CN109829575B - Energy data prediction processing method and device, readable medium and electronic equipment - Google Patents
Energy data prediction processing method and device, readable medium and electronic equipment Download PDFInfo
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Abstract
The invention discloses a processing method, a device, a readable medium and electronic equipment for energy data prediction, wherein the method comprises the following steps: constructing a training data set, wherein the training data set comprises a plurality of operation data; receiving current energy supply data respectively corresponding to at least two types of input energy input from the outside; training by utilizing each operation data in the training data set to obtain a Gaussian process regression model corresponding to each output energy respectively; and substituting the current energy supply data into the Gaussian process regression model corresponding to the output energy according to each output energy, and calculating the current capacity data corresponding to the output energy. Through the technical scheme of the invention, the capacity data of the energy concentrator can be predicted more accurately.
Description
Technical Field
The invention relates to the field of energy, in particular to a processing method and device for energy data prediction, a readable medium and electronic equipment.
Background
In the field of integrated energy, an energy hub may represent a multi-energy conversion device to which, after a plurality of energy sources (input energy sources) are input, the input plurality of input energy sources may be converted to output a plurality of energy sources (output energy sources). When the energy concentrator receives various input energy sources respectively corresponding to certain energy supply data, the productivity data respectively corresponding to various output energy sources correspondingly output by the energy concentrator are predicted.
At present, in order to predict the capacity data of the energy hub, it is generally required to determine an energy efficiency transformation matrix corresponding to the energy hub, where the energy efficiency transformation matrix is a constant generally determined according to a rated energy transformation rate or an empirical value of the energy hub.
However, in the actual operation process of the energy hub, the actual energy conversion rate is usually affected by the factors such as the operation time, the load, the ambient temperature, and the like of the energy hub, and the energy conversion matrix is defined, and the operation data of the energy hub in the actual operation process is not considered, so that obviously, the capacity data of the energy hub cannot be accurately predicted.
Disclosure of Invention
The invention provides a processing method and device for energy data prediction, a readable medium and electronic equipment, which can more accurately predict the capacity data of an energy concentrator.
In a first aspect, the present invention provides a method for processing energy data prediction, including:
constructing a training data set, wherein the training data set comprises a plurality of operation data;
receiving current energy supply data respectively corresponding to at least two types of input energy input from the outside;
training by utilizing each operating data in the training data set to obtain a Gaussian process regression model corresponding to each output energy respectively;
and aiming at each output energy, substituting each current energy supply data into the Gaussian process regression model corresponding to the output energy, and calculating the current capacity data corresponding to the output energy.
Preferably, the first and second electrodes are formed of a metal,
the operational data includes: the energy concentrator comprises energy supply data which correspond to the at least two input energy sources input to the energy concentrator within a set time interval, and capacity data which correspond to each output energy source and are respectively converted and output by the energy concentrator within the set time interval.
Preferably, the first and second electrodes are formed of a metal,
the constructing of the training data set comprises:
periodically collecting the operating data of the energy concentrator within a set time interval, and adding the collected operating data into a training data set;
and setting a corresponding time stamp for the collected operation data.
Preferably, the first and second electrodes are formed of a metal,
the method comprises the following steps: detecting whether the total amount of each operating data in the training data set is larger than a preset threshold value, if so, deleting one operating data in the training data set according to the timestamp corresponding to each operating data in the training data set.
In a second aspect, the present invention provides a processing apparatus for energy data prediction, including:
the training data management module is used for constructing a training data set, and the training data set comprises a plurality of operation data;
the data receiving module is used for receiving current energy supply data respectively corresponding to at least two types of input energy input from the outside;
the model training module is used for training by utilizing the operation data in the training data set to obtain a Gaussian process regression model corresponding to each output energy source;
and the capacity prediction module is used for substituting each current energy supply data into the Gaussian process regression model corresponding to the output energy according to each output energy, and calculating the current capacity data corresponding to the output energy.
Preferably, the first and second electrodes are formed of a metal,
the operational data includes: the energy supply data respectively corresponding to the at least two input energy sources input to the energy hub within a set time interval, and the energy hub converts and outputs the at least two input energy sources input within the set time interval to the energy capacity data respectively corresponding to the at least one output energy source.
Preferably, the first and second electrodes are formed of a metal,
the training data management module is used for periodically acquiring the operating data of the energy concentrator within a set time interval and adding the acquired operating data into a training data set.
Preferably, the first and second electrodes are formed of a metal,
the training data management module is further used for setting a corresponding timestamp for the collected operation data; detecting whether the total amount of each operating data in the training data set is larger than a preset threshold value, if so, deleting one operating data in the training data set according to the timestamp corresponding to each operating data in the training data set.
In a third aspect, the invention provides a readable medium comprising executable instructions, which when executed by a processor of an electronic device, perform the method according to any of the first aspect.
In a fourth aspect, the present invention provides an electronic device, comprising a processor and a memory storing execution instructions, wherein when the processor executes the execution instructions stored in the memory, the processor performs the method according to any one of the first aspect.
The invention provides a processing method, a device, a readable medium and electronic equipment for energy quantity prediction, wherein the method constructs a training data set formed by a plurality of operation data of an energy concentrator, when the capacity data of the energy concentrator needs to be predicted, for example, when current energy supply data respectively corresponding to at least two types of input energy input from the outside are received, a Gaussian process regression model respectively corresponding to each type of output energy of the energy concentrator can be obtained by training by using each operation data in the training set, and each current energy supply data is substituted into the Gaussian process regression model respectively corresponding to each type of output energy, so as to calculate the current capacity data respectively corresponding to each type of output energy of the energy concentrator. According to the technical scheme provided by the invention, a plurality of running data of the energy concentrator during actual running can be used as the basis for predicting the capacity data of the energy concentrator, and the capacity data of the energy concentrator can be more accurately predicted.
Drawings
In order to more clearly illustrate the embodiments or the prior art solutions of the present invention, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments described in the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive labor.
Fig. 1 is a schematic flow chart illustrating a processing method for energy data prediction according to an embodiment of the present invention;
fig. 2 is a schematic flow chart illustrating another energy data prediction processing method according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a processing apparatus for energy data prediction according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail and completely with reference to the following embodiments and accompanying drawings. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, an embodiment of the present invention provides a processing method for energy data prediction, including the following steps:
102, receiving current energy supply data corresponding to at least two types of input energy sources input from the outside;
103, training by using each operating data in the training data set to obtain a Gaussian process regression model corresponding to each output energy respectively;
and 104, substituting the current energy supply data into the Gaussian process regression model corresponding to the output energy for each output energy, and calculating the current capacity data corresponding to the output energy.
As shown in fig. 1, in the method, a training data set composed of a plurality of operation data of the energy hub is constructed, when the capacity data of the energy hub needs to be predicted, for example, when current energy supply data corresponding to at least two types of input energy respectively input from the outside is received, a gaussian process regression model corresponding to each type of output energy of the energy hub can be obtained by training using each operation data in the training set, and then each current energy supply data is substituted into the gaussian process regression model corresponding to each type of output energy respectively, so as to calculate the current capacity data corresponding to each type of output energy of the energy hub respectively. According to the technical scheme provided by the invention, a plurality of running data of the energy concentrator during actual running can be used as the basis for predicting the capacity data of the energy concentrator, and the capacity data of the energy concentrator can be more accurately predicted.
Specifically, the operation data includes: the energy concentrator comprises energy supply data which correspond to the at least two input energy sources input to the energy concentrator within a set time interval, and capacity data which correspond to each output energy source and are respectively converted and output by the energy concentrator within the set time interval.
Obviously, the energy supply data corresponding to the input energy specifically refers to the total energy supply amount of the corresponding energy (i.e. the input energy, such as gas) input to the energy hub within a set time interval; accordingly, the capacity data corresponding to the output energy specifically refers to the capacity total amount of the energy hub, which converts the input multiple input energy sources and then outputs the corresponding energy sources (i.e., the output energy sources, such as electric energy or heat energy) within a set time interval.
X capable of receiving external input by energy hub1～xmM kinds of energy sources (namely m kinds of input energy sources) and converts the input m kinds of energy sources to output y1To ynFor n energy sources (i.e., n output energy sources) as an example, each of the collected operation data should include: the energy supply total amount (namely energy supply data) corresponding to the m kinds of energy sources input to the energy source concentrator in a set time interval and the capacity total amount (namely capacity data) corresponding to the n kinds of energy sources output by the energy source concentrator in the set time interval.
The specific process of training to obtain the gaussian process regression model corresponding to each output energy source is not repeated here, and the gaussian process regression model corresponding to each output energy source obtained by training can be described specifically by using the following formula:
wherein, yiRepresenting the capacity data corresponding to the ith output energy in the n output energy, and the energy supply data X corresponding to the m input energy input to the energy concentrator at a set time interval1，x2，...，xm]K characterizes the kernel function, epsiloniRepresenting Gaussian noise and sigma corresponding to the ith output energyiCharacterised epsiloniStandard deviation of (2).
In order to adapt to the influence of the factors such as the running time, the load and the ambient temperature on the energy hub in the long-term running process, in one embodiment of the invention, the constructing the training data set includes: the method comprises the steps of periodically collecting operation data of an energy concentrator in a set time interval, and adding the collected operation data into a training data set.
In the embodiment, the operating data of the energy hub are continuously collected to be used as the basis of the Gaussian process regression model corresponding to each output energy of the training energy hub, namely, a large amount of operating data capable of expressing the operating conditions of the energy hub under different operating times, different loads and different environmental temperatures can exist in the training data set; meanwhile, when the capacity data of the energy hub needs to be predicted at different moments, different Gaussian process regression models can be trained and obtained for each output energy of the energy hub at different moments because the operation data in the training data set are not identical. Therefore, the actual operation condition of the energy concentrator can be combined, for each output energy of the energy concentrator, different Gaussian process regression models are obtained at different prediction moments in a data-driven mode to accurately predict the productivity data respectively corresponding to various output energy which the energy concentrator is likely to output when receiving various input energy respectively corresponding to certain energy supply data at the prediction moment.
It should be noted that the period of collecting the operation data is not necessarily related to the set time interval, and the set time interval may be a unit time, for example, one hour; the period of acquiring the operation data may be greater than the set time interval, for example, set to two hours, that is, both the period of acquiring the operation data and the set time interval may be set in combination with the actual service scenario. Typically, the period of collecting operational data and the set time interval may be 1 hour, or may be a time unit of smaller granularity, such as 1 minute.
The impact on the actual energy conversion of the energy hub with respect to run time, load and ambient temperature is generally non-transient; for example, when the operation time has an influence on the actual energy conversion rate of the energy hub, it is essential that the actual energy conversion rate of the energy hub gradually decreases with the increase of the operation time, rather than instantaneously decreasing the actual energy conversion rate of the energy hub from an extremely high value to an extremely low value; for another example, when the environment temperature affects the actual energy conversion rate of the energy hub, under the condition that other conditions are not changed, if the environment temperature does not change, the actual energy conversion rate of the energy hub should be kept at a constant value, meanwhile, when the environment temperature changes in a small range, the effect on the actual energy conversion rate of the energy hub is very small, and the environment temperature usually does not change greatly in a relatively short time, so that the effect on the environment temperature does not cause the actual energy conversion rate of the energy hub to rapidly decrease from a very high value to a very low value.
Then, assuming that the user needs to predict the capacity data corresponding to each output energy that the energy hub should output when receiving the input energy corresponding to a certain energy supply data at the current time, the more the collected operation data corresponds to the operation data with the time interval closer to the current time, the more the actual energy conversion condition of the energy hub at the current time can be reflected.
Therefore, in order to reduce the amount of data of the operation data used as the basis for training the gaussian process regression models respectively corresponding to the output energy sources in the case of ensuring that each of the trained gaussian process regression models can accurately predict the productivity data corresponding to the corresponding output energy source, in one embodiment of the present invention,
adding the collected operating data into a training data set, further comprising: setting a corresponding timestamp for the collected operation data;
further comprising: detecting whether the total amount of each operating data in the training data set is larger than a preset threshold value, if so, deleting one operating data in the training data set according to the timestamp corresponding to each operating data in the training data set.
As will be readily appreciated, the timestamp is specifically used to indicate the point in time when the corresponding operational data was acquired; the preset threshold may be an empirical value set in conjunction with a set time interval. The larger the set time interval is, the smaller the preset threshold value can be; conversely, the smaller the set time interval, the larger the preset threshold.
For example, taking the example of periodically collecting the operation data of the energy hub at a set time interval, the operation data are sequentially collected at t1～tnN operating data of the energy concentrator are respectively collected at n moments1The operation data collected at the moment can be corresponding to t1Setting the timestamp of the operation data acquired at the moment to t1Taking the preset threshold value n as an example, then, at tnThe nth operation data can be added into the training data set at the moment, and when the training data set is at the tn+1The method includes the steps that after the operation data of the energy hub are collected again at any moment, it can be detected that the operation data in the training data set are larger than a threshold value n, at the moment, one training data in the training data set can be deleted according to the timestamp of each operation data in the training data set, for example, according to the timestamp of each operation data, the corresponding timestamp which is added into the training data set at the earliest is deleted to be t1Is a running data.
To more clearly illustrate the technical solution of the present invention, as shown in fig. 2, an embodiment of the present invention provides another energy data prediction processing method, and when the method is used to predict the capacity data of the energy hub, the method may specifically include the following steps:
Here, the operation data includes energy supply data corresponding to at least two kinds of input energy inputted to the energy hub within a set time interval, and capacity data corresponding to at least two kinds of output energy outputted by the energy hub by converting the at least two kinds of input energy inputted within the set time interval.
In a possible implementation manner, after a user needs to predict that at least two input energy sources respectively corresponding to current function data are input to the energy hub at the current time, the energy hub converts the input at least two input energy sources respectively corresponding to current energy supply data and possibly outputs current capacity data respectively corresponding to at least two output energy sources respectively corresponding to output, the current function data respectively corresponding to the at least two input energy sources of the energy hub can be input from the outside, and thus, a gaussian process regression model respectively corresponding to each output energy source of the energy hub is triggered and trained.
In another possible implementation manner, training by using each operating data in the training data set may also be performed periodically to obtain a gaussian process regression model corresponding to each output energy, so as to implement periodic iterative update of the gaussian process regression model corresponding to each output energy. Therefore, when the capacity data corresponding to each output energy of the energy hub needs to be predicted, namely after the current energy data corresponding to at least two input energy of the externally input energy hub are received, for each output energy, the corresponding iteratively updated gaussian process regression model can be used for predicting the capacity data corresponding to the output energy.
The steps of the embodiment of the present invention are only described with respect to the first possible implementation manner.
And step 206, training by using each operation data in the training data set to obtain a Gaussian process regression model corresponding to each output energy respectively.
And step 207, substituting the current energy supply data into the Gaussian process regression model corresponding to the output energy for each output energy, and calculating the current capacity data corresponding to the output energy.
Referring to fig. 3, based on the same concept as the method embodiment of the present invention, an embodiment of the present invention further provides a processing apparatus for energy data prediction, including:
a training data management module 301, configured to construct a training data set, where the training data set includes a plurality of operation data;
the data receiving module 302 is configured to receive current energy supply data respectively corresponding to the at least two types of input energy input from the outside;
a model training module 303, configured to train with each of the operation data in the training data set to obtain a gaussian process regression model corresponding to each of the output energy sources;
and the capacity prediction module 304 is configured to substitute each current energy supply data into the gaussian process regression model corresponding to the output energy for each output energy, and calculate current capacity data corresponding to the output energy.
Specifically, the operation data includes: the energy supply system comprises energy supply data which correspond to at least two input energy sources input to an energy hub within a set time interval respectively, and capacity data which correspond to at least two output energy sources input to the energy hub within the set time interval and output through conversion.
In an embodiment of the present invention, the training data management module 301 is configured to periodically collect operation data of the energy hub within a set time interval, and add the collected operation data to a training data set.
In an embodiment of the present invention, the training data management module 301 is further configured to set a corresponding timestamp for the collected operation data; detecting whether the total amount of each operating data in the training data set is larger than a preset threshold value, if so, deleting one operating data in the training data set according to the timestamp corresponding to each operating data in the training data set.
For convenience of description, the above device embodiments are described as being separated into various units or modules, and the power supply of the units or modules may be implemented in the same software and/or hardware in implementing the invention.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention. On the hardware level, the electronic device comprises a processor and optionally an internal bus, a network interface and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory, such as at least 1 disk Memory. Of course, the electronic device may also include hardware required for other services.
The processor, the network interface, and the memory may be connected to each other via an internal bus, which may be an ISA (Industry standard architecture) bus, a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry standard architecture) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 4, but that does not indicate only one bus or one type of bus.
And the memory is used for storing the execution instruction. In particular, a computer program that can be executed by executing instructions. The memory may include both memory and non-volatile storage and provides execution instructions and data to the processor.
In a possible implementation manner, the processor reads the corresponding execution instruction from the nonvolatile memory to the memory and then runs the corresponding execution instruction, and the corresponding execution instruction can also be obtained from other equipment so as to form the processing device for energy data prediction on a logic level. The processor executes the execution instructions stored in the memory, so as to realize the processing method for energy data prediction provided by any embodiment of the invention through the executed execution instructions.
The method executed by the processing device for energy data prediction according to the embodiment of the present invention shown in fig. 3 may be applied to or implemented by a processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.
Embodiments of the present invention further provide a readable storage medium, where the readable storage medium stores executable instructions, and when the stored executable instructions are executed by a processor of an electronic device, the electronic device can be caused to perform the processing method for energy data prediction provided in any embodiment of the present invention, and is specifically configured to perform the methods shown in fig. 1 and fig. 2.
The electronic device described in the foregoing embodiments may be a computer.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects.
The embodiments of the present invention are described in a progressive manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, as for the apparatus embodiment, since it is substantially similar to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above description is only an example of the present invention, and is not intended to limit the present invention. Various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.
Claims (8)
1. A method for processing energy data prediction is characterized by comprising the following steps:
constructing a training data set, wherein the training data set comprises a plurality of operating data;
receiving current energy supply data respectively corresponding to at least two types of input energy input from the outside;
training by utilizing each operating data in the training data set to obtain a Gaussian process regression model corresponding to each output energy respectively;
for each output energy, substituting each current energy supply data into the Gaussian process regression model corresponding to the output energy, and calculating current capacity data corresponding to the output energy;
the operational data includes:
the energy concentrator comprises energy supply data which correspond to the at least two input energy sources input to the energy concentrator within a set time interval, and capacity data which correspond to each output energy source and are respectively converted and output by the energy concentrator within the set time interval.
2. The method of claim 1, wherein constructing the training data set comprises:
periodically collecting the operating data of the energy concentrator within a set time interval, and adding the collected operating data into a training data set;
and setting a corresponding time stamp for the collected operation data.
3. The method of claim 2, wherein the adding the collected operational data to a training data set comprises:
detecting whether the total amount of each operating data in the training data set is larger than a preset threshold value, if so, deleting one operating data in the training data set according to the timestamp corresponding to each operating data in the training data set.
4. A processing apparatus for energy data prediction, comprising:
the training data management module is used for constructing a training data set, and the training data set comprises a plurality of operation data;
the data receiving module is used for receiving current energy supply data respectively corresponding to at least two types of input energy input from the outside;
the model training module is used for training by utilizing each operating data in the training data set to obtain a Gaussian process regression model corresponding to each output energy;
the capacity prediction module is used for substituting each current energy supply data into the Gaussian process regression model corresponding to each output energy source according to each output energy source, and calculating the current capacity data corresponding to the output energy source;
the operational data includes: the energy supply data respectively corresponding to the at least two input energy sources input to the energy hub within a set time interval, and the energy hub converts and outputs the at least two input energy sources input within the set time interval to the energy capacity data respectively corresponding to the at least one output energy source.
5. The apparatus of claim 4,
the training data management module is used for periodically acquiring the operating data of the energy concentrator within a set time interval and adding the acquired operating data into a training data set;
and the corresponding timestamp is set for the collected operation data.
6. The apparatus of claim 5,
the training data management module is further configured to detect whether a total amount of each of the operation data in the training data set is greater than a preset threshold, and if so, delete one of the operation data in the training data set according to a timestamp corresponding to each of the operation data in the training data set.
7. A readable medium comprising executable instructions which, when executed by a processor of an electronic device, cause the electronic device to perform the method of any of claims 1 to 3.
8. An electronic device comprising a processor and a memory storing execution instructions, the processor performing the method of any of claims 1-3 when the processor executes the execution instructions stored by the memory.
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