CN110991761A - Heat supply load prediction method and device - Google Patents
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Abstract
The invention is suitable for the technical field of energy, and provides a heat supply load prediction method and a heat supply load prediction device, wherein the method comprises the following steps: acquiring similar moment data of a moment to be predicted according to the heat supply load data of the time sequence; according to the similar moment data, obtaining similar moment characteristics corresponding to the similar moment data; processing the similar moment data by adopting a long-term and short-term memory network to obtain the time sequence characteristics of the similar moment data; and processing the similar time characteristics and the time sequence characteristics by adopting a multilayer feedforward network to obtain the predicted heat supply load of the time to be predicted. The method can effectively make up the defect of inaccurate load prediction, solve the problem of large prediction deviation, and meanwhile, the accurate prediction result is beneficial to scheduling optimization of subsequent work; the method has simple overall operation, ensures the accuracy, convenience and rapidness of prediction, improves the overall processing speed and saves processing resources.
Description
Technical Field
The invention belongs to the technical field of energy, and particularly relates to a heat supply load prediction method and a heat supply load prediction device.
Background
The heating load is a measure of the ability of a heating company to provide heat for users, and is an important index for measuring the ability of the heating company to provide heat. At present, steam supply users are divided into industries, businesses, residents, offices and the like, steam loads, load magnitude and load characteristics of different users are different, and the accuracy of load prediction is about optimization scheduling and operation strategies. In reality, there are many methods for load prediction, such as exponential smoothing, difference-integrated Moving Average autoregressive model (autoregressive integrated Moving Average model), etc., but all have the problems of single load prediction method, poor accuracy, large prediction deviation, etc. In view of the above problem, a new method for solving the problem of poor load prediction accuracy is needed.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method and an apparatus for predicting a heating load, a terminal device, and a computer-readable storage medium, so as to solve the technical problem in the prior art that a heating load cannot be accurately predicted.
In a first aspect of the embodiments of the present invention, a method for predicting a heating load is provided, including:
acquiring similar moment data of a moment to be predicted according to the heat supply load data of the time sequence;
according to the similar moment data, obtaining similar moment characteristics corresponding to the similar moment data;
processing the similar moment data by adopting a long-term and short-term memory network to obtain the time sequence characteristics of the similar moment data;
and processing the similar time characteristics and the time sequence characteristics by adopting a multilayer feedforward network to obtain the predicted heat supply load of the time to be predicted.
In a second aspect of the embodiments of the present invention, there is provided a heating load prediction apparatus, including:
the similar moment data acquisition module is used for acquiring similar moment data of the moment to be predicted according to the heat supply load data of the time sequence;
a similar time characteristic obtaining module, configured to obtain, according to the similar time data, a similar time characteristic corresponding to the similar time data;
the time sequence characteristic acquisition module is used for processing the similar moment data by adopting a long-term and short-term memory network to acquire the time sequence characteristics of the similar moment data;
a predicted load obtaining module; and the system is used for processing the similar time characteristics and the time sequence characteristics by adopting a multilayer feedforward network to obtain the predicted heating load of the time to be predicted.
In a third aspect of the embodiments of the present invention, there is provided a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the heating load prediction method when executing the computer program.
In a fourth aspect of the embodiments of the present invention, there is provided a computer-readable storage medium storing a computer program which, when executed by a processor, implements the steps of the heating load prediction method.
The heat supply load prediction method provided by the embodiment of the invention has the beneficial effects that at least: the method can effectively make up the defect of inaccurate load prediction, solve the problem of large prediction deviation, and meanwhile, the accurate prediction result is beneficial to scheduling optimization of subsequent work; the method has simple overall operation, ensures the accuracy, convenience and rapidness of prediction, improves the overall processing speed, saves processing resources and ensures the scientificity of the scheme.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed for the embodiments or the prior art descriptions will be briefly described 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 without creative efforts.
Fig. 1 is a first schematic flow chart of a heat supply load prediction method according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a first implementation flow of obtaining similar time data of a time to be predicted in the heat supply load prediction method according to the embodiment of the present invention;
fig. 3 is a schematic diagram of a second implementation flow of obtaining similar time data of a time to be predicted in the heat supply load prediction method according to the embodiment of the present invention;
fig. 4 is a schematic flow chart illustrating an implementation process of determining that the initial similar time data is abnormal data in the heat supply load prediction method according to the embodiment of the present invention;
fig. 5 is a schematic flow chart of an implementation of a heating load prediction method according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of a process of implementing training of an initial long-short term memory network and an initial multi-layer feedforward network in a heating load prediction method according to an embodiment of the present invention;
FIG. 7 is a schematic flow chart of the implementation of the testing of the trained long-short term memory network and the multi-layer feedforward network in the method for predicting the heating load according to the embodiment of the present invention;
fig. 8 is a first schematic diagram of a heating load prediction device according to an embodiment of the present invention;
fig. 9 is a schematic diagram of a similar time data obtaining module in the heating load prediction apparatus according to the embodiment of the present invention;
fig. 10 is a schematic diagram of a similar time data acquisition unit in the heating load prediction apparatus according to the embodiment of the present invention;
fig. 11 is a second schematic diagram of a heating load prediction device according to an embodiment of the present invention;
FIG. 12 is a schematic diagram of a network training module in a heating load prediction device according to an embodiment of the present invention;
fig. 13 is a schematic diagram of a prediction result testing unit in the heating load prediction apparatus according to the embodiment of the present invention;
fig. 14 is a schematic diagram of a terminal device according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the invention, are within the scope of the invention. Unless otherwise specified, the technical means used in the examples are conventional means well known to those skilled in the art.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the present application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in the specification of the present application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to a determination" or "in response to a detection". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
In order to explain the technical means of the present invention, the following description will be given by way of specific examples.
Referring to fig. 1, a first implementation flow diagram of a heating load prediction method according to an embodiment of the present invention is shown, where the method may include:
step S10: and acquiring similar time data of the time to be predicted according to the heat supply load data of the time sequence.
Further, in order to acquire data at similar time, it is necessary to acquire heating load data of time series. Fig. 2 is a schematic diagram of a first implementation flow of obtaining data of similar time at a time to be predicted in the heat supply load prediction method according to the embodiment of the present invention, in this embodiment, heat supply load data of a time sequence is obtained; extracting initial similar moment data of a moment to be predicted from the heat supply load data of the time sequence according to a preset rule; and preprocessing the initial similar time data to correct the initial similar time data to acquire similar time data. One way to obtain similar time data may include the steps of:
step S101: and acquiring time-series heat supply load data.
For example, the time series data may be 24 data per day, i.e., one data per hour. It should be understood that the selection of specific time data is made as desired and not limited herein.
Step S102: and extracting initial similar moment data of the moment to be predicted from the heat supply load data of the time sequence according to a preset rule.
According to the time to be predicted, at least one of the following heating load data is obtained from the heating load data of the time series: the system comprises a plurality of pieces of heat supply load data, a plurality of pieces of heat supply load data and a plurality of pieces of heat supply load data, wherein the heat supply load data of the same time every day on preset days before the time to be predicted, the heat supply load data of the same time every week on preset weeks before the time to be predicted, the heat supply load data of the same time every month on preset months before the time to be predicted, and the heat supply load data of the same time every year on preset years before the time to be predicted.
For example: the strategy for extracting the heat supply load data from the data at a certain time t is as follows: the data of the same time corresponding to each day of the previous 3 days, the data of the time corresponding to the previous 3 weeks, the data of the time corresponding to the previous 3 months and the data of the current year.
It should be understood that the selection of specific time data is performed as needed, and may be any time, such as the first 5 days, the first 7 months, etc., without limitation.
Step S103: and preprocessing the initial similar time data to correct the initial similar time data to acquire similar time data.
Further, in order to obtain the similar time data, the original similar time data needs to be preprocessed and corrected. Please refer to fig. 3, which is a schematic diagram of a second implementation flow of obtaining similar time data of a time to be predicted in the heat supply load prediction method according to the embodiment of the present invention, in the embodiment, a heat supply load average value and a heat supply load value variance of the initial similar time data are obtained; determining abnormal data in the initial similar moment data according to the average heat supply load value and the variance of the heat supply load value; and correcting the abnormal data to obtain corrected similar time data. One way to obtain similar time data may include the steps of:
step S1031: and acquiring the average value and the variance of the heat supply load values of the initial similar moment data.
The method for acquiring the average value and the variance of the heat supply load value comprises the following steps:
wherein x isn,iRepresenting the heating load value at the ith moment of the nth day,
the average value of the heating load is characterized,
characterizing the variance of the heat supply load value.
Step S1032: and determining abnormal data in the initial similar moment data according to the average value of the heat supply load and the variance of the heat supply load.
Further, in order to determine the abnormal data in the initial similar time data, it is necessary to determine whether a difference between the heat supply load value of the initial similar time data and the heat supply load average value is greater than a preset multiple of heat supply load standard deviation. Please refer to fig. 4, which is a schematic diagram illustrating an implementation process of determining that the initial similar time data is abnormal data in the heat supply load prediction method according to the embodiment of the present invention, in this embodiment, it is determined whether a difference between a heat supply load value of the initial similar time data and the heat supply load average value is greater than a heat supply load standard deviation of a preset multiple; and if the difference value between the heat supply load value of the initial similar moment data and the heat supply load average value is larger than the heat supply load standard deviation of a preset multiple, determining that the initial similar moment data are abnormal data. One way to determine anomalous data in the initial similar moment data may include the steps of:
step S10321: and judging whether the difference value between the heat supply load value of the initial similar moment data and the heat supply load average value is larger than the heat supply load standard deviation of a preset multiple.
The 3 sigma criterion is also called Layida criterion, which is that a group of detection data is assumed to only contain random errors, the detection data is calculated to obtain standard deviation, an interval is determined according to a certain probability, and if the error exceeding the interval is considered to be not the random error but a coarse error, the data containing the error is rejected. And 3 sigma is suitable when there are more groups of data.
The 3 sigma principle is as follows: the probability of the numerical distribution in (μ - σ, μ + σ) is 0.6827; the probability of the numerical distribution in (μ -3 σ, μ +3 σ) is 0.9545; the probability of the numerical distribution in (μ -3 σ, μ +3 σ) is 0.9973; it can be considered that the values are almost entirely concentrated in the interval of (mu-3 sigma, mu +3 sigma) vi > 3 sigma, and the possibility of exceeding this range is only less than 0.3%.
The 3 sigma criterion is based on equal-precision repeated measurement of normal distribution, so that the interference or noise of singular data is difficult to meet the normal distribution. If the absolute value nui of the residual error of a certain measured value in a group of measured data is larger than 3 sigma, the measured value is a bad value and should be removed.
The 3 sigma criterion exists because the probability of falling outside 3 sigma is only 0.27% for a normally distributed random error, which is usually taken as the limit error, and it is less likely to occur in a limited number of measurements. The 3 sigma criterion is the most common and simplest gross error criterion and is generally applied when the number of measurements is sufficiently large (n ≧ 30) or when n > 10 is used for coarse discrimination.
A normal distribution has two parameters, mu and sigma2The first parameter mu is the mean of the random variables following a normal distribution, and the second parameter sigma is the distribution of the continuous random variables2Is the variance of this random variable, so a normal distribution is denoted as N (μ, σ)2)。
μ is a position parameter of the normal distribution, describing the central tendency position of the normal distribution. The probability law is that the probability of taking values adjacent to μ is high, while the probability of taking values further away from μ is lower. The normal distribution is completely symmetrical about the axis of symmetry X ═ μ. The expectation, mean, median, and mode of normal distribution are the same and equal to μ.
The sigma describes the degree of dispersion of data distribution of normally distributed data, the larger the sigma is, the more dispersed the data distribution is, and the smaller the sigma is, the more concentrated the data distribution is. Also known as a normally distributed shape parameter, the larger σ, the flatter the curve, whereas the smaller σ, the thinner the curve.
Step S10322: and if the difference value between the heat supply load value of the initial similar moment data and the heat supply load average value is larger than the heat supply load standard deviation of a preset multiple, determining that the initial similar moment data are abnormal data.
Step S1033: and correcting the abnormal data to obtain corrected similar time data.
The correction method comprises the following steps:
wherein the content of the first and second substances,
representing the heating load correction value at the ith moment of the nth day,
representing the heat supply load values of m similar units at the ith moment before and after the heat supply load value,
and representing the heat supply load values of p similar unit time ith moments before and after.
When m is 1 and p is 2, the formula is:
wherein the content of the first and second substances,
characterization of the heating load correction value at the i-th moment of the nth day, xn±1,iRepresenting the heat supply load value at the ith moment of 2 similar days before and after the heat supply load value,
and (5) representing the heating load values at the ith moment of 2 similar days before and after.
Day of the same kind: for example, a certain time t currently being the second friday (day) of the month, and two days (days) of the respective times of the first friday (day) and the third friday (day) adjacent to the month; similar day: such as the two days (days) at a time t currently being the second friday of the month, the corresponding times of the second thursday (day) of the month and the saturday (day).
It should be understood that the same kind of unit time and the similar unit time may be any time herein, and a plurality of unit times may be acquired at the same time, which is not limited herein.
Referring to fig. 1, further, after obtaining the similar time data of the time to be predicted, the following steps may be performed:
step S30: and acquiring similar time characteristics corresponding to the similar time data according to the similar time data.
For example: the strategy for extracting data at a certain time t and similar time data is as follows: the data of the same time corresponding to each day of the previous 3 days, the data of the time corresponding to the previous 3 weeks, the data of the time corresponding to the previous 3 months and the data of the time corresponding to the previous year are obtained, so that the similar time characteristics of the data of the similar time are obtained.
Referring to fig. 1, further, after obtaining the similar time feature corresponding to the similar time data, the following steps may be performed:
step S50: and processing the similar moment data by adopting a long-term and short-term memory network to obtain the time sequence characteristics of the similar moment data.
Step S70: and processing the similar time characteristics and the time sequence characteristics by adopting a multilayer feedforward network to obtain the predicted heat supply load of the time to be predicted.
Please refer to fig. 5, which is a schematic flow chart illustrating an implementation process of the method for predicting a heating load according to the embodiment of the present invention. The method comprises the following steps of processing the similar time data by adopting a long-term and short-term memory network, and acquiring the time sequence characteristics of the similar time data, wherein the method also comprises the following steps:
step S40: and training the initial long-short term memory network and the initial multi-layer feedforward network to obtain the long-short term memory network and the multi-layer feedforward network meeting the preset requirements for heat supply load prediction.
Further, in order to obtain a long-short term memory network and a multilayer feedforward network meeting preset requirements for heat supply load prediction, the initial long-short term memory network and the initial multilayer feedforward network need to be trained. Please refer to fig. 6, which is a schematic diagram of an implementation process of training an initial long-short term memory network and an initial multi-layer feedforward network in a heat supply load prediction method according to an embodiment of the present invention, in this embodiment, the similar time data is segmented to obtain training data and test data; training the initial long-short term memory network by adopting the training data to obtain the trained long-short term memory network and the time sequence characteristics of the training data; training the initial multi-layer feedforward network by using the time sequence characteristics and the similar time characteristics of the training data to obtain a trained multi-layer feedforward network; and testing the trained long-short term memory network and the multi-layer feedforward network by adopting the test data and the similar time characteristics of the test data so as to determine the long-short term memory network and the multi-layer feedforward network meeting the preset requirement for predicting the heat supply load. One way to obtain a long-short term memory network and a multi-layer feed-forward network that meet preset requirements for heating load prediction may include the steps of:
step S401: and segmenting the data at the similar moment to acquire training data and test data.
For example, the segmentation ratio of the training data and the test data may be 2: 1. It should be understood that the division ratio may be other ratios, and may be determined according to needs or other situations, and is not limited herein.
Step S402: and training the initial long-short term memory network by adopting the training data to obtain the trained long-short term memory network and the time sequence characteristics of the training data.
Training the initial long-short term memory network by adopting the training data to obtain a trained long-short term memory network; and extracting similar moment data according to the trained long and short term memory network to obtain the time sequence characteristics of the training data.
Step S403: and training the initial multi-layer feedforward network by using the time sequence characteristics and the similar time characteristics of the training data to obtain the trained multi-layer feedforward network.
And acquiring a combined characteristic according to the time sequence characteristic and the similar moment characteristic of the training data, and training the initial multilayer feedforward network by using the combined characteristic to acquire the trained multilayer feedforward network.
Step S404: and testing the trained long-short term memory network and the multi-layer feedforward network by adopting the test data and the similar time characteristics of the test data so as to determine the long-short term memory network and the multi-layer feedforward network meeting the preset requirement for predicting the heat supply load.
Further, in order to determine that the long-short term memory network and the multi-layer feedforward network meeting the preset requirements are used for heat supply load prediction, the trained long-short term memory network and the multi-layer feedforward network need to be tested. Please refer to fig. 7, which is a schematic diagram illustrating an implementation process of testing a trained long-short term memory network and a multi-layer feedforward network in a heat supply load prediction method according to an embodiment of the present invention, in this embodiment, the test data is input into the trained long-short term memory network to obtain a timing characteristic of the test data; inputting the time sequence characteristics and the similar time characteristics of the test data into the trained multilayer feedforward network to obtain a test prediction result; obtaining a test index according to the test prediction result and the test data; judging whether the test index meets a preset requirement or not; if the test index meets the preset requirement, determining the trained long-short term memory network and the multi-layer feedforward network as the long-short term memory network and the multi-layer feedforward network for predicting the heat supply load respectively; and if the test index does not meet the preset requirement, returning to the step of training the initial long-short term memory network by adopting the training data. One way to determine that long-short term memory networks and multi-layer feed-forward networks meeting preset requirements are used for heating load prediction may include the steps of:
step S4041: and inputting the test data into a trained long-term and short-term memory network to acquire the time sequence characteristics of the test data.
Step S4042: and inputting the time sequence characteristics and the similar time characteristics of the test data into the trained multilayer feedforward network to obtain a test prediction result.
Step S4043: and obtaining a test index according to the test prediction result and the test data.
The test indexes are as follows:
wherein, yiCharacterizing a test value in the time series data,
characterizing a predicted value of the time series data.
Step S4044: and judging whether the test index meets a preset requirement.
Step S4045: and if the test index meets the preset requirement, determining that the trained long-short term memory network and the trained multilayer feedforward network are respectively the long-short term memory network and the multilayer feedforward network for predicting the heat supply load.
Step S4046: and if the test index does not meet the preset requirement, returning to the step of training the initial long-short term memory network by adopting the training data.
It should be understood that the above-mentioned letters and/or symbols are only used for the purpose of clearly explaining the meaning of specific parameters of the device or steps, and other letters or symbols can be used for representation. And are not limited herein.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
The heat supply load prediction method provided by the embodiment of the invention has the beneficial effects that at least: acquiring similar moment data of a moment to be predicted according to the heat supply load data of the time sequence; according to the similar moment data, obtaining similar moment characteristics corresponding to the similar moment data; processing the similar moment data by adopting a long-term and short-term memory network to obtain the time sequence characteristics of the similar moment data; and processing the similar time characteristics and the time sequence characteristics by adopting a multilayer feedforward network to obtain the predicted heat supply load of the time to be predicted. The method can effectively make up the defect of inaccurate load prediction, solve the problem of large prediction deviation, and meanwhile, the accurate prediction result is beneficial to scheduling optimization of subsequent work; the method has simple overall operation, ensures the accuracy, convenience and rapidness of prediction, improves the overall processing speed, saves processing resources and ensures the scientificity of the scheme.
An object of an embodiment of the present invention is to provide a heating load prediction apparatus, and fig. 8 is a schematic diagram of the heating load prediction apparatus provided in the embodiment of the present invention, and for convenience of description, only a part related to the embodiment of the present application is shown.
Referring to fig. 8, a heating load prediction apparatus includes a similar time data obtaining module 81, a similar time characteristic obtaining module 83, a time sequence characteristic obtaining module 85, and a predicted load obtaining module 87. The similar time data obtaining module 81 is configured to obtain similar time data of a time to be predicted according to the heat supply load data of the time sequence; the time characteristic obtaining module 83 is configured to obtain, according to the similar time data, a similar time characteristic corresponding to the similar time data; the time sequence characteristic acquisition module 85 is configured to process the similar time data by using a long-term and short-term memory network, and acquire a time sequence characteristic of the similar time data; the predicted load obtaining module 84 is configured to process the similar time characteristic and the time sequence characteristic by using a multilayer feedforward network, and obtain a predicted heating load at the time to be predicted.
Referring to fig. 9, further, the similar time data acquiring module 81 includes a heating load data acquiring unit 811, an initial similar time data acquiring unit 812, and a similar time data acquiring unit 813. The heating load data acquiring unit 811 is configured to acquire heating load data of a time series; the initial similar time data obtaining unit 812 is configured to extract initial similar time data of a time to be predicted from the heat supply load data of the time series according to a preset rule; the similar time data acquiring unit 813 is configured to perform preprocessing on the initial similar time data to correct the initial similar time data to acquire similar time data.
Referring to fig. 10, further, the similar time data acquisition unit 813 includes a mean value and variance determination sub-unit 8131, an abnormal data determination sub-unit 8132, and a correction determination sub-unit 8133. The average value and variance determining subunit 8131 is configured to obtain a heat supply load average value and a heat supply load value variance of the initial similar time data; the abnormal data determination subunit 8132 is configured to determine abnormal data in the initial similar time data according to the average value of the heat supply load and the variance of the heat supply load; the correction stator unit 8133 is configured to correct the abnormal data to obtain corrected similar time data.
Further, please refer to fig. 11, which is a schematic diagram of a heating load prediction apparatus according to an embodiment of the present invention. The heating load prediction device comprises a network training module 84, which is used for training the initial long-short term memory network and the initial multi-layer feedforward network to obtain the long-short term memory network and the multi-layer feedforward network which meet the preset requirements for heating load prediction.
Referring to fig. 12, the network training module 84 includes a training data and test data obtaining unit 841, a first obtaining unit 842, a second obtaining unit 843, and a test unit 844. The training data and test data obtaining unit 841 is configured to segment the similar time data to obtain training data and test data; the first obtaining unit 842 is configured to train the initial long-short term memory network with the training data to obtain the trained long-short term memory network and the timing characteristics of the training data; the second obtaining unit 843 is configured to train the initial multilayer feedforward network by using the timing characteristic and the similar time characteristic of the training data to obtain a trained multilayer feedforward network; the testing unit 844 is configured to test the trained long-short term memory network and the multi-layer feedforward network using the test data and the similar time characteristics of the test data to determine that the long-short term memory network and the multi-layer feedforward network meet preset requirements for heat supply load prediction.
Referring to fig. 13, the test unit 844 includes a first obtaining subunit 8441, a test prediction result obtaining subunit 8442, a test index obtaining subunit 8443, a judging subunit 8444, a determining subunit 8445, and a returning subunit 8446. The first obtaining subunit 8441 is configured to input the test data into the trained long-term and short-term memory network to obtain a timing characteristic of the test data; the test prediction result obtaining subunit 8442 is configured to input the time sequence characteristics and the similar time characteristics of the test data into the trained multilayer feedforward network, so as to obtain a test prediction result; the test index obtaining subunit 8443 is configured to obtain a test index according to the test prediction result and the test data; the judgment subunit 8444 is configured to judge whether the test index meets a preset requirement; the determining subunit 8445 is configured to determine that the trained long-short term memory network and the trained multilayer feedforward network are the long-short term memory network and the multilayer feedforward network used for the heat supply load prediction, respectively, if the test index meets a preset requirement; the returning subunit 8446 is configured to return to the step of training the initial long-term and short-term memory network by using the training data if the test index does not meet the preset requirement.
Fig. 14 is a schematic diagram of a terminal device according to an embodiment of the present invention. As shown in fig. 14, the terminal device 9 includes a memory 91, a processor 90, and a computer program 92 stored in the memory 91 and executable on the processor 90, and the processor 90 implements the steps of the heating load prediction method when executing the computer program 92. Such as steps S10-S70 shown in fig. 1-7.
The terminal device 9 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The terminal device may include, but is not limited to, the processor 90 and the memory 91. Those skilled in the art will appreciate that fig. 14 is only an example of the terminal device 9, and does not constitute a limitation to the terminal device 9, and may include more or less components than those shown, or combine some components, or different components, for example, the terminal device may also include an input-output device, a network access device, a bus, etc.
The Processor 90 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 91 may be an internal storage unit of the terminal device 9, such as a hard disk or a memory of the terminal device 9. The memory 91 may also be an external storage device of the terminal device 9, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) and the like provided on the terminal device 9. Further, the memory 91 may also include both an internal storage unit and an external storage device of the terminal device 9. The memory 91 is used for storing the computer program and other programs and data required by the terminal device. The memory 91 may also be used to temporarily store data that has been output or is to be output.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method of the embodiments described above can be realized by a computer program, which can be stored in a computer-readable storage medium and can realize the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, etc. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
Specifically, the present application further provides a computer-readable storage medium, which may be a computer-readable storage medium contained in the memory in the foregoing embodiments; or it may be a separate computer-readable storage medium not incorporated into the terminal device. The computer readable storage medium stores one or more computer programs:
computer-readable storage medium, comprising a computer program stored thereon, which, when being executed by a processor, carries out the steps of the heating load prediction method.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. A heating load prediction method, comprising:
acquiring similar moment data of a moment to be predicted according to the heat supply load data of the time sequence;
according to the similar moment data, obtaining similar moment characteristics corresponding to the similar moment data;
processing the similar moment data by adopting a long-term and short-term memory network to obtain the time sequence characteristics of the similar moment data;
and processing the similar time characteristics and the time sequence characteristics by adopting a multilayer feedforward network to obtain the predicted heat supply load of the time to be predicted.
2. A heating load prediction method according to claim 1, wherein the obtaining of similar time data of the time to be predicted from the heating load data of the time series includes:
acquiring time-series heat supply load data;
extracting initial similar moment data of a moment to be predicted from the heat supply load data of the time sequence according to a preset rule;
and preprocessing the initial similar time data to correct the initial similar time data to acquire similar time data.
3. A heating load prediction method according to claim 2, wherein in the step of extracting initial similar time data of the time to be predicted from the heating load data of the time series according to a preset rule, at least one of the following heating load data is obtained from the heating load data of the time series according to the time to be predicted: the system comprises a plurality of pieces of heat supply load data, a plurality of pieces of heat supply load data and a plurality of pieces of heat supply load data, wherein the heat supply load data of the same time every day on preset days before the time to be predicted, the heat supply load data of the same time every week on preset weeks before the time to be predicted, the heat supply load data of the same time every month on preset months before the time to be predicted, and the heat supply load data of the same time every year on preset years before the time to be predicted.
4. A heating load prediction method according to claim 2, wherein the preprocessing the initial similar time data to modify the initial similar time data to obtain similar time data includes:
acquiring the average value and variance of the heat supply load values of the initial similar moment data, wherein the acquiring modes of the average value and variance of the heat supply load values are as follows:
wherein x isn,iCharacterizing the heating load value at the ith time of the nth day,
the average value of the heating load is characterized,
characterizing the heat supply load value variance;
determining abnormal data in the initial similar moment data according to the heat supply load average value and the heat supply load value variance;
correcting the abnormal data to obtain corrected similar time data, wherein the correction mode is as follows:
wherein the content of the first and second substances,
representing the heating load correction value at the ith moment of the nth day,
representing the heat supply load values of m similar units at the ith moment before and after the heat supply load value,
and representing the heat supply load values of p similar unit time ith moments before and after.
5. The heating load prediction method according to claim 4, wherein the determining abnormal data in the initial similar time data according to the heating load average value and the heating load variance comprises:
judging whether the difference value between the heat supply load value of the initial similar moment data and the heat supply load average value is larger than the heat supply load standard deviation of a preset multiple or not;
and if the difference value between the heat supply load value of the initial similar moment data and the heat supply load average value is larger than the heat supply load standard deviation of a preset multiple, determining that the initial similar moment data are abnormal data.
6. A heating load prediction method as claimed in claim 1, wherein before the step of processing the similar time data by using a long-short term memory network to obtain the time sequence characteristics of the similar time data, the method further comprises:
training the initial long-short term memory network and the initial multi-layer feedforward network to obtain the long-short term memory network and the multi-layer feedforward network meeting the preset requirements for heat supply load prediction, wherein the training comprises the following steps:
segmenting the similar moment data to obtain training data and test data;
training the initial long-short term memory network by adopting the training data to obtain the trained long-short term memory network and the time sequence characteristics of the training data;
training the initial multi-layer feedforward network by using the time sequence characteristics and the similar time characteristics of the training data to obtain a trained multi-layer feedforward network;
and testing the trained long-short term memory network and the multi-layer feedforward network by adopting the test data and the similar time characteristics of the test data so as to determine the long-short term memory network and the multi-layer feedforward network meeting the preset requirement for predicting the heat supply load.
7. A heating load prediction method according to claim 6, wherein the testing the trained long-short term memory network and multi-layer feedforward network using the test data and similar time characteristics of the test data to determine the long-short term memory network and multi-layer feedforward network meeting preset requirements for heating load prediction comprises:
inputting the test data into a trained long-term and short-term memory network to obtain the time sequence characteristics of the test data;
inputting the time sequence characteristics and the similar time characteristics of the test data into the trained multilayer feedforward network to obtain a test prediction result;
obtaining a test index according to the test prediction result and the test data, wherein the test index is as follows:
wherein, yiCharacterizing a test value in the time series data,
characterizing a predicted value of the time series data;
judging whether the test index meets a preset requirement or not;
if the test index meets the preset requirement, determining the trained long-short term memory network and the multi-layer feedforward network as the long-short term memory network and the multi-layer feedforward network for predicting the heat supply load respectively;
and if the test index does not meet the preset requirement, returning to the step of training the initial long-short term memory network by adopting the training data.
8. A heating load prediction apparatus, characterized by comprising:
the similar moment data acquisition module is used for acquiring similar moment data of the moment to be predicted according to the heat supply load data of the time sequence;
a similar time characteristic obtaining module, configured to obtain, according to the similar time data, a similar time characteristic corresponding to the similar time data;
the time sequence characteristic acquisition module is used for processing the similar moment data by adopting a long-term and short-term memory network to acquire the time sequence characteristics of the similar moment data;
a predicted load obtaining module; and the system is used for processing the similar time characteristics and the time sequence characteristics by adopting a multilayer feedforward network to obtain the predicted heating load of the time to be predicted.
9. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111415050A (en) * | 2020-04-27 | 2020-07-14 | 新智数字科技有限公司 | Short-term load prediction method and short-term load prediction model training method and device |
| CN112747477A (en) * | 2021-01-25 | 2021-05-04 | 华南理工大学 | Intelligent control system and method for safe and energy-saving gas-fired hot water boiler |
| CN113706337A (en) * | 2021-09-06 | 2021-11-26 | 天津宏达瑞信科技有限公司 | Heat supply load prediction method based on similar time periods |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170179716A1 (en) * | 2015-12-16 | 2017-06-22 | Johnson Controls Technology Company | Central plant control system with building energy load estimation |
| CN107239859A (en) * | 2017-06-05 | 2017-10-10 | 国网山东省电力公司电力科学研究院 | The heating load forecasting method of Recognition with Recurrent Neural Network is remembered based on series connection shot and long term |
| CN109934392A (en) * | 2019-02-28 | 2019-06-25 | 武汉大学 | A kind of micro-capacitance sensor short-term load forecasting method based on deep learning |
| CN110263866A (en) * | 2019-06-24 | 2019-09-20 | 苏州智睿新能信息科技有限公司 | A kind of power consumer load setting prediction technique based on deep learning |
-
2019
- 2019-12-13 CN CN201911285593.XA patent/CN110991761B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170179716A1 (en) * | 2015-12-16 | 2017-06-22 | Johnson Controls Technology Company | Central plant control system with building energy load estimation |
| CN107239859A (en) * | 2017-06-05 | 2017-10-10 | 国网山东省电力公司电力科学研究院 | The heating load forecasting method of Recognition with Recurrent Neural Network is remembered based on series connection shot and long term |
| CN109934392A (en) * | 2019-02-28 | 2019-06-25 | 武汉大学 | A kind of micro-capacitance sensor short-term load forecasting method based on deep learning |
| CN110263866A (en) * | 2019-06-24 | 2019-09-20 | 苏州智睿新能信息科技有限公司 | A kind of power consumer load setting prediction technique based on deep learning |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111415050A (en) * | 2020-04-27 | 2020-07-14 | 新智数字科技有限公司 | Short-term load prediction method and short-term load prediction model training method and device |
| CN111415050B (en) * | 2020-04-27 | 2023-12-05 | 新奥新智科技有限公司 | Short-term load prediction method, short-term load prediction model training method and device |
| CN112747477A (en) * | 2021-01-25 | 2021-05-04 | 华南理工大学 | Intelligent control system and method for safe and energy-saving gas-fired hot water boiler |
| CN113706337A (en) * | 2021-09-06 | 2021-11-26 | 天津宏达瑞信科技有限公司 | Heat supply load prediction method based on similar time periods |
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