CN112728737A - Air conditioner and cloud server - Google Patents
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
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Abstract
The invention discloses an air conditioner and a cloud server, wherein the air conditioner comprises: the controller is provided with a random forest prediction model, receives values of a plurality of operating parameters of the air conditioner and inputs the values into the random forest prediction model, and the random forest prediction model outputs the predicted energy efficiency ratio of the air conditioner; the plurality of operating parameters of the air conditioner comprise any combination of evaporator water inlet temperature TEI, evaporator water outlet temperature TEO, condenser water inlet temperature TCI, condenser water outlet temperature TCO, heat exchanger water inlet temperature TSI, condenser loop temperature TS0, heat exchanger water outlet temperature TBI, evaporator loop temperature TBO, building water inlet temperature CTI, building water outlet temperature CTO, steam heating quantity kW, evaporator flow TEA, condenser flow TCA, condenser temperature difference characteristic value TRE and refrigerant temperature TRC. The air conditioner can effectively operate on a large data set and has extremely high accuracy. Input samples with high dimensional features can be processed. Not easy to fall into overfitting and has good anti-noise capability.
Description
Technical Field
The invention relates to the technical field of electric appliances, in particular to a cloud server and an air conditioner.
Background
With the improvement of living standard of people, the air conditioner becomes a necessary product in each family, and meanwhile, higher requirements are also made on the intelligent level of the air conditioner. Besides the intelligence level of the air conditioner, the control of the energy consumption of the air conditioner is also an important aspect.
All large air conditioner manufacturers strive to improve the energy efficiency ratio of the air conditioner so that the air conditioner can operate under higher energy efficiency and meet the requirement of energy conservation. The method mainly comprises the following two aspects of research, wherein firstly, the method starts from the forward control of the air conditioner, obtains detailed operation parameters of the air conditioner through experiments, adjusts a control algorithm to achieve energy-saving control, and the other aspect is to predict the energy efficiency ratio of the air conditioner to be used as a feedback input of an air conditioner control system to complete more accurate control of the air conditioner.
At present, various methods are used for predicting the air conditioner energy efficiency, such as linear regression, Bayesian estimation algorithm, genetic algorithm and the like, but the air conditioner energy efficiency is influenced by various factors and parameters, and is a very complex nonlinear system, and the accurate prediction effect is difficult to obtain by the traditional method.
Disclosure of Invention
In order to solve the problems of large calculation amount and poor precision of energy efficiency ratio prediction of an air conditioner in the prior art, the invention provides the air conditioner and a cloud server, which are based on a random forest prediction model, are not easy to over-fit, have higher training speed and can more accurately predict the energy efficiency of a multi-split air conditioning system.
In order to achieve the purpose, the invention adopts the following technical scheme:
the present invention provides an air conditioner, comprising:
a controller configured with a random forest prediction model, the controller receiving values of a plurality of operating parameters of an air conditioner and inputting the values to the random forest prediction model, the random forest prediction model outputting a predicted energy efficiency ratio of the air conditioner;
the air conditioner comprises an air conditioner body, and is characterized in that a plurality of operation parameters of the air conditioner body comprise any combination of evaporator water inlet temperature TEI, evaporator water outlet temperature TEO, condenser water inlet temperature TCI, condenser water outlet temperature TCO, heat exchanger water inlet temperature TSI, condenser loop temperature TS0, heat exchanger water outlet temperature TBI, evaporator loop temperature TBO, building water inlet temperature CTI, building water outlet temperature CTO, steam heating quantity, evaporator flow TEA, condenser flow TCA, condenser temperature difference characteristic value TRE and refrigerant temperature TRC.
Further, the method for configuring the random forest prediction model by the controller comprises the following steps:
selecting parameter variables, and selecting any parameter variable combination from the multiple operation parameters, wherein the characteristic number in the parameter variable combination is M;
acquiring historical data of each parameter variable in the parameter variable combination to obtain a plurality of pieces of historical data, and acquiring an energy efficiency ratio corresponding to each piece of historical data;
constructing a random forest prediction model, comprising the following steps:
constructing a training set of each classification tree, and respectively randomly and replaceably extracting N training samples from the historical data to serve as the training set of each classification tree;
and respectively training each classification tree, randomly selecting M feature subsets from the M features as training features of each classification tree, wherein M is more than or equal to 1 and less than M.
Further, the method also comprises the step of preprocessing the historical data before the random forest prediction model is constructed, and the historical data is converted into dimensionless numerical values.
Further, the preprocessing the historical data comprises: and carrying out normalization processing on the historical data, and converting each data into a range interval of 0-1.
Further, the method also comprises a step of estimating the out-of-bag error rate of the random forest prediction model, and comprises the following steps:
inputting each piece of data in the historical data into a classification tree which is not extracted as a training sample to obtain a classification result of the classification tree;
determining an estimated classification result according to the classification result;
comparing the estimated classification result with the actual classification result, and counting the number of misclassifications;
the ratio of the number of misclassifications to the total number of samples is used as the out-of-bag error rate of the random forest prediction model;
and when the error rate outside the bag is greater than a set threshold value, adjusting the value m.
Further, in the step of estimating the out-of-bag error rate of each classification tree, if a plurality of classification results output by different classification trees are obtained, the estimated classification result of the sample is determined by adopting a majority voting mechanism.
Further, the energy efficiency prediction of the air conditioner comprises:
acquiring a parameter variable combination in a random forest prediction model, and acquiring values of each operating parameter of the air conditioner according to parameter variables in the parameter variable combination;
inputting the values of the operating parameters into the random forest prediction model;
each classification tree in the random forest prediction model respectively obtains the value of an operation parameter required by prediction and outputs a prediction result;
and determining the predicted energy efficiency ratio according to the prediction result output by each classification tree.
Furthermore, a majority voting mechanism is adopted, and the prediction result with the largest proportion serves as the prediction energy efficiency ratio.
Further, the method further comprises the step of sending the predicted energy efficiency ratio to the air conditioner and adjusting and controlling the air conditioner.
The invention also provides a cloud server, which comprises:
a controller configured with a random forest prediction model, the controller receiving values of a plurality of operating parameters of an air conditioner through a network and inputting the values to the random forest prediction model, the random forest prediction model outputting a predicted energy efficiency ratio of the air conditioner;
the air conditioner comprises an air conditioner body, and is characterized in that a plurality of operation parameters of the air conditioner body comprise any combination of evaporator water inlet temperature TEI, evaporator water outlet temperature TEO, condenser water inlet temperature TCI, condenser water outlet temperature TCO, heat exchanger water inlet temperature TSI, condenser loop temperature TS0, heat exchanger water outlet temperature TBI, evaporator loop temperature TBO, building water inlet temperature CTI, building water outlet temperature CTO, steam heating quantity, evaporator flow TEA, condenser flow TCA, condenser temperature difference characteristic value TRE and refrigerant temperature TRC.
Compared with the prior art, the technical scheme of the invention has the following technical effects: can be effectively operated on a large data set and has excellent accuracy. Input samples with high dimensional features can be processed without the need for dimension reduction. The selection of the samples and the selection of the classification trees to the characteristic parameters during the training of the random forest prediction model have randomness, so that the random forest is not easy to fall into overfitting, and the noise resistance is good.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic block diagram of a COP prediction model of an air conditioner according to the present invention;
FIG. 2 is a diagram of the effect of the random forest algorithm prediction in an embodiment of the air conditioner;
FIG. 3 is a diagram illustrating the prediction effect of an extreme random forest algorithm in an embodiment of the air conditioner according to the present invention;
fig. 4 is a diagram of the predicted effect of the gradient boost algorithm in an embodiment of the air conditioner according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it is to be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience of description and simplicity of description, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and thus, are not to be construed as limiting the present invention.
In the description of the present invention, it should be noted that the terms "mounted," "connected," and "connected" are to be construed broadly and may be, for example, fixedly connected, detachably connected, or integrally connected unless otherwise explicitly stated or limited. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art. In the foregoing description of embodiments, the particular features, structures, materials, or characteristics may be combined in any suitable manner in any one or more embodiments or examples.
The terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless otherwise specified.
The air conditioner performs a refrigeration cycle and a heating cycle of the air conditioner by using a compressor, a condenser, an expansion valve, and an evaporator, and control is performed by a controller, achieving flow direction control of refrigerant, opening degree control of the expansion valve, and the like. The refrigeration cycle and the heating cycle include a series of processes involving compression, condensation, expansion, and evaporation, and supply refrigerant to air that has been conditioned and heat-exchanged.
The compressor compresses a refrigerant gas in a high-temperature and high-pressure state and discharges the compressed refrigerant gas. The discharged refrigerant gas flows into the condenser. The condenser condenses the compressed refrigerant into a liquid phase, and heat is released to the surrounding environment through the condensation process.
The expansion valve expands the liquid-phase refrigerant in a high-temperature and high-pressure state condensed in the condenser into a low-pressure liquid-phase refrigerant. The evaporator evaporates the refrigerant expanded in the expansion valve and returns the refrigerant gas in a low-temperature and low-pressure state to the compressor. The evaporator can achieve a cooling effect by heat-exchanging with a material to be cooled using latent heat of evaporation of a refrigerant. The air conditioner can adjust the temperature of the indoor space throughout the cycle.
The outdoor unit of the air conditioner refers to a portion of a refrigeration cycle including a compressor and an outdoor heat exchanger, the indoor unit of the air conditioner includes an indoor heat exchanger, and an expansion valve may be provided in the indoor unit or the outdoor unit.
The indoor heat exchanger and the outdoor heat exchanger serve as a condenser or an evaporator. When the indoor heat exchanger is used as a condenser, the air conditioner is used as a heater in a heating mode, and when the indoor heat exchanger is used as an evaporator, the air conditioner is used as a cooler in a cooling mode.
The heat pump type air conditioning system uses water as a medium for air conditioning, during a heating cycle, a compressor compresses a low-temperature low-pressure refrigerant into a high-temperature high-pressure refrigerant to be discharged, the high-temperature high-pressure refrigerant enters a gas cooler to perform reverse heat exchange with water entering the gas cooler and then is changed into a medium-temperature high-pressure refrigerant, the medium-temperature high-pressure refrigerant is throttled by a throttling device to be changed into a low-temperature low-pressure gas-liquid two-phase refrigerant, the gas-liquid two-phase refrigerant is evaporated in an evaporator to absorb heat and then is changed into a low-temperature low-pressure gas refrigerant to return to the compressor, a basic cycle.
During the refrigeration cycle, the flow direction of the refrigerant is opposite to that of the heating cycle, the refrigerant absorbs heat from water, and the cooled low-temperature water circulates indoors for refrigeration regulation.
The energy efficiency ratio (CPO) is an important parameter for evaluating the performance of an air conditioner, and various air conditioner manufacturers strive to improve the energy efficiency ratio of the air conditioner so that the air conditioner can operate under higher energy efficiency to meet the requirement of energy conservation. The method mainly comprises the following two aspects of research, wherein firstly, the method starts from the forward control of the air conditioner, obtains detailed operation parameters of the air conditioner through experiments, adjusts a control algorithm to achieve energy-saving control, and the other aspect is to predict the energy efficiency ratio of the air conditioner to be used as a feedback input of an air conditioner control system to complete more accurate control of the air conditioner.
A Random Forest (RF) is a highly flexible machine learning algorithm, and has a plurality of classification trees inside, each classification tree can independently perform decision-making judgment on input data, output a decision-making result, and obtain a final output result of the Random Forest according to output results of all classification trees.
According to the method, the energy efficiency value is predicted by adopting a random forest according to the air conditioner operation data and the energy efficiency value.
Example one
The air conditioner of the embodiment comprises a controller, as shown in fig. 1, configured with a random forest prediction model, wherein the controller receives values of a plurality of operating parameters of the air conditioner and inputs the values into the random forest prediction model, and the random forest prediction model outputs a predicted energy efficiency ratio of the air conditioner;
the plurality of operating parameters of the air conditioner comprise any combination of evaporator water inlet temperature TEI, evaporator water outlet temperature TEO, condenser water inlet temperature TCI, condenser water outlet temperature TCO, heat exchanger water inlet temperature TSI, condenser loop temperature TS0, heat exchanger water outlet temperature TBI, evaporator loop temperature TBO, building water inlet temperature CTI, building water outlet temperature CTO, steam heating quantity kW, evaporator flow TEA, condenser flow TCA, condenser temperature difference characteristic value TRE and refrigerant temperature TRC.
The parameter variables selected in the invention are mainly parameters which have a large influence on energy efficiency ratio prediction, and for a heat pump type air conditioning system, the parameter variables mainly comprise evaporator water inlet temperature TEI, evaporator water outlet temperature TEO, condenser water inlet temperature TCI, condenser water outlet temperature TCO, heat exchanger water inlet temperature TSI, condenser loop temperature TS0, heat exchanger water outlet temperature TBI, evaporator loop temperature TBO, building water inlet temperature CTI, building water outlet temperature CTO, steam heating amount kW, evaporator flow TEA, condenser flow TCA, condenser temperature difference representation value TRE and refrigerant temperature TRC.
The air conditioner parameters except for the COP can be obtained through sensors, and for obtaining historical COP, a more accurate value can be obtained by calculating the COP as the refrigerating capacity/loss power, but for transient COP, the COP is difficult to obtain, and the COP is one of the advantages of energy efficiency prediction.
The method for configuring the random forest prediction model by the controller comprises the following steps:
selecting parameter variables, and selecting any parameter variable combination from the multiple operation parameters. In this embodiment, all the above parameters are taken as examples for explanation.
Based on the parameter variables, an input parameter vector X ═ is established
[ TEI, TEO, TCI, TCO, TSI, TSO, TBI, TBO, CTI, CTO, Kw, TEA, TCA, TRE, TRC ]. And the historical data set of the air conditioning system can be obtained by detecting the states of the air conditioning unit at different time points. X ═ X1, X2 … Xi for the dataset, where i ═ N, N denotes the total number of acquisitions. Based on the fact that the dimension of the input vector is 15 dimensions and the same historical COP data is used as a one-dimensional output variable, the established algorithm model predicts the value of the one-dimensional COP by inputting a 15-dimensional data set.
Acquiring historical data of each parameter variable in the parameter variable combination to obtain a plurality of pieces of historical data, and acquiring an energy efficiency ratio corresponding to each piece of historical data;
constructing a random forest prediction model, comprising the following steps:
constructing a training set of each classification tree, and respectively randomly and replaceably extracting N training samples from the historical data to serve as the training set of each classification tree;
and respectively training each classification tree, randomly selecting M feature subsets from the M features as training features of each classification tree, wherein M is more than or equal to 1 and less than M.
The size of the training sample is N, and for each classification tree, N training samples are randomly and replaceably extracted from a training set to serve as the training set of the tree; therefore, the training set of each classification tree is different and contains repeated training samples.
Each classification tree in the random forest prediction model is a classifier, and each classifier corresponds to a classification result for one input sample. And the random forest integrates the classification voting results of all classifiers and designates the class with the highest voting frequency as the final output.
The selection of the samples and the selection of the classification trees on the characteristic parameters during the training of the random forest prediction model have randomness, so that the random forest is not easy to fall into overfitting, and the noise resistance is good, for example, when a certain air conditioner operation parameter is short of and saved, the final judgment result is not influenced.
The random forest prediction model ensemble learning solves the problem of single prediction by establishing a plurality of classifier combinations. Its working principle is to generate multiple classifiers/models, each of which learns and makes predictions independently. These predictions are eventually combined into a single prediction and therefore are superior to making predictions for any single classification.
The collected operation parameters include temperature, flow rate, and the like, and the unit and the measurement mode are different, and in this embodiment, it is preferable to further include a step of preprocessing the historical data before the random forest prediction model is constructed, and convert the historical data into a dimensionless numerical value.
As a preferred embodiment, the preprocessing of the historical data comprises: and (3) carrying out normalization processing on the historical data, converting each data into a range interval of 0-1, removing unit limitation of the data, and converting the unit limitation into a dimensionless numerical value.
The normalization processing method comprises the following steps: respectively forming historical data of each operation parameter into a data set of characteristic variables, and carrying out normalization calculation on each characteristic variable in the data set:
X＝(Xori-Xmin)/(Xmax–Xmin)。
wherein Xori represents raw data of a characteristic variable, Xmin represents a minimum value in a data set of a certain characteristic variable, and Xmax represents a maximum value in the data set.
In order to enable the output result of the random forest prediction model trained by using the historical data to be more accurate, the method further comprises the step of estimating the out-of-bag error rate of the random forest prediction model.
The key problem for constructing the random forest is how to select the optimal m, and the problem to be solved is mainly based on calculating out-of-bag error rate oob error (out-of-bag error).
Random forest has an important advantage in that it is not necessary to cross-validate it or use a separate test set to obtain an unbiased estimate of the error. It can be evaluated internally, i.e. an unbiased estimate of the error can be established during the generation.
In building each classification tree, we use a different random and ex-place draw on the training set. So for each classification tree (assuming for the kth classification tree) about 1/3 training examples did not participate in the generation of the kth classification tree, which are called oob samples of the kth classification tree.
This sampling feature allows us to make oob estimates, which are calculated as follows:
inputting each piece of data in the historical data into a classification tree which is not extracted as a training sample to obtain a classification result of the classification tree;
determining an estimated classification result according to the classification result;
comparing the estimated classification result with the actual classification result, and counting the number of misclassifications;
the ratio of the number of misclassifications to the total number of samples is used as the out-of-bag error rate of the random forest prediction model;
and when the error rate outside the bag is greater than a set threshold value, adjusting the value m.
oob false score is an unbiased estimate of random forest generalization error, the result of which approximates k-fold cross validation which requires a large number of computations.
In the step of estimating the error rate outside the bag for each classification tree, if a plurality of classification results output by different classification trees are obtained, the estimated classification result of the sample is determined by adopting a majority voting mechanism.
In this embodiment, the energy efficiency prediction for the air conditioner includes:
acquiring a parameter variable combination in a random forest prediction model, and acquiring values of each operating parameter of the air conditioner according to parameter variables in the parameter variable combination;
inputting the values of the operation parameters into a random forest prediction model;
respectively acquiring the values of the operation parameters required by prediction by each classification tree in the random forest prediction model, and outputting prediction results;
and determining the predicted energy efficiency ratio according to the prediction result output by each classification tree.
And (4) adopting a majority voting mechanism, and taking the prediction result with the largest proportion as the prediction energy efficiency ratio.
The method further comprises the step of measuring the prediction accuracy of the random forest prediction model.
Two metrics are used to measure the accuracy of the model, the first is the root mean square error as shown in the following equation:
wherein n is the total number of the evaluation samples, is the actual value of the ith sample, is the model predicted value of the ith sample, RMSE represents the root mean square error of the energy efficiency prediction data and the actual data, RMSE (model) represents the root mean square error of the model prediction data and the actual data, the total score is obtained by calculating the scores of three prediction indexes and then averaging, R2 score is a deterministic correlation coefficient used for measuring the prediction effect of the model on unknown samples, the best score is 1.0, and the value can also be a negative number.
Usually, the minimum mean square error and the maximum interpretation variance score are guaranteed.
And sending the predicted energy efficiency ratio to the air conditioner, and adjusting and controlling the air conditioner.
The specific adjusting control method includes, but is not limited to, automatically adjusting the outlet water temperature and adjusting the operating frequency of the compressor, so as to adjust the operating output capacity of the whole air conditioner.
After the energy efficiency prediction model of the air conditioner is built based on the steps, the air conditioner collects the actual operation data of the air conditioner in actual use, and the variables are as above. After the trained model is input, the predicted COP value can be obtained, and the air conditioner is correspondingly adjusted and controlled according to the predicted value. The outstanding characteristics of the algorithm applied to the air conditioning system are simple algorithm and small operand. The algorithm is well suited for implementation within an air conditioning host.
Examples of the experiments
The experiment acquires 5000 groups of data aiming at the air conditioner to obtain an initial data set. The partial samples of the data set collected are as follows:
TABLE 1
The data set was randomly sampled, 20% as test and 80% as training.
In the experiment, a random forest, an extreme random forest and a gradient lifting three-party are respectively adopted for modeling, so that the following different model prediction effects are obtained.
As shown in fig. 2-4, fig. 2 is a diagram of the prediction effect of the random forest algorithm. Fig. 3 is a prediction effect graph of an extreme random forest algorithm, and fig. 4 is a prediction effect graph of a gradient boost algorithm.
The model accuracy indexes of the three algorithms are as follows:
the default evaluation value for random forest regression is: 0.9889919310756686
The R _ squared value for random forest regression is: 0.9889919310756686
The mean square error of the random forest regression is 6.81604194752396e-07
The mean absolute error of the random forest regression was 0.00039498043130990415
The default estimates for the extreme random forest regression are: 0.9932807078143473
The R _ squared value for the extreme random forest regression is: 0.9889919310756686
The mean square error of the extreme random forest regression is 6.81604194752396e-07
The mean absolute error of the extreme random forest regression was 0.00039498043130990415
The default estimates for gradient boost regression are: 0.9817562674862764
The R _ squared value of the gradient lifting regression is: 0.9889919310756686
The mean square error of the gradient lifting regression is 6.81604194752396e-07
The mean absolute error of the gradient boost regression is 0.00039498043130990415.
From the above comparative analysis, it can be seen that for the use scene of air conditioner energy efficiency prediction, the prediction accuracy of both the random forest and the extreme random forest is very high, and the random forest and the extreme random forest can be selected according to actual conditions in practical application.
Example two
The invention also provides a cloud server, comprising:
the controller is configured with a random forest prediction model, receives values of a plurality of operating parameters of the air conditioner through a network and inputs the values into the random forest prediction model, and the random forest prediction model outputs a predicted energy efficiency ratio of the air conditioner and sends the predicted energy efficiency ratio to the air conditioner;
the plurality of operating parameters of the air conditioner comprise any combination of evaporator water inlet temperature TEI, evaporator water outlet temperature TEO, condenser water inlet temperature TCI, condenser water outlet temperature TCO, heat exchanger water inlet temperature TSI, condenser loop temperature TS0, heat exchanger water outlet temperature TBI, evaporator loop temperature TBO, building water inlet temperature CTI, building water outlet temperature CTO, steam heating quantity kW, evaporator flow TEA, condenser flow TCA, condenser temperature difference characteristic value TRE and refrigerant temperature TRC.
The cloud server receives a plurality of operating parameters of the air conditioner through the network, predicts the energy efficiency ratio of the air conditioner and sends the predicted energy efficiency ratio to the air conditioner, and the cloud server has stronger data computing and processing capacity, predicts the energy efficiency ratio through the cloud server, is favorable for reducing occupation of air conditioner resources, and saves the cost of the air conditioner.
And after the predicted energy efficiency ratio is fed back to the air conditioner, the predicted energy efficiency ratio is used as a feedback input of an air conditioner control system to complete more accurate control of the air conditioner.
The random forest prediction model may refer to the specific record in the first embodiment, and is not described herein again.
In the foregoing description of embodiments, the particular features, structures, materials, or characteristics may be combined in any suitable manner in any one or more embodiments or examples.
In the foregoing description of embodiments, the particular features, structures, materials, or characteristics may be combined in any suitable manner in any one or more embodiments or examples.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. An air conditioner, comprising:
a controller configured with a random forest prediction model, the controller receiving values of a plurality of operating parameters of an air conditioner and inputting the values to the random forest prediction model, the random forest prediction model outputting a predicted energy efficiency ratio of the air conditioner;
the air conditioner comprises an air conditioner body, and is characterized in that a plurality of operation parameters of the air conditioner body comprise any combination of evaporator water inlet temperature TEI, evaporator water outlet temperature TEO, condenser water inlet temperature TCI, condenser water outlet temperature TCO, heat exchanger water inlet temperature TSI, condenser loop temperature TS0, heat exchanger water outlet temperature TBI, evaporator loop temperature TBO, building water inlet temperature CTI, building water outlet temperature CTO, steam heating quantity, evaporator flow TEA, condenser flow TCA, condenser temperature difference characteristic value TRE and refrigerant temperature TRC.
2. The air conditioner as claimed in claim 1, wherein the method for the controller to configure the random forest prediction model comprises:
selecting parameter variables, and selecting any parameter variable combination from the multiple operation parameters, wherein the characteristic number in the parameter variable combination is M;
acquiring historical data of each parameter variable in the parameter variable combination to obtain a plurality of pieces of historical data, and acquiring an energy efficiency ratio corresponding to each piece of historical data;
constructing a random forest prediction model, comprising the following steps:
constructing a training set of each classification tree, and respectively randomly and replaceably extracting N training samples from the historical data to serve as the training set of each classification tree;
and respectively training each classification tree, randomly selecting M feature subsets from the M features as training features of each classification tree, wherein M is more than or equal to 1 and less than M.
3. The air conditioner as claimed in claim 2, further comprising a step of preprocessing the historical data to convert it into dimensionless values before constructing the random forest prediction model.
4. The cloud server of claim 3, wherein preprocessing the historical data comprises: and carrying out normalization processing on the historical data, and converting each data into a range interval of 0-1.
5. The air conditioner of claim 2, further comprising the step of out-of-bag error rate estimation for a random forest prediction model, comprising:
inputting each piece of data in the historical data into a classification tree which is not extracted as a training sample to obtain a classification result of the classification tree;
determining an estimated classification result according to the classification result;
comparing the estimated classification result with the actual classification result, and counting the number of misclassifications;
the ratio of the number of misclassifications to the total number of samples is used as the out-of-bag error rate of the random forest prediction model;
and when the error rate outside the bag is greater than a set threshold value, adjusting the value m.
6. The air conditioner of claim 5, wherein in the step of estimating the error rate outside the bag for each classification tree, if a plurality of classification results outputted from different classification trees are obtained, the estimated classification result of the sample is determined by using a majority voting mechanism.
7. The air conditioner of claim 1, wherein the energy efficiency prediction of the air conditioner comprises:
acquiring a parameter variable combination in a random forest prediction model, and acquiring values of each operating parameter of the air conditioner according to parameter variables in the parameter variable combination;
inputting the values of the operating parameters into the random forest prediction model;
each classification tree in the random forest prediction model respectively obtains the value of an operation parameter required by prediction and outputs a prediction result;
and determining the predicted energy efficiency ratio according to the prediction result output by each classification tree.
8. The air conditioner according to claim 7, wherein a majority voting mechanism is used, and the most significant prediction result is used as the predicted energy efficiency ratio.
9. The air conditioner according to any one of claims 1 to 7, further comprising a step of sending the predicted energy efficiency ratio to the air conditioner to perform a regulation control of the air conditioner.
10. A cloud server, comprising:
a controller configured with a random forest prediction model, the controller receiving values of a plurality of operating parameters of an air conditioner through a network and inputting the values to the random forest prediction model, the random forest prediction model outputting a predicted energy efficiency ratio of the air conditioner;
the air conditioner comprises an air conditioner body, and is characterized in that a plurality of operation parameters of the air conditioner body comprise any combination of evaporator water inlet temperature TEI, evaporator water outlet temperature TEO, condenser water inlet temperature TCI, condenser water outlet temperature TCO, heat exchanger water inlet temperature TSI, condenser loop temperature TS0, heat exchanger water outlet temperature TBI, evaporator loop temperature TBO, building water inlet temperature CTI, building water outlet temperature CTO, steam heating quantity, evaporator flow TEA, condenser flow TCA, condenser temperature difference characteristic value TRE and refrigerant temperature TRC.
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