CN117515802A - Fixed-frequency central air conditioner daily-load prediction method considering running state of air conditioner - Google Patents
Fixed-frequency central air conditioner daily-load prediction method considering running state of air conditioner Download PDFInfo
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Abstract
The invention provides a fixed-frequency central air conditioner daily-load prediction method considering the running state of an air conditioner, which comprises the steps of acquiring n groups of monitoring data in a historical period; carrying out normalization processing on the monitoring data to obtain dimensionless new data, carrying out correlation analysis on the new data influencing the central air conditioning cold demand factors and the central air conditioning system load rate respectively, and distributing weights according to correlation analysis results to obtain a contribution value historical data sequence; establishing an EPGA-BPNN model; obtaining a contribution value data sequence of a set period before a predicted period; inputting the trained EPGA-BPNN model to obtain the central air conditioning system load rate in a prediction period; judging the running state of a central air conditioning system in a prediction period, and formulating a corresponding regulation strategy in the prediction period; the method can improve the accuracy of the daily load prediction of the central air conditioner, can realize the accurate regulation and control of the central air conditioner, and is suitable for the prediction of the load and the operation stage of the air conditioner.
Description
Technical Field
The invention relates to a fixed-frequency central air conditioner daily-load prediction method considering the running state of an air conditioner, and belongs to the technical field of central air conditioner load prediction.
Background
With the development of Chinese economy at a high speed, the building scale in cities is continuously enlarged, and a central air conditioning system is used as one of main energy consumption equipment of the building, so that electricity consumption of the central air conditioning system occupies a large proportion of urban electricity consumption. The load prediction and regulation strategy of the central air conditioner is researched, so that the energy consumption of the central air conditioner can be greatly reduced, and the purposes of reducing the running cost of the air conditioner, saving energy and reducing emission can be well realized.
Central air conditioning load prediction generally utilizes mathematical models and algorithms to predict the load condition of the central air conditioning system over a period of time. Central air conditioning load prediction generally involves a plurality of factors affecting the state of the air conditioner, including indoor and outdoor humiture, wind speed, people flow, building conditions, etc. By collecting and analyzing this data, a predictive model can be built. The method for establishing the prediction model comprises mechanism modeling, data driving modeling and the like. The mechanism modeling is also called white-box modeling, and is to build a model according to the internal structural principle of an object to be researched, the change of each heat transfer medium and each external related factor. The model built based on the mechanism is very cumbersome, a large number of differential equations need to be calculated when designing the controller, and therefore there are great limitations in engineering applications. The data-driven modeling is also called a black box model, and the data-driven model is modeled by utilizing experimental data of a system, does not need a great deal of theoretical knowledge, and only needs to analyze the experimental data and establish a model structure through the relationship between input and output.
At present, a central air conditioner load prediction model and a regulation strategy research are still in a preliminary stage, the central air conditioner load resource collection accuracy is not high, the regulation refinement level is not enough, and a more accurate prediction method for the central air conditioner load needs to be excavated. Therefore, the fixed-frequency central air conditioner daily-load prediction method considering the running state of the air conditioner is researched, the accurate regulation and control of the central air conditioner load are facilitated, and flexible interaction with power grid dispatching is realized.
The above-mentioned problems are to be considered and solved in the course of predicting daily load of constant-frequency central air conditioner taking into account the running state of air conditioner.
Disclosure of Invention
The invention aims to provide a fixed-frequency central air conditioner daily front load prediction method considering the running state of an air conditioner, which solves the problem that the accuracy of central air conditioner daily front load prediction in the prior art is to be improved.
The technical scheme of the invention is as follows:
a method for predicting daily load of a constant-frequency central air conditioner according to the running state of the air conditioner, which comprises the following steps,
s1, acquiring n groups of monitoring data in a historical period, wherein each group of monitoring data comprises factors influencing the cold energy requirement of a central air conditioner and the total running power of the central air conditioner system;
s2, carrying out normalization processing on the monitored data to obtain dimensionless new data, carrying out correlation analysis on the new data influencing the central air conditioning cold demand factors and the central air conditioning system load rate respectively, and distributing weights according to correlation analysis results to obtain a contribution value historical data sequence of each dimensionless new data to the central air conditioning system load rate;
s3, establishing an elite retention strategy genetic algorithm-based EPGA improved counter propagation neural network model, namely an EPGA-BPNN model, taking a contribution value historical data sequence as input data, and training the EPGA-BPNN model by combining a central air conditioning system load rate of a historical period to obtain a trained EPGA-BPNN model;
s4, acquiring monitoring data of a set period before a predicted period, and obtaining a contribution value data sequence of the set period before the predicted period;
s5, inputting the contribution value data sequence of the set period before the prediction period into the trained EPGA-BPNN model to obtain the central air conditioning system load rate of the prediction period;
s6, judging the running state of the central air conditioning system in the prediction period according to the central air conditioning system load rate in the prediction period obtained in the step S5, and formulating a corresponding regulation strategy in the prediction period according to the running state.
Further, in step S1, n sets of monitoring data of the historical period are obtained, each set of monitoring data includes factors affecting the cooling capacity requirement of the central air conditioner and total running power of the central air conditioning system, specifically, n sets of factors affecting the cooling capacity requirement of the central air conditioner are collected, including indoor temperature T in Indoor humidity H in Outdoor temperature T out Outdoor humidity H out And chilled water temperature T cw The method comprises the steps of carrying out a first treatment on the surface of the And collecting the total running power P of the corresponding central air conditioning system air The method comprises the steps of carrying out a first treatment on the surface of the Obtaining n groups of monitoring data { T ] in ,H in ,T out ,H out ,T cw ，P air }。
Further, in step S2, the monitoring data is normalized to obtain dimensionless new data, specifically, normalized monitoring data:
wherein the sequence x 1 ,x 2 ,x 3 ,x 4 ,x 5 Respectively representing input variables of indoor temperature Tin, indoor humidity Hin, outdoor temperature Tout, outdoor humidity Hout and chilled water temperature Tcw, sequence x 6 Running a total power Pair data sequence for a central air conditioning system, i.e. { x } 1 ＝T in ,x 2 ＝H in ,x 3 ＝T out ,x 4 ＝H out ,x 5 ＝T cw ,x 6 ＝P air Post-conversion to yield the output y 1 ,y 2 ,y 3 ,y 4 ,y 5 ,y 6 ∈[0,1]The output is new data without dimension.
Further, in step S2, the correlation analysis between the new data affecting the central air conditioning cooling capacity demand factor and the central air conditioning system load rate is performed respectively:
wherein i=1, 2, 6,k =1, 2, n, LR (k) is a central air conditioning system load factor sequence, a parent sequence; y is i (k) The i standardized output sequence is a subsequence; f (LR (k), y i (k) The relevance of the ith subsequence and the corresponding dimension of the parent sequence is represented, the value range is between 0 and 1, 0 represents uncorrelation, 1 represents strong relevance, and the larger the number is, the stronger the representative relevance is; ρ is the resolution factor; p and q are respectively two-stage minimum difference and two-stage maximum difference:
further, in step S2, weights are assigned according to the correlation analysis result, so as to obtain a historical data sequence of contribution values of the dimensionless new data to the load rate of the central air conditioning system, specifically, a weight a is assigned according to the correlation analysis result 1 ,A 2 ,A 3 ,A 4 ,A 5 ,A 6 The normalized data sequence y 1 ,y 2 ,y 3 ,y 4 ,y 5 ,y 6 Multiplying the distributed weight values to obtain a contribution value historical data sequence for the load rate: y is cvi (k)＝y i ·A i Wherein i=1, 2,.. 6,k =1, 2,..n.
Further, in step S3, the back propagation neural network model, namely EPGA-BPNN model, which is improved based on the elite retention policy genetic algorithm EPGA comprises a back propagation neural network model, namely BPNN model and a parameter optimization module,
BPNN model: taking a contribution value historical data sequence from w-v moment to w moment in a historical period as input data, carrying out forward propagation, and obtaining a predicted load rate from w+1 moment to w+u moment; the error between the obtained predicted load rate and the corresponding running total power of the central air conditioning system in the historical period is reversely propagated;
parameter optimization module: and carrying out iterative optimization on the network parameters of the BPNN model by adopting an elite retention strategy genetic algorithm EPGA to obtain the optimized network parameters of the BPNN model.
Further, in step S3, the contribution value historical data sequence is used as input data, and after the EPGA-BPNN model is trained by combining the central air conditioning system load rate of the historical period, the trained EPGA-BPNN model is obtained, specifically,
s31, inputting a contribution value historical data sequence from w-v time to w time in a historical period, inputting a BPNN model of the EPGA-BPNN model, and outputting a central air conditioning system load rate predicted value sequence from w+1 time to w+u time;
s32, calculating a Loss function Loss of a prediction error by the total running power of the central air conditioning system in the historical period and the load rate predicted value sequence of the central air conditioning system;
s33, initializing network parameters of the BPNN model, and taking the initialized network parameters as an initial parameter population;
s34, when a Loss function Loss of the prediction error is propagated reversely, iteratively updating the parameter population by using an elite retention strategy genetic algorithm EPGA, and repeating the steps S31-S34 until the Loss function is smaller than a set value or reaches set times, and solving to obtain an optimal network parameter value;
and S35, assigning an optimal network parameter value to the BPNN model to obtain the trained EPGA-BPNN model.
Further, in step S34, the parameter population is iteratively updated using the elite retention policy genetic algorithm EPGA, in particular,
s341, marking parameter population of elite retention strategy genetic algorithm EPGA asWherein x represents population individuals, and k represents population algebra;
s342, taking the Loss function Loss as an fitness function value of an individual in each iteration, and directly copying the individual with the best performance in the population, namely the individual with the highest fitness function value, to the next generation;
s343, old populationThe old population is +.>The other individuals of the population form a new population by the new individuals generated by genetic operator operation and the individuals with the best performance in the population together>For new->Re-calculating the fitness function value of the individual during the re-evolution; the individuals in the population that perform best are taken as the optimal network parameter values.
Further, in step S6, the operation state of the central air conditioning system in the prediction period includes a start-up phase, an operation phase, and a stop phase.
Further, in step S6, the running state of the central air conditioning system in the predicted period is determined according to the central air conditioning system load rate in the predicted period obtained in step S5, specifically,
in the load factor of the predicted period, there are any one of the following three cases that are determined as the start-up phase:
case one: the load rate continuously rises, and the load rate at the starting moment and the load rate at the ending moment rise and change rate are in a first set interval;
and a second case: the load rate of the existing time period in the predicted time period reaches the second set interval;
and a third case: the load rate continuously drops, and the load rate at the starting moment and the load rate at the ending moment drop change rate are in a third set interval;
judging the operation stage when the load rate of the prediction period is kept in a set range and the load rate change rate is in a fourth set interval;
the load factor at the start time and the load factor at the end time fall at a rate within a fifth set interval, and the load factor falls to 0, and the stop phase is determined.
The beneficial effects of the invention are as follows: according to the fixed-frequency central air conditioner daily load prediction method considering the air conditioner running state, the air conditioner running state is considered, the EPGA-BPNN model is adopted to predict the air conditioner load rate, the air conditioner running stage in the prediction period is judged according to the load rate, the accuracy of the central air conditioner daily load prediction can be improved, the accurate regulation and control of the central air conditioner can be realized, and the method is suitable for air conditioner load and running stage prediction.
Drawings
FIG. 1 is a schematic flow chart of a method for predicting daily load of a constant-frequency central air conditioner according to an embodiment of the invention, which takes into consideration the running state of the air conditioner;
FIG. 2 is a schematic diagram illustrating iterative updating of parameter populations using elite retention strategy genetic algorithm EPGA in an embodiment;
fig. 3 is a schematic diagram of a trend of load factor change corresponding to an operation state of a central air conditioning system in an embodiment.
Detailed Description
Preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
Examples
A fixed frequency central air conditioner daily load prediction method considering the running state of an air conditioner, as shown in figure 1, comprises the following steps,
s1, acquiring n groups of monitoring data of a historical period, wherein each group of monitoring data comprises factors influencing the cold energy requirement of a central air conditioner and the total running power of the central air conditioner system.
In step S1, n sets of monitoring data of the history period are acquired, each set of monitoring data including the influenceThe factors of the cold energy demand of the central air conditioner and the total running power of the central air conditioning system are specifically, n groups of factors affecting the cold energy demand of the central air conditioner are collected and comprise the indoor temperature T in Indoor humidity H in Outdoor temperature T out Outdoor humidity H out And chilled water temperature T cw Collecting through a temperature and humidity sensor; and collecting the total running power P of the corresponding central air conditioning system air The method comprises the steps of carrying out a first treatment on the surface of the Obtaining n groups of monitoring data { T ] in ,H in ,T out ,H out ,T cw ，P air }。
S2, carrying out normalization processing on the monitored data to obtain dimensionless new data, carrying out correlation analysis on the new data influencing the central air conditioning cold demand factors and the central air conditioning system load rate respectively, and distributing weights according to correlation analysis results to obtain a contribution value historical data sequence of each dimensionless new data to the central air conditioning system load rate.
In step S2, the monitoring data is normalized to obtain dimensionless new data, specifically, normalized monitoring data:
wherein the sequence x 1 ,x 2 ,x 3 ,x 4 ,x 5 Respectively representing input variables of indoor temperature Tin, indoor humidity Hin, outdoor temperature Tout, outdoor humidity Hout and chilled water temperature Tcw, sequence x 6 Running a total power Pair data sequence for a central air conditioning system, i.e. { x } 1 ＝T in ,x 2 ＝H in ,x 3 ＝T out ,x 4 ＝H out ,x 5 ＝T cw ,x 6 ＝P air Post-conversion to yield the output y 1 ,y 2 ,y 3 ,y 4 ,y 5 ,y 6 ∈[0,1]The output is new data without dimension.
In step S2, the new data affecting the cooling capacity demand factor of the central air conditioner are respectively analyzed in correlation with the load factor of the central air conditioner system:
wherein i=1, 2, 6,k =1, 2, n, LR (k) is a central air conditioning system load factor sequence, a parent sequence; y is i (k) The i standardized output sequence is a subsequence; f (LR (k), y i (k) The relevance of the ith subsequence and the corresponding dimension of the parent sequence is represented, the value range is between 0 and 1, 0 represents uncorrelation, 1 represents strong relevance, and the larger the number is, the stronger the representative relevance is; ρ is the resolution factor; p and q are respectively two-stage minimum difference and two-stage maximum difference:
in step S2, weight is allocated according to the correlation analysis result to obtain a historical data sequence of contribution value of each dimensionless new data to the load rate of the central air conditioning system, specifically, weight A is allocated according to the correlation analysis result 1 ,A 2 ,A 3 ,A 4 ,A 5 ,A 6 The normalized data sequence y 1 ,y 2 ,y 3 ,y 4 ,y 5 ,y 6 Multiplying the distributed weight values to obtain a contribution value historical data sequence for the load rate: y is cvi (k)＝y i ·A i Wherein i=1, 2,.. 6,k =1, 2,..n.
S3, establishing an elite retention strategy genetic algorithm-based EPGA improved counter propagation neural network model, namely an EPGA-BPNN model, taking a contribution value historical data sequence as input data, and training the EPGA-BPNN model by combining a central air conditioning system load rate in a historical period to obtain a trained EPGA-BPNN model.
In step S3, the back propagation neural network model (EPGA-BPNN model) improved based on elite retention policy genetic algorithm (EPGA) comprises a back propagation neural network model (BPNN model) and a parameter optimization module,
BPNN model: taking a contribution value historical data sequence from w-v moment to w moment in a historical period as input data, carrying out forward propagation, and obtaining a predicted load rate from w+1 moment to w+u moment; the error between the obtained predicted load rate and the corresponding running total power of the central air conditioning system in the historical period is reversely propagated;
parameter optimization module: and carrying out iterative optimization on the network parameters of the BPNN model by adopting an elite retention strategy genetic algorithm EPGA to obtain the optimized network parameters of the BPNN model.
In step S3, the contribution value historical data sequence is used as input data, and after the EPGA-BPNN model is trained by combining the central air conditioning system load rate of the historical period, the trained EPGA-BPNN model is obtained, specifically,
s31, inputting a contribution value historical data sequence from w-v time to w time in a historical period, inputting a BPNN model of the EPGA-BPNN model, and outputting a central air conditioning system load rate predicted value sequence from w+1 time to w+u time;
s32, calculating a Loss function Loss of a prediction error by the total running power of the central air conditioning system in the historical period and the load rate predicted value sequence of the central air conditioning system;
s33, initializing network parameters of the BPNN model, and taking the initialized network parameters as an initial parameter population;
s34, when a Loss function Loss of the prediction error is propagated reversely, iteratively updating the parameter population by using an elite retention strategy genetic algorithm EPGA, and repeating the steps S31-S34 until the Loss function is smaller than a set value or reaches set times, and solving to obtain an optimal network parameter value;
in step S34, during the back propagation of the Loss function Loss of the prediction error, the elite retention policy genetic algorithm EPGA is used to update the parameter population iteratively, specifically as shown in fig. 2:
s341, marking parameter population of elite retention strategy genetic algorithm EPGA asWherein x represents population individuals, and k represents population algebra;
s342, taking the Loss function Loss as an fitness function value of an individual in each iteration, and directly copying the individual with the best performance in the population, namely the individual with the highest fitness function value, to the next generation;
s343, old populationThe old population is +.>The other individuals of the population form a new population by the new individuals generated by genetic operator operation and the individuals with the best performance in the population together>For new->Re-calculating the fitness function value of the individual during the re-evolution; the individuals in the population that perform best are taken as the optimal network parameter values.
And S35, assigning an optimal network parameter value to the BPNN model to obtain the trained EPGA-BPNN model.
In step S3, the parameter optimization module adopts an elite retention policy genetic algorithm EPGA to solve the optimal network parameters for the BPNN model. Each individual in the EPGA algorithm population represents a set of network parameters of the BPNN model, including all weights and thresholds of the BPNN model, and when the elite retention policy genetic algorithm EPGA iterates, the mean square error function Loss of the BPNN output value and the true value is used as an fitness function for evaluating the individual performance.
In step S3, the population of elite retention strategy genetic algorithm EPGA is marked as(where x represents population individuals and k represents population algebra), EPGA employs +.>Searching optimal individuals in an iterative updating mode, and directly copying the individuals with the optimal performance in the population to the next generation in each iteration; old population->On the premise of keeping the whole scale unchanged, keeping +.>A group of network parameters with the best performance, and a new population is formed together with new individuals generated by other individuals through genetic operator operation>At->The fitness function value of the individual is recalculated when evolving again.
S4, acquiring monitoring data of a set period before the prediction period, and obtaining a contribution value data sequence of the set period before the prediction period.
In step S4, the same method as in step S2 is used to obtain the contribution value data sequence of the set period before the predicted period for the monitored data of the set period before the predicted period.
S5, inputting the contribution value data sequence of the set period before the prediction period into the trained EPGA-BPNN model to obtain the central air conditioning system load rate of the prediction period.
In step S5, the trained EPGA-BPNN model is input by using the contribution value data sequence from the T-n time to the T time, and the air conditioner load rate LR from the T+1 time to the T+m time in the prediction period is predicted fv (j) Where j=1, 2,..m.
In step S5, the central air conditioning system load rate=the central air conditioning system load amount/the sum of rated powers of the respective devices of the central air conditioning system. Since the sum of rated powers of the respective devices in the central air conditioning system is a constant value, the prediction of the load factor of the central air conditioning system is also called central air conditioning load prediction.
S6, judging the running state of the central air conditioning system in the prediction period according to the central air conditioning system load rate in the prediction period obtained in the step S5, and formulating a corresponding regulation strategy in the prediction period according to the running state.
In step S6, the operation state of the central air conditioning system of the prediction period includes a start-up phase, an operation phase, and a stop phase.
In step S6, the running state of the central air conditioning system in the predicted period is determined according to the central air conditioning system load rate in the predicted period obtained in step S5, specifically,
in the load factor of the predicted period, there are any one of the following three cases that are determined as the start-up phase:
case one: the load factor is continuously increased, and the load factor at the starting moment and the load factor at the ending moment are increased and changed at a first set interval such as [180, 570], wherein the interval unit is%/1 h;
and a second case: the load rate of the existing time period in the predicted time period reaches the second set interval such as [80,100], and the interval unit is;
and a third case: the load rate continuously drops, and the load rate at the starting moment and the load rate at the ending moment drop change rates are in a third set interval such as [20,34], and the interval unit is that;
when the load rate of the predicted period is maintained within a set range such as 50% to 80%, and the load rate change rate is within a fourth set interval such as [0,14], the interval unit is%/1 h, and the operation stage is determined;
the load factor at the start time and the load factor at the end time decrease in the rate of change are within a fifth set interval [300, 420], the interval unit is%/1 h, and the load factor decreases to 0, and the stop phase is determined.
According to the fixed-frequency central air conditioner daily load prediction method considering the air conditioner running state, the air conditioner running state is considered, the EPGA-BPNN model is adopted to predict the air conditioner load rate, the air conditioner running stage in the prediction period is judged according to the load rate, the accuracy of the central air conditioner daily load prediction can be improved, the accurate regulation and control of the central air conditioner can be realized, and the method is suitable for air conditioner load and running stage prediction.
Although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that the foregoing embodiments may be modified or equivalents substituted for some of the features thereof. All equivalent structures made by the content of the specification and the drawings of the invention are directly or indirectly applied to other related technical fields, and are also within the scope of the invention.
Claims (10)
1. A fixed frequency central air conditioner daily load prediction method considering the running state of an air conditioner is characterized in that: comprises the steps of,
s1, acquiring n groups of monitoring data in a historical period, wherein each group of monitoring data comprises factors influencing the cold energy requirement of a central air conditioner and the total running power of the central air conditioner system;
s2, carrying out normalization processing on the monitored data to obtain dimensionless new data, carrying out correlation analysis on the new data influencing the central air conditioning cold demand factors and the central air conditioning system load rate respectively, and distributing weights according to correlation analysis results to obtain a contribution value historical data sequence of each dimensionless new data to the central air conditioning system load rate;
s3, establishing an elite retention strategy genetic algorithm-based EPGA improved counter propagation neural network model, namely an EPGA-BPNN model, taking a contribution value historical data sequence as input data, and training the EPGA-BPNN model by combining a central air conditioning system load rate of a historical period to obtain a trained EPGA-BPNN model;
s4, acquiring monitoring data of a set period before a predicted period, and obtaining a contribution value data sequence of the set period before the predicted period;
s5, inputting the contribution value data sequence of the set period before the prediction period into the trained EPGA-BPNN model to obtain the central air conditioning system load rate of the prediction period;
s6, judging the running state of the central air conditioning system in the prediction period according to the central air conditioning system load rate in the prediction period obtained in the step S5, and formulating a corresponding regulation strategy in the prediction period according to the running state.
2. As claimed inThe fixed-frequency central air conditioner daily-load prediction method considering the running state of the air conditioner is characterized by comprising the following steps of: in step S1, n sets of monitoring data of the historical period are obtained, each set of monitoring data includes factors affecting the cooling capacity requirement of the central air conditioner and total running power of the central air conditioning system, specifically, n sets of factors affecting the cooling capacity requirement of the central air conditioner are collected, including indoor temperature T in Indoor humidity H in Outdoor temperature T out Outdoor humidity H out And chilled water temperature T cw The method comprises the steps of carrying out a first treatment on the surface of the And collecting the total running power P of the corresponding central air conditioning system air The method comprises the steps of carrying out a first treatment on the surface of the Obtaining n groups of monitoring data { T ] in ,H in ,T out ,H out ,T cw ，P air }。
3. The fixed frequency central air conditioner daily pre-load prediction method considering the running state of the air conditioner as claimed in claim 1, wherein: in step S2, the monitoring data is normalized to obtain dimensionless new data, specifically, normalized monitoring data:
wherein the sequence x 1 ,x 2 ,x 3 ,x 4 ,x 5 Respectively representing input variables of indoor temperature Tin, indoor humidity Hin, outdoor temperature Tout, outdoor humidity Hout and chilled water temperature Tcw, sequence x 6 Running a total power Pair data sequence for a central air conditioning system, i.e. { x } 1 ＝T in ,x 2 ＝H in ,x 3 ＝T out ,x 4 ＝H out ,x 5 ＝T cw ,x 6 ＝P air Post-conversion to yield the output y 1 ,y 2 ,y 3 ,y 4 ,y 5 ,y 6 ∈[0,1]The output is new data without dimension.
4. A fixed frequency central air conditioning daily load prediction method taking into account the running state of an air conditioner as claimed in any one of claims 1 to 3, wherein: in step S2, the new data affecting the cooling capacity demand factor of the central air conditioner are respectively analyzed in correlation with the load factor of the central air conditioner system:
wherein i=1, 2, 6,k =1, 2, n, LR (k) is a central air conditioning system load factor sequence, a parent sequence; y is i (k) The i standardized output sequence is a subsequence; f (LR (k), y i (k) The relevance of the ith subsequence and the corresponding dimension of the parent sequence is represented, the value range is between 0 and 1, 0 represents uncorrelation, 1 represents strong relevance, and the larger the number is, the stronger the representative relevance is; ρ is the resolution factor; p and q are respectively two-stage minimum difference and two-stage maximum difference:
5. a fixed frequency central air conditioning daily load prediction method taking into account the running state of an air conditioner as claimed in any one of claims 1 to 3, wherein: in step S2, weight is allocated according to the correlation analysis result to obtain a historical data sequence of contribution value of each dimensionless new data to the load rate of the central air conditioning system, specifically, weight A is allocated according to the correlation analysis result 1 ,A 2 ,A 3 ,A 4 ,A 5 ,A 6 The normalized data sequence y 1 ,y 2 ,y 3 ,y 4 ,y 5 ,y 6 Multiplying the distributed weight values to obtain a contribution value historical data sequence for the load rate: y is cvi (k)＝y i ·A i Wherein i=1, 2,.. 6,k =1, 2,..n.
6. A fixed frequency central air conditioning daily load prediction method taking into account the running state of an air conditioner as claimed in any one of claims 1 to 3, wherein: in step S3, the back propagation neural network model (EPGA-BPNN model) improved based on elite retention policy genetic algorithm (EPGA) comprises a back propagation neural network model (BPNN model) and a parameter optimization module,
BPNN model: taking a contribution value historical data sequence from w-v moment to w moment in a historical period as input data, carrying out forward propagation, and obtaining a predicted load rate from w+1 moment to w+u moment; the error between the obtained predicted load rate and the corresponding running total power of the central air conditioning system in the historical period is reversely propagated;
parameter optimization module: and carrying out iterative optimization on the network parameters of the BPNN model by adopting an elite retention strategy genetic algorithm EPGA to obtain the optimized network parameters of the BPNN model.
7. A fixed frequency central air conditioning daily load prediction method taking into account the running state of an air conditioner as claimed in any one of claims 1 to 3, wherein: in step S3, the contribution value historical data sequence is used as input data, and after the EPGA-BPNN model is trained by combining the central air conditioning system load rate of the historical period, the trained EPGA-BPNN model is obtained, specifically,
s31, inputting a contribution value historical data sequence from w-v time to w time in a historical period, inputting a BPNN model of the EPGA-BPNN model, and outputting a central air conditioning system load rate predicted value sequence from w+1 time to w+u time;
s32, calculating a Loss function Loss of a prediction error by the total running power of the central air conditioning system in the historical period and the load rate predicted value sequence of the central air conditioning system;
s33, initializing network parameters of the BPNN model, and taking the initialized network parameters as an initial parameter population;
s34, when a Loss function Loss of the prediction error is propagated reversely, iteratively updating the parameter population by using an elite retention strategy genetic algorithm EPGA, and repeating the steps S31-S34 until the Loss function is smaller than a set value or reaches set times, and solving to obtain an optimal network parameter value;
and S35, assigning an optimal network parameter value to the BPNN model to obtain the trained EPGA-BPNN model.
8. The method for predicting the daily load of the constant-frequency central air conditioner according to claim 7, wherein the method is characterized in that: in step S34, the parameter population is iteratively updated using the elite retention policy genetic algorithm EPGA, in particular,
s341, marking parameter population of elite retention strategy genetic algorithm EPGA asWherein x represents population individuals, and k represents population algebra;
s342, taking the Loss function Loss as an fitness function value of an individual in each iteration, and directly copying the individual with the best performance in the population, namely the individual with the highest fitness function value, to the next generation;
s343, old populationThe old population is +.>The other individuals of the population form a new population by the new individuals generated by genetic operator operation and the individuals with the best performance in the population together>For new->Re-calculating the fitness function value of the individual during the re-evolution; the individuals in the population that perform best are taken as the optimal network parameter values.
9. A fixed frequency central air conditioning daily load prediction method taking into account the running state of an air conditioner as claimed in any one of claims 1 to 3, wherein: in step S6, the operation state of the central air conditioning system of the prediction period includes a start-up phase, an operation phase, and a stop phase.
10. The fixed frequency central air conditioner daily pre-load prediction method considering the running state of the air conditioner as claimed in claim 9, wherein: in step S6, the running state of the central air conditioning system in the predicted period is determined according to the central air conditioning system load rate in the predicted period obtained in step S5, specifically,
in the load factor of the predicted period, there are any one of the following three cases that are determined as the start-up phase:
case one: the load rate continuously rises, and the load rate at the starting moment and the load rate at the ending moment rise and change rate are in a first set interval;
and a second case: the load rate of the existing time period in the predicted time period reaches the second set interval;
and a third case: the load rate continuously drops, and the load rate at the starting moment and the load rate at the ending moment drop change rate are in a third set interval;
judging the operation stage when the load rate of the prediction period is kept in a set range and the load rate change rate is in a fourth set interval;
the load factor at the start time and the load factor at the end time fall at a rate within a fifth set interval, and the load factor falls to 0, and the stop phase is determined.
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