fusion deviation temperature value, wherein the fusion deviation temperature value is used for describing a temperature difference value after heat fusion; when the fusion deviation temperature value is a negative value, judging whether the fusion deviation temperature value reaches a preset adjustment threshold value or not; when the temperature is reached, an optimization space is constructed based on the fusion deviation temperature value, the heat pump operation parameter and the temperature information, and an optimization control parameter combination is obtained; and generating a control instruction based on the optimized control parameter combination to control the operation of the heat pump.
According to a second aspect of the present disclosure, there is provided an optimal control system for a solar heat pump, comprising: the weather information acquisition module is used for acquiring weather information through the big data platform and acquiring solar sunlight time, solar sunlight intensity and temperature information based on the weather information; the heat prediction module is used for predicting the sun sunshine time and the sun sunshine intensity serving as input variables through a heat prediction model to obtain heat prediction information; the temperature change curve construction module is used for constructing a temperature change curve based on the temperature information; the alignment heat fusion analysis module is used for carrying out alignment heat fusion analysis on the heat prediction information by utilizing the temperature change curve to determine a fusion deviation temperature value, wherein the fusion deviation temperature value is used for describing a temperature difference value after heat fusion; the fusion deviation temperature value judging module is used for judging whether the fusion deviation temperature value reaches a preset adjustment threshold value or not when the fusion deviation temperature value is a negative value; the control parameter optimization module is used for constructing an optimization space based on the fusion deviation temperature value, the heat pump operation parameter and the temperature information when the control parameter optimization module is reached, and obtaining an optimized control parameter combination; and the optimizing control module is used for generating a control instruction based on the optimizing control parameter combination to control the operation of the heat pump.
According to the optimal control method for the solar heat pump, the influence of the ambient temperature and the solar heat on the water temperature is analyzed respectively, heat fusion is carried out, the fusion deviation temperature value is obtained, and further the optimal control parameter combination is obtained according to the fusion deviation temperature value to control the heat pump, so that the auxiliary matching of the solar water heater and the heat pump is realized, the solar energy is utilized to the maximum extent, the water pipe is prevented from being frozen, and the technical effects of energy conservation and consumption reduction are realized.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.
Drawings
For a clearer description of the present disclosure or of the prior art, the drawings that are required to be used in the description of the embodiments or of the prior art will be briefly described, it being apparent that the drawings in the description below are merely illustrative and that other drawings may be obtained, without inventive effort, by a person skilled in the art from the drawings provided.
Fig. 1 is a schematic flow chart of an optimization control method for a solar heat pump according to an embodiment of the disclosure;
FIG. 2 is a flow chart of obtaining an optimized control parameter combination in an embodiment of the disclosure;
FIG. 3 is a schematic flow chart of adding the water tank maintenance amount as an optimization variable into an optimization space in an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of an optimizing control system for a solar heat pump according to an embodiment of the disclosure.
Reference numerals illustrate: the system comprises a meteorological information acquisition module 11, a heat prediction module 12, a temperature change curve construction module 13, an alignment heat fusion analysis module 14, a fusion deviation temperature value judgment module 15, a control parameter optimization module 16 and an optimization control module 17.
Detailed Description
Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
In order to solve the technical problems that in the prior art, the control effect on the solar heat pump is poor due to inaccurate analysis of influence factors on water temperature, and the phenomenon that a water pipe is frozen is difficult to timely and effectively avoid, the inventor of the present disclosure obtains the optimal control method and the optimal control system for the solar heat pump through creative labor.
Fig. 1 is a diagram of an optimization control method for a solar heat pump according to an embodiment of the present disclosure, where, as shown in fig. 1, the method includes:
step S100: acquiring weather information through a big data platform, and acquiring sun sunshine time, sun sunshine intensity and temperature information based on the weather information;
specifically, the big data platform refers to various weather forecast platforms, such as weather nets, weather nets and the like, weather information is obtained through the big data platform, the weather information comprises temperature, humidity, illumination intensity and the like, the weather information comprises various weather information, available information, namely solar sunlight time, solar sunlight intensity and temperature information, needs to be extracted from the weather information, the solar sunlight time refers to the time when the sun irradiates the ground directly, the solar sunlight intensity refers to the magnitude of solar radiation power received in a unit area, the temperature information is ambient temperature, and the solar sunlight time, the solar sunlight intensity and the temperature information need to be described as dynamic changes along with time.
Step S200: taking the sun sunshine time and the sun sunshine intensity as input variables, and predicting through a heat prediction model to obtain heat prediction information;
specifically, the solar water heater absorbs heat of sunlight by using a evacuated collector tube, and transfers the heat to cold water, so that the heat is converted into hot water. Because the vacuum heat collecting pipes are generally arranged in the solar water heater, when the sunlight irradiates the solar heat pump, the vacuum heat collecting pipes in the solar water heater can absorb all the heat of the sunlight and then transfer the heat into cold water, so that the cold water is changed into hot water. The heat prediction model is a neural network model in machine learning, and generates heat according to solar sunlight time and solar sunlight intensity analysis, that is, the input of the heat prediction model is solar sunlight time and solar sunlight intensity, the output is heat prediction information, and the heat prediction information is generated solar heat. Specifically, a training data set can be obtained based on big data, the training data set comprises sample sun sunshine time, sample sun sunshine intensity and corresponding sample heat prediction information, each group of sample sun sunshine time and sample sun sunshine intensity in the training data set are input into a prediction model, and the output of the prediction model is supervised and adjusted by utilizing the corresponding sample heat prediction information, so that the output of the prediction model is consistent with the sample heat prediction information, the prediction model is trained to be converged, the accuracy of the prediction model is verified, and the prediction model with the accuracy meeting the requirements is obtained. The sun sunshine time and the sun sunshine intensity are changed along with time, so that the heat prediction information obtained by the prediction model is also dynamically changed.
Step S300: constructing a temperature change curve based on the temperature information;
specifically, the time is taken as an abscissa, the temperature is taken as an ordinate, and the temperature change condition is drawn into a temperature change curve according to the temperature information, so that the change condition of the temperature along with the time can be obtained more intuitively.
Step S400: carrying out alignment heat fusion analysis on the heat prediction information by utilizing the temperature change curve to determine a fusion deviation temperature value, wherein the fusion deviation temperature value is used for describing a temperature difference value after heat fusion;
wherein, step S400 of the embodiment of the present disclosure further includes:
step S410: generating a thermal prediction change curve based on the thermal prediction information;
step S420: the curve mapping relation analysis is carried out on the temperature change curve and the heat prediction change curve to obtain a curve mapping characteristic set, wherein the temperature change curve and the heat prediction change curve have a time corresponding relation;
step S430: performing feature fusion analysis on each time based on the curve mapping feature set to determine a fusion difference feature set;
step S440: and determining the heat difference value of each time point based on the fusion difference feature set to obtain the fusion deviation temperature value.
Specifically, the heat prediction information is a predicted value of solar heat, the predicted value of solar heat changes along with time, a temperature change curve is an environment temperature change condition outside a water pipe, if the environment temperature is too low, the temperature of water in the water pipe can be reduced, even the water in the water pipe is frozen, the predicted value of the temperature of the water in the water pipe and the environment temperature need to be fused, a fusion deviation temperature value is obtained, the fusion deviation temperature value is positive and negative, and the fusion deviation temperature value is used for describing a temperature difference value after heat fusion.
Specifically, the heat prediction information dynamically changes along with time, the time is taken as an abscissa, the temperature prediction value of water in a water pipe is taken as an ordinate, a heat prediction change curve is drawn according to the heat prediction information, the temperature change curve and the heat prediction change curve have a time corresponding relation, a curve mapping relation analysis is carried out on the temperature change curve and the heat prediction change curve to obtain a curve mapping characteristic set, that is, the abscissa of the temperature change curve and the heat prediction change curve is time, the two curves are formed by connecting a plurality of points, the two curves are placed in the same coordinate, the analysis is convenient, the points with the time corresponding relation are aligned according to the time corresponding relation, the difference between the environment temperature and the heat prediction information can be intuitively seen, the curve mapping feature set refers to the environmental temperature and heat prediction information with time corresponding relation on the temperature change curve and the heat prediction change curve, the feature fusion analysis is carried out on each time according to the curve mapping feature set, in short, under the condition that the environmental temperature is too low, the environment absorbs the heat of water so as to change the temperature in a water pipe, according to the environmental temperature and heat prediction information corresponding to each event in the curve mapping feature set, the temperature influence of the environmental temperature on the water temperature and the temperature influence of solar heat on the water are analyzed, in other words, the environmental temperature is too low, the water temperature is possibly lowered, even frozen, the solar heat can raise the water temperature, thus obtaining the influence value of the solar heat and the environmental temperature on the water temperature, the influence value is the temperature value of the water temperature rise or fall, the influence value has positive (rise) and negative (fall), the fusion difference feature set is formed by the solar heat corresponding to each time and the influence value of the ambient temperature on the water temperature, and then the solar heat corresponding to each time and the influence value of the ambient temperature on the water temperature are overlapped according to the fusion difference feature set, the overlapped temperature value is the fusion deviation temperature value, and the fusion deviation temperature value is positive and negative, for example, the influence value of solar energy on the water temperature is positive, or the solar heat can raise the water temperature without considering the environment, but the ambient temperature can raise or lower the water temperature, so that the influence value of the ambient temperature on the water temperature is positive or negative (raise or lower), based on the superposition result, the influence value of solar energy on the water temperature and the influence value of the ambient temperature on the water temperature can be positive or negative, that is, if the fusion deviation temperature value is negative, the risk of freezing a water pipe is indicated, and the risk analysis on the freezing of the water pipe is realized by acquiring the fusion deviation temperature value, so that the risk analysis on the water pipe is convenient to adjust the heat pump control parameters subsequently is prevented.
Step S500: when the fusion deviation temperature value is a negative value, judging whether the fusion deviation temperature value reaches a preset adjustment threshold value or not;
wherein, step S500 of the embodiment of the present disclosure further includes:
step S510: obtaining a historical pipeline fault case;
step S520: performing fault feature analysis based on the historical pipeline fault cases to determine a fault feature set;
step S530: and carrying out fault environment temperature-heat characteristic analysis according to the fault characteristic set, and determining a temperature-heat deviation threshold value as the preset adjustment threshold value.
Specifically, when the fusion deviation temperature value is a negative value, the risk of freezing of the water pipe is indicated, risk judgment is needed at this time, heat pump control is performed according to a risk judgment result, a preset adjustment threshold is the basis of risk judgment, the preset adjustment threshold is a reference index for judging the fusion deviation temperature value, that is, when the fusion deviation temperature value is a negative value, the water pipe is frozen, but only when the fusion deviation temperature value is lower than the preset adjustment threshold, the possibility of freezing of the water pipe is higher, at this time, the heat pump is controlled and adjusted to avoid freezing of the water pipe, and the preset adjustment threshold is acquired according to historical data.
Specifically, the process of acquiring the preset adjustment threshold is as follows: the method comprises the steps of obtaining a historical pipeline fault case, wherein the historical pipeline fault case refers to a case that a water pipe is frozen in the history, carrying out fault feature analysis according to the historical pipeline fault case, analyzing the ambient temperature and solar heat when the water pipe is frozen in the history, wherein the historical pipeline fault case comprises a plurality of fault cases, one case corresponds to a group of ambient temperature and solar heat, the ambient temperature and the solar heat corresponding to each case form a fault feature set, carrying out fault ambient temperature-heat feature analysis on the fault feature set by adopting the same method as that of obtaining a fusion deviation temperature value in the steps, superposing the influence value of the ambient temperature and the solar heat on the water temperature in each case, obtaining a plurality of superposed value corresponding to the plurality of cases, analyzing the superposed value as a negative number, taking the absolute value in the superposed value as a temperature-heat deviation threshold, taking the absolute value in the superposed value as a preset adjustment threshold, and further judging the fusion deviation temperature value according to the preset adjustment threshold, thereby realizing the effect of accurately judging the freezing risk of the water pipe.
Step S600: when the temperature is reached, an optimization space is constructed based on the fusion deviation temperature value, the heat pump operation parameter and the temperature information, and an optimization control parameter combination is obtained;
as shown in fig. 2, step S600 of the embodiment of the disclosure further includes:
step S610: determining target adjustment heat according to the fusion deviation temperature value;
step S620: determining an operation heat-energy consumption corresponding relation according to the heat pump operation parameters;
step S630: taking the temperature information as a constraint condition, and constructing an energy-saving target fitness function according to the corresponding relation between the running heat and the energy consumption and the target regulating heat;
step S640: and establishing the optimization space based on the energy-saving target fitness function, and performing iterative optimization to obtain the optimization control parameter combination.
Specifically, when the fusion deviation temperature value reaches a preset adjustment threshold value, the possibility that the water pipe is frozen is high, an optimization space is constructed based on the fusion deviation temperature value, heat pump operation parameters and temperature information, and an optimal control parameter combination is obtained, wherein the heat pump operation parameters are power parameters and heating time parameters during heat pump operation.
Specifically, the target adjustment heat is determined according to the fusion deviation temperature value, in short, the purpose of starting the heat pump to perform temperature adjustment is to offset the fusion deviation temperature value, prevent the water pipe from being frozen, perform temperature-heat relation conversion according to the fusion deviation temperature value, determine how much heat the heat pump needs to provide to offset the fusion deviation temperature value, and take the heat pump as the target adjustment heat. Further, according to the heat pump operation parameters, the corresponding relation between the operation heat and the energy consumption is determined, when the heat pump is used for heating, different powers can be selected, different heating times are set, different powers can generate the same heat, but different powers have different consumption amounts of energy sources, different types of heat pumps are different, based on the different types of used fuels and energy sources, the cost of the fuel or the energy sources is used as an energy consumption standard, for example, the cost of the natural gas consumed under different heating powers is obtained through heating of natural gas, temperature information is used as an energy consumption standard, namely, in the heating process of the heat pump, the environmental temperature can have a certain influence on the generated heat, and the influence is unavoidable, for example, the heat pump can generate the same heat as the target adjustment heat by controlling the heat pump through a group of heat pump control parameters, but the generated heat is lower than the target adjustment heat in practice, therefore, when the iteration of the control parameters is performed, the control parameters are required to be adjusted according to the environmental temperature, the adjustment range of the control parameters is required, and the target adjustment heat can be achieved. Based on the method, temperature information is used as constraint conditions, an energy-saving target fitness function is constructed according to the corresponding relation of the running heat and the energy consumption and the target regulation heat, the fitness function is an important component of the optimization control process, and the capacity of solving the problem of different optimization parameters is described through the fitness function, so that the optimization parameter with the highest capacity of solving the problem is obtained as the final optimization parameter. In this embodiment, the energy-saving objective fitness function is used to evaluate the energy-saving effect of the heat pump control parameters, that is, under the constraint of temperature information, multiple groups of heat pump control parameters can be obtained according to the objective adjustment heat, each group of heat pump control parameters includes a power control parameter and a time control parameter, the energy consumption generated by each group of heat pump control parameters is obtained according to the corresponding relation of the running heat and the energy consumption, the energy-saving effect of the multiple groups of heat pump control parameters is evaluated based on the energy-saving effect, and the heat pump control parameter with the minimum energy consumption is selected from the multiple groups of heat pump control parameters as the optimized control parameter combination according to the energy-saving effect evaluation result. Therefore, the technical effects of energy conservation and consumption reduction while the heat pump operation is optimally controlled and the water pipe is prevented from being frozen are achieved.
Step S700: and generating a control instruction based on the optimized control parameter combination to control the operation of the heat pump.
Specifically, the optimal control parameter combination generates a control instruction, and the heat pump is controlled to operate and control the heat pump according to the power control parameter and the heating time parameter in the optimal control parameter combination by the control instruction, so that the technical effects of saving energy and reducing consumption while preventing the water pipe from being frozen are achieved.
The step S800 of the embodiment of the present disclosure includes:
step S810: constructing a characteristic time sequence chain in a time sequence based on the curve mapping characteristic set;
step S820: acquiring a target heating temperature;
step S830: based on the characteristic time sequence chain and the target heating temperature, carrying out temperature-heat absorption fusion on a first time node, and determining a first node heat balance;
step S840: based on the characteristic time sequence chain and the target heating temperature, performing temperature-heat absorption fusion on a second time node, and fusing the fusion result with the first node heat balance to obtain a second node heat balance;
step S850: and so on, based on the characteristic time sequence chain and the target heating temperature, carrying out temperature-heat absorption fusion on an N-th time node, and carrying out fusion on the fusion result and the N-1-th node heat balance to obtain the N-th node heat balance;
Step S860: and controlling the heat pump according to the first node heat balance and the second node heat balance until the N node heat balance.
Specifically, when solar energy is used for heating water, the heat generated by the solar energy is also random and unstable in consideration of the random change of weather information, so that the solar energy and the heat pump are mutually auxiliary heat sources, the solar energy is utilized to the maximum extent, and the heat pump is started to heat under the condition of insufficient solar energy. According to the curve mapping feature set, a feature time sequence chain is constructed by a time sequence, in short, according to the time sequence, the environmental temperature and heat prediction information with a time corresponding relation on the temperature change curve and the heat prediction change curve are arranged, an obtained arrangement result is the feature time sequence chain, a plurality of time nodes are arranged on the feature time sequence chain, and one node corresponds to one environmental temperature and one heat prediction information. Acquiring a target heating temperature, namely, a temperature value which is required by a user to be reached by the water temperature, further carrying out temperature-heat absorption fusion on a first time node, and determining a first node heat balance, namely, analyzing the temperature influence of the environment temperature on the water temperature and the temperature influence of solar heat on the water, and analyzing whether the water temperature can reach the target heating temperature or not only through solar energy, and if the water temperature can reach the target heating temperature, the first node heat balance is zero; if the target heating temperature cannot be reached, acquiring a heat value required by the water temperature to reach the target heating temperature, and adding a negative sign to the heat value, wherein the heat balance of the first node is negative, and then the first node is required to be heated by a heat pump; if the water temperature can exceed the target heating temperature, releasing heat which can enable the water temperature to just reach the target heating temperature through a vacuum heat collecting pipe in the solar water heater, and storing the rest heat, so that the rest heat after the water temperature reaches the target heating temperature is the first node heat balance, and the first node heat balance is positive at the moment; further, based on the characteristic time sequence chain and the target heating temperature, the temperature-heat absorption fusion is performed on the second time node by adopting the same method as that for acquiring the first node heat balance, so as to acquire a fusion result, and it is required to acquire the second node heat balance corresponding to the second time node, and further, it is required to perform analysis in combination with the first node heat balance, that is, first, analyze the influence of the ambient temperature and the solar heat of the second time node on the water temperature, and analyze whether the solar heat can enable the water temperature to reach the target heating temperature, so as to acquire the fusion result of the second time node, and meanwhile, if the first node heat balance is a positive number, it is required to superimpose the first node heat balance on the fusion result, and the superimposed result is taken as the second node heat balance, for example, if the solar heat corresponding to the second node heat balance (fusion result) is a negative value, but after the heat in the first node heat balance is superimposed together, the target heating temperature may be reached, and the superimposed heat is taken as the second node heat balance. And similarly, continuing to perform temperature-heat absorption fusion on the N-th time node by adopting the same method as the second node heat balance, and fusing the fusion result with the N-1-th node heat balance to obtain the N-th node heat balance. And controlling the heat pump according to the first node heat balance and the second node heat balance until the N-th node heat balance, specifically, if the first node heat balance and the second node heat balance until the N-th node heat balance are negative, the fact that the water temperature cannot reach the target heating temperature through solar heat is explained, the negative value represents the heat required by the water temperature to reach the target heating temperature, the heat pump is controlled to generate the required heat at the corresponding time node based on the negative value, the water temperature reaches the target heating temperature, the auxiliary cooperation of the solar water heater and the heat pump is realized, the requirement of a user on the water temperature is ensured while the solar energy is utilized to the maximum extent, and the technical effects of energy conservation and consumption reduction are realized.
The step S900 of the embodiment of the present disclosure includes:
step S910: when the fusion deviation temperature value is a positive value, determining the water temperature according to the fusion deviation temperature value;
step S920: when the water temperature does not meet the preset temperature requirement, obtaining a water temperature difference, and carrying out parameter matching based on the water temperature difference and the heat pump operation parameter to determine the heat pump auxiliary control parameter.
Specifically, when the fusion deviation temperature value is a positive value, it is indicated that the water pipe is not in risk of being frozen, however, at this time, the water temperature may not reach the expected temperature, so that the water temperature needs to be determined according to the fusion deviation temperature value, specifically, the current water temperature may be collected by using a temperature sensor, the current water temperature plus the fusion deviation temperature, that is, the water temperature which can be reached by solar heat, is used as the water temperature, whether the water temperature meets the preset temperature requirement is judged, the preset temperature requirement is the temperature expected by the user, if the water temperature does not meet the preset temperature requirement, the preset temperature requirement and the water temperature are differed, the water temperature difference is obtained, the heat pump operation parameters which can enable the water temperature to reach the expected temperature are determined as heat pump auxiliary control parameters according to the water temperature difference, and the heat pump auxiliary control parameters include heating power and heating time, so that the effect of guaranteeing the water use requirement of the user is achieved.
As shown in fig. 3, step S1000 in the embodiment of the disclosure includes:
step S1010: acquiring volume information and water level limiting information of a water storage tank;
step S1020: performing heat balance analysis according to the fusion deviation temperature value and a preset adjustment threshold value, and determining water flow demand;
step S1030: obtaining a water tank maintenance amount according to the volume information, the water level limit information and the water flow demand of the water tank;
step S1040: and taking the maintenance quantity of the water tank as an optimization variable, and adding the maintenance quantity of the water tank into the optimization space.
Specifically, volume information and water level limiting information of a water storage tank are obtained, the volume information of the water storage tank represents the volume of the water tank, the water level limiting information refers to information for limiting the water level in the water tank, such as the water level cannot exceed 1 meter, heat balance analysis is further carried out according to a fusion deviation temperature value and a preset adjustment threshold value, water flow demand is determined, the heat balance analysis is to ensure that water in the water tank is not frozen, that is, the water in the water tank flows out of a water outlet for a user to use, in the process, the fusion deviation temperature value cannot reach the preset adjustment threshold value, so that the water can be prevented from being frozen, but excessive water in the water tank can increase energy consumption, water flow conditions when the water pipe cannot be frozen are analyzed according to the fusion deviation temperature value and the preset adjustment threshold value, such as water demand of a user at night is small, and the water quantity in the water tank can be reduced by reducing the water flow. The water tank maintenance amount is obtained according to the volume information, the water level limiting information and the water flow demand of the water tank, the water tank maintenance amount is the water level requirement, the water tank maintenance amount is required to obtain the lowest maintenance water level according to the volume information and the water flow demand of the water tank under the condition that the water tank volume information and the water flow demand are not more than the water level limiting information, the water level is reduced while the user demand is ensured, the energy saving and consumption reduction are realized, the water tank maintenance amount is taken as an optimization variable, and an optimization space is added, so that the obtained energy saving effect of the optimized control parameter combination is better.
The step S1100 of the embodiment of the present disclosure includes:
step S1110: obtaining a historical water use record of a user;
step S1120: according to the historical water consumption record of the user, carrying out user water consumption trend feature analysis and determining water consumption trend features;
step S1130: and determining a dynamic water level adjustment feature based on the water consumption trend feature to obtain the water flow demand, wherein the water flow demand has a time correlation feature.
Specifically, the process of achieving water flow demand is as follows: the method comprises the steps of obtaining a historical water consumption record of a user, wherein the historical water consumption record comprises water consumption and water consumption time in the past period (such as the past month), analyzing the trend characteristics of the user water consumption according to the water consumption and the water consumption time in the historical water consumption record of the user, specifically, the water consumption is taken as an abscissa, the water consumption is taken as an ordinate, a water consumption change curve is drawn according to the historical water consumption record of the user, the trend is dynamically changed according to the water consumption change curve, the trend characteristics are taken as the water consumption trend characteristics, the water consumption trend characteristics comprise rising, falling and stabilizing, various trend characteristics can exist in unit time, such as the water consumption of the user is larger in the morning and evening, the water consumption of the user is lower in the unit time, multiple trend change characteristics can occur, the water consumption requirement is obtained based on the dynamic water level adjustment characteristics, the water consumption requirement is obtained, namely, the water consumption requirement characteristics are obtained according to the trend characteristics of the water consumption of the user in different times, the water level in the water tank is adjusted, and the water level of the water tank is adjusted when the water consumption is lower, so that the purpose of saving energy is achieved.
Based on the above analysis, the disclosure provides an optimal control method for a solar heat pump, in this embodiment, by respectively analyzing the influence of ambient temperature and solar heat on water temperature, performing heat fusion, obtaining a fusion deviation temperature value, further obtaining an optimal control parameter combination according to the fusion deviation temperature value to control the heat pump, so as to achieve auxiliary cooperation of a solar water heater and the heat pump, prevent a water pipe from being frozen while maximally utilizing solar energy, and achieve the technical effects of energy conservation and consumption reduction.
Based on the same inventive concept as the optimal control method for a solar heat pump in the foregoing embodiments, as shown in fig. 4, the present disclosure further provides an optimal control system for a solar heat pump, the system comprising:
the weather information acquisition module 11 is used for acquiring weather information through a big data platform, and acquiring solar sunlight time, solar sunlight intensity and temperature information based on the weather information;
the heat prediction module 12 is configured to predict the solar sunlight time and the solar sunlight intensity by using the heat prediction module 12 as input variables, and obtain heat prediction information;
A temperature change curve construction module 13, wherein the temperature change curve construction module 13 is used for constructing a temperature change curve based on the temperature information;
the alignment heat fusion analysis module 14 is configured to perform alignment heat fusion analysis on the heat prediction information by using the temperature change curve, and determine a fusion deviation temperature value, where the fusion deviation temperature value is used for describing a temperature difference value after heat fusion;
the fusion deviation temperature value judging module 15 is used for judging whether the fusion deviation temperature value reaches a preset adjustment threshold value or not when the fusion deviation temperature value is a negative value;
the control parameter optimization module 16, wherein the control parameter optimization module 16 is used for constructing an optimization space based on the fusion deviation temperature value, the heat pump operation parameter and the temperature information when the fusion deviation temperature value is reached, and obtaining an optimized control parameter combination;
and the optimizing control module 17 is used for generating a control instruction based on the optimizing control parameter combination to control the operation of the heat pump.
Further, the system further comprises:
the heat prediction change curve generation module is used for generating a heat prediction change curve based on the heat prediction information;
The curve mapping relation analysis module is used for obtaining a curve mapping characteristic set by carrying out curve mapping relation analysis on the temperature change curve and the heat prediction change curve, wherein the temperature change curve and the heat prediction change curve have a time corresponding relation;
the characteristic fusion analysis module is used for carrying out characteristic fusion analysis on each time based on the curve mapping characteristic set to determine a fusion difference characteristic set;
and the fusion deviation temperature value acquisition module is used for determining the heat difference value of each time point based on the fusion difference characteristic set to acquire the fusion deviation temperature value.
Further, the system further comprises:
the characteristic time sequence chain construction module is used for constructing a characteristic time sequence chain in a time sequence based on the curve mapping characteristic set;
the target heating temperature acquisition module is used for acquiring a target heating temperature;
the first node heat balance determining module is used for carrying out temperature-heat absorption fusion on a first time node based on the characteristic time sequence chain and the target heating temperature to determine a first node heat balance;
The second node heat balance acquisition module is used for carrying out temperature-heat absorption fusion on a second time node based on the characteristic time sequence chain and the target heating temperature, and carrying out fusion on the fusion result and the first node heat balance to obtain a second node heat balance;
the N-th node heat balance acquisition module is used for carrying out temperature-heat absorption fusion on an N-th time node based on the characteristic time sequence chain and the target heating temperature and carrying out fusion on the N-th time node and the N-1-th node heat balance by utilizing a fusion result to obtain the N-th node heat balance;
and the heat pump control module is used for controlling the heat pump according to the first node heat balance and the second node heat balance until the Nth node heat balance.
Further, the system further comprises:
the system comprises a historical pipeline fault case acquisition module, a fault detection module and a fault detection module, wherein the historical pipeline fault case acquisition module is used for acquiring a historical pipeline fault case;
the fault feature analysis module is used for carrying out fault feature analysis based on the historical pipeline fault cases and determining a fault feature set;
The preset adjustment threshold determining module is used for carrying out fault environment temperature-heat characteristic analysis according to the fault characteristic set and determining a temperature-heat deviation threshold as the preset adjustment threshold.
Further, the system further comprises:
the water tank information acquisition module is used for acquiring volume information and water level limiting information of the water storage tank;
the heat balance analysis module is used for carrying out heat balance analysis according to the fusion deviation temperature value and a preset adjustment threshold value, and determining water flow requirements;
the water tank maintenance amount determining module is used for obtaining water tank maintenance amount according to the volume information of the water storage tank, the water level limiting information and the water flow demand;
and the optimization variable adding module is used for taking the maintenance quantity of the water tank as an optimization variable and adding the maintenance quantity of the water tank into the optimization space.
Further, the system further comprises:
the target adjusting heat determining module is used for determining target adjusting heat according to the fusion deviation temperature value;
the energy consumption analysis module is used for determining an operation heat-energy consumption corresponding relation according to the heat pump operation parameters;
The fitness function construction module is used for constructing an energy-saving target fitness function according to the corresponding relation between the running heat and the energy consumption and the target regulating heat by taking the temperature information as a constraint condition;
and the fitness function module is used for establishing the optimization space based on the energy-saving target fitness function and performing iterative optimization to obtain the optimization control parameter combination.
Further, the system further comprises:
the water temperature determining module is used for determining the water temperature according to the fusion deviation temperature value when the fusion deviation temperature value is a positive value;
the heat pump auxiliary control parameter determining module is used for obtaining a water temperature difference when the water temperature does not meet the preset temperature requirement, and performing parameter matching based on the water temperature difference and the heat pump operation parameter to determine the heat pump auxiliary control parameter.
Further, the system further comprises:
the historical water use record acquisition module is used for acquiring a historical water use record of a user;
the trend feature analysis module is used for carrying out trend feature analysis of the water consumption of the user according to the historical water consumption record of the user and determining the trend feature of the water consumption;
The water level dynamic adjustment feature determining module is used for determining a water level dynamic adjustment feature based on the water consumption trend feature to obtain the water flow demand, and the water flow demand has a time correlation feature.
A specific example of an optimization control method for a solar heat pump in the foregoing embodiment is also applicable to an optimization control system for a solar heat pump in the present embodiment, and it will be apparent to those skilled in the art from the foregoing detailed description of an optimization control method for a solar heat pump in the present embodiment, so that details thereof will not be described herein for brevity. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel, sequentially, or in a different order, provided that the desired results of the disclosed aspects are achieved, and are not limited herein.
The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.
Claims (7)
1. An optimal control method for a solar heat pump, the method comprising:
acquiring weather information through a big data platform, and acquiring sun sunshine time, sun sunshine intensity and temperature information based on the weather information;
taking the sun sunshine time and the sun sunshine intensity as input variables, and predicting through a heat prediction model to obtain heat prediction information;
constructing a temperature change curve based on the temperature information;
carrying out alignment heat fusion analysis on the heat prediction information by utilizing the temperature change curve to determine a fusion deviation temperature value, wherein the fusion deviation temperature value is used for describing a temperature difference value after heat fusion;
when the fusion deviation temperature value is a negative value, judging whether the fusion deviation temperature value reaches a preset adjustment threshold value or not;
When the fusion deviation temperature value reaches a preset adjustment threshold value, an optimization space is constructed based on the fusion deviation temperature value, the heat pump operation parameters and the temperature information, and an optimization control parameter combination is obtained;
generating a control instruction based on the optimized control parameter combination to control the operation of the heat pump;
carrying out alignment heat fusion analysis on the heat prediction information by utilizing the temperature change curve to determine a fusion deviation temperature value, wherein the method comprises the following steps of:
generating a thermal prediction change curve based on the thermal prediction information;
the curve mapping relation analysis is carried out on the temperature change curve and the heat prediction change curve to obtain a curve mapping characteristic set, wherein the temperature change curve and the heat prediction change curve have a time corresponding relation;
performing feature fusion analysis on each time based on the curve mapping feature set to determine a fusion difference feature set;
determining the heat difference value of each time point based on the fusion difference feature set to obtain the fusion deviation temperature value;
performing feature fusion analysis on each time based on the curve mapping feature set to determine a fusion difference feature set, including:
constructing a characteristic time sequence chain in a time sequence based on the curve mapping characteristic set;
Acquiring a target heating temperature;
based on the characteristic time sequence chain and the target heating temperature, carrying out temperature-heat absorption fusion on a first time node, and determining a first node heat balance;
based on the characteristic time sequence chain and the target heating temperature, performing temperature-heat absorption fusion on a second time node, and fusing the fusion result with the first node heat balance to obtain a second node heat balance;
and so on, based on the characteristic time sequence chain and the target heating temperature, carrying out temperature-heat absorption fusion on an N-th time node, and carrying out fusion on the fusion result and the N-1-th node heat balance to obtain the N-th node heat balance;
and controlling the heat pump according to the first node heat balance and the second node heat balance until the N node heat balance.
2. The method of claim 1, wherein determining whether the fusion deviation temperature value reaches a preset adjustment threshold value comprises, before:
obtaining a historical pipeline fault case;
performing fault feature analysis based on the historical pipeline fault cases to determine a fault feature set;
and carrying out fault environment temperature-heat characteristic analysis according to the fault characteristic set, and determining a temperature-heat deviation threshold value as the preset adjustment threshold value.
3. The method of claim 1, wherein the method further comprises:
acquiring volume information and water level limiting information of a water storage tank;
performing heat balance analysis according to the fusion deviation temperature value and a preset adjustment threshold value, and determining water flow demand;
obtaining a water tank maintenance amount according to the volume information, the water level limit information and the water flow demand of the water tank;
and taking the maintenance quantity of the water tank as an optimization variable, and adding the maintenance quantity of the water tank into the optimization space.
4. The method of claim 1, wherein constructing an optimization space based on the fusion deviation temperature value, heat pump operation parameters, and temperature information to obtain an optimized control parameter combination, comprises:
determining target adjustment heat according to the fusion deviation temperature value;
determining an operation heat-energy consumption corresponding relation according to the heat pump operation parameters;
taking the temperature information as a constraint condition, and constructing an energy-saving target fitness function according to the corresponding relation between the running heat and the energy consumption and the target regulating heat;
and establishing the optimization space based on the energy-saving target fitness function, and performing iterative optimization to obtain the optimization control parameter combination.
5. The method of claim 1, wherein the method further comprises:
When the fusion deviation temperature value is a positive value, determining the water temperature according to the fusion deviation temperature value;
when the water temperature does not meet the preset temperature requirement, obtaining a water temperature difference, and carrying out parameter matching based on the water temperature difference and the heat pump operation parameter to determine the heat pump auxiliary control parameter.
6. A method as claimed in claim 3, wherein the method further comprises:
obtaining a historical water use record of a user;
according to the historical water consumption record of the user, carrying out user water consumption trend feature analysis and determining water consumption trend features;
and determining a dynamic water level adjustment feature based on the water consumption trend feature to obtain the water flow demand, wherein the water flow demand has a time correlation feature.
7. An optimal control system for a solar heat pump, the system comprising:
the weather information acquisition module is used for acquiring weather information through the big data platform and acquiring solar sunlight time, solar sunlight intensity and temperature information based on the weather information;
the heat prediction module is used for predicting the sun sunshine time and the sun sunshine intensity serving as input variables through a heat prediction model to obtain heat prediction information;
The temperature change curve construction module is used for constructing a temperature change curve based on the temperature information;
the alignment heat fusion analysis module is used for carrying out alignment heat fusion analysis on the heat prediction information by utilizing the temperature change curve to determine a fusion deviation temperature value, wherein the fusion deviation temperature value is used for describing a temperature difference value after heat fusion;
the fusion deviation temperature value judging module is used for judging whether the fusion deviation temperature value reaches a preset adjustment threshold value or not when the fusion deviation temperature value is a negative value;
the control parameter optimization module is used for constructing an optimization space based on the fusion deviation temperature value, the heat pump operation parameter and the temperature information when the fusion deviation temperature value reaches a preset adjustment threshold value, and obtaining an optimized control parameter combination;
the optimizing control module is used for generating a control instruction based on the optimizing control parameter combination to control the operation of the heat pump;
the system further comprises:
the heat prediction change curve generation module is used for generating a heat prediction change curve based on the heat prediction information;
The curve mapping relation analysis module is used for obtaining a curve mapping characteristic set by carrying out curve mapping relation analysis on the temperature change curve and the heat prediction change curve, wherein the temperature change curve and the heat prediction change curve have a time corresponding relation;
the characteristic fusion analysis module is used for carrying out characteristic fusion analysis on each time based on the curve mapping characteristic set to determine a fusion difference characteristic set;
the fusion deviation temperature value acquisition module is used for determining the heat difference value of each time point based on the fusion difference characteristic set to obtain the fusion deviation temperature value;
the characteristic time sequence chain construction module is used for constructing a characteristic time sequence chain in a time sequence based on the curve mapping characteristic set;
the target heating temperature acquisition module is used for acquiring a target heating temperature;
the first node heat balance determining module is used for carrying out temperature-heat absorption fusion on a first time node based on the characteristic time sequence chain and the target heating temperature to determine a first node heat balance;
The second node heat balance acquisition module is used for carrying out temperature-heat absorption fusion on a second time node based on the characteristic time sequence chain and the target heating temperature, and carrying out fusion on the fusion result and the first node heat balance to obtain a second node heat balance;
the N-th node heat balance acquisition module is used for carrying out temperature-heat absorption fusion on an N-th time node based on the characteristic time sequence chain and the target heating temperature and carrying out fusion on the N-th time node and the N-1-th node heat balance by utilizing a fusion result to obtain the N-th node heat balance;
and the heat pump control module is used for controlling the heat pump according to the first node heat balance and the second node heat balance until the Nth node heat balance.
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