CN114595851B - Air conditioner room power consumption analysis device using neural network - Google Patents
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Abstract
The invention relates to an analysis device for consumed power of an air conditioner room by utilizing a neural network, which comprises: the reinforcement learning equipment is used for taking the convolutional neural network model as an intelligent agent model, inputting input data associated with reservation information of a reservation user into the intelligent agent model and developing a reinforcement learning process of the intelligent agent model to obtain the current consumed power of the air conditioner room; and the strategy selection equipment is used for sending a manual intervention request to trigger manual field temperature control processing when the current consumed power of the air conditioner room is greater than or equal to a set power consumption threshold. The invention also relates to an analysis method for the consumed power of the air conditioner room by using the neural network. According to the method and the device, the intelligent prediction of the daily consumed power of the air conditioner room can be completed based on the pertinence-designed reinforcement learning intelligent agent model, and meanwhile, when the daily consumed power of the air conditioner room is overhigh, a worker is requested to be dispatched to the air conditioner room to execute temperature control processing, so that the carbon emission value of the air conditioner room is effectively reduced.
Description
Technical Field
The invention relates to the field of neural network application, in particular to an analysis device for consumed power of an air conditioner room by using a neural network.
Background
From many years, people have attempted to recognize and solve the problem of how the human brain works from various perspectives such as medicine, biology, physiology, philosophy, informatics, computer science, cognition, tissue synergetics, and the like. In the process of research for searching answers to the above-mentioned questions, an emerging multidisciplinary cross-technology field called "neural network" is gradually formed. The study of neural networks involves numerous areas of discipline that combine, interpenetrate, and drive each other. Scientists in different fields set out different problems and research from different angles based on the interests and characteristics of the respective disciplines.
The artificial neural network firstly needs to learn according to a certain learning criterion and then can work. Taking the recognition of two letters written "a" and "B" by an artificial neural network as an example, it is specified that "1" should be output when "a" is input to the network, and "0" should be output when "B" is input.
The criteria for web learning should be: if the network makes a wrong decision, learning through the network should cause the network to reduce the likelihood of making the same mistake the next time. Firstly, random values in the (0, 1) interval are given to each connection weight of the network, the image mode corresponding to the A is input to the network, the network carries out weighted summation, comparison with a threshold and nonlinear operation on the input modes, and the output of the network is obtained. In this case, the probabilities of the network outputs being "1" and "0" are each 50%, that is to say completely random. At this time, if the output is "1" (the result is correct), the connection weight is increased, so that the network can still make a correct judgment when encountering the input of the "A" mode again.
Neural networks are commonly used in various application fields, but due to their short popularization time and excessively detailed and complex application fields, specific application schemes are lacking in some fields. For example, when the air conditioner room is subjected to carbon emission management, an intelligent model is needed, which can perform intelligent prediction on the daily power consumption of the air conditioner room before the air conditioner room is started every day, and if the carbon emission corresponding to the prediction result is too high, a site manager needs to be arranged to perform temperature control processing to realize energy conservation and emission reduction of the air conditioner room. However, the prior art lacks targeted research efforts in the above-mentioned field.
Disclosure of Invention
In order to solve the above problems in the prior art, the present invention provides an analysis apparatus for power consumption of an air conditioner room using a neural network, which can use the habitual set temperature of each user who makes a reservation authorization to enter the air conditioner room every day as multiple inputs of a convolutional neural network, i.e., an intelligent model, use the power consumption of the air conditioner room every day as a single output of the intelligent model, predict the power consumption of the air conditioner room every day through a reinforcement learning algorithm, and if the power consumption is too high, execute manual intervention to perform temperature control, thereby forming a temperature regulation and control strategy in different states, and achieving the final purpose of reducing carbon emission.
Compared with the prior art, the invention at least needs to have the following prominent substantive characteristics:
(1) analyzing respective air-conditioning habit set temperatures by reserving information of each user authorized to enter the air-conditioning room every day, taking a plurality of air-conditioning habit set temperatures corresponding to a plurality of users with long working time as input data of an intelligent agent model, developing a reinforcement learning process of the intelligent agent model, obtaining the current consumption power of the air-conditioning room as output data of the reinforcement learning intelligent agent model, and executing intelligent prediction on the current consumption power of the air-conditioning room before the air-conditioning room is started every day;
(2) when the daily consumed power of the intelligently predicted air conditioner room is more than or equal to the set power consumption threshold, sending a manual intervention request to request a temperature control manager to arrive at the air conditioner room to perform manual intervention of temperature control on site, so that the phenomenon that the daily overall consumed power of the air conditioner room is too high is avoided, and the effect of reducing the carbon emission of the air conditioner room is achieved;
(3) inputting input data of a certain day in the historical date of the air conditioner room into the intelligent agent model, and executing a reinforcement learning process on the intelligent agent model by taking the energy saving value of the certain day of the air conditioner room as a reward signal for reinforcement learning on the intelligent agent model;
(4) the times of the reinforcement learning process of the reinforcement learning intelligent agent model are in direct proportion to the number of air conditioners in the air conditioner room, so that different reinforcement learning processes of different air conditioner rooms are customized.
According to a first aspect of the present invention, there is provided an apparatus for analyzing power consumption of an air conditioner room using a neural network, the apparatus including:
the system comprises information analysis equipment, a control equipment and a control equipment, wherein the information analysis equipment is used for acquiring the reservation information of each reservation user authorized to enter the air-conditioning room before the air-conditioning room is started every day, and the reservation information of each reservation user comprises a user name of the reservation user, the reserved working time and the air-conditioning habit set temperature;
the time length screening equipment is connected with the information analysis equipment and is used for acquiring the reservation information of each reservation user and taking the reservation user with the reserved working time length exceeding a set time length threshold value as a target user so as to acquire the reservation information of a plurality of target users;
the data integration equipment is connected with the duration screening equipment and is used for taking a plurality of air conditioner habit set temperatures respectively corresponding to a fixed number of target users with the longest reserved working duration in the plurality of target users as input data;
the reinforcement learning equipment is connected with the data integration equipment and is used for taking a convolutional neural network model as an intelligent agent model, inputting the input data into the intelligent agent model and developing the reinforcement learning process of the intelligent agent model to obtain the current power consumption of the air conditioner room as single output data of the reinforcement learning intelligent agent model;
the strategy selection equipment is connected with the reinforcement learning equipment and used for sending a manual intervention request to request a temperature control manager to arrive at the air conditioner room to execute manual intervention of temperature control on site when the daily consumed power of the received air conditioner room serving as prediction data is greater than or equal to a set power consumption threshold;
the strategy selection equipment is also used for sending a self-management instruction to refuse to send a temperature control manager to arrive at the air conditioner room to execute manual intervention of temperature control on site when the daily consumed power of the received air conditioner room serving as prediction data is smaller than the set power consumption threshold;
the method for acquiring the reservation information of a plurality of target users by taking the reservation users with the reservation working time length exceeding the set time length threshold as the target users comprises the following steps: and the value of the set time length threshold is in monotone positive correlation with the value of the opening duration of the air conditioner room.
According to a second aspect of the present invention, there is provided an apparatus for analyzing power consumption of an air conditioning room using a neural network, the apparatus comprising one or more processors and a memory, the memory storing a computer program configured to be executed by the one or more processors to perform the steps of:
the method comprises the steps that reservation information of each reservation user authorized to enter the air conditioner room is obtained before the air conditioner room is started every day, and the reservation information of each reservation user comprises a user name of the reservation user, a reserved working time and an air conditioner habit set temperature;
acquiring reservation information of each reservation user, and taking the reservation user with the reserved working time length exceeding a set time length threshold value as a target user to acquire reservation information of a plurality of target users;
a plurality of air conditioner habit set temperatures respectively corresponding to a plurality of target users with the longest reserved working time and a fixed number of target users are used as input data;
the convolutional neural network model is used as an intelligent agent model, the input data are input into the intelligent agent model, the reinforcement learning process of the intelligent agent model is carried out, and the power consumed by the air conditioner room on the same day is obtained and used as single output data of the reinforcement learning intelligent agent model;
when the daily consumed power of the air conditioner room which is received as the prediction data is more than or equal to a set power consumption threshold value, sending a manual intervention request to request a temperature control manager to arrive at the air conditioner room to execute manual intervention of temperature control on site;
when the daily consumed power of the received air conditioner room serving as prediction data is smaller than the set power consumption threshold, sending a self-management instruction to refuse to dispatch a temperature control manager to arrive at the air conditioner room to execute manual intervention of temperature control on site;
the method for acquiring the reservation information of a plurality of target users by taking the reservation users with the reservation working time length exceeding the set time length threshold as the target users comprises the following steps: and the value of the set time length threshold is in monotone positive correlation with the value of the opening duration of the air conditioner room.
According to a third aspect of the present invention, there is provided an air conditioner room power consumption analysis method using a neural network, the method including:
the method comprises the steps that reservation information of each reservation user authorized to enter the air conditioner room is obtained before the air conditioner room is started every day, and the reservation information of each reservation user comprises a user name of the reservation user, a reserved working time and an air conditioner habit set temperature;
acquiring reservation information of each reservation user, and taking the reservation user with the reserved working time length exceeding a set time length threshold value as a target user to acquire reservation information of a plurality of target users;
a plurality of air conditioner habit set temperatures respectively corresponding to a plurality of target users with the longest reserved working time and a fixed number of target users are used as input data;
the convolutional neural network model is used as an intelligent agent model, the input data are input into the intelligent agent model, the reinforcement learning process of the intelligent agent model is carried out, and the power consumed by the air conditioner room on the same day is obtained and used as single output data of the reinforcement learning intelligent agent model;
when the daily consumed power of the air conditioner room which is received as the prediction data is more than or equal to the set power consumption threshold, sending a manual intervention request to request a temperature control manager to arrive at the air conditioner room to perform manual intervention of temperature control;
when the daily consumed power of the received air conditioner room serving as the prediction data is smaller than the set power consumption threshold, sending a self-management instruction to refuse to send a temperature control manager to arrive at the air conditioner room to perform manual intervention of temperature control on site;
the method for acquiring the reservation information of a plurality of target users by taking the reservation users with the reservation working time length exceeding the set time length threshold as the target users comprises the following steps: and the value of the set time length threshold is in monotone positive correlation with the value of the opening duration of the air conditioner room.
Drawings
Embodiments of the invention will now be described with reference to the accompanying drawings, in which:
fig. 1 is a technical flowchart of an apparatus for analyzing power consumption of an air conditioner room using a neural network according to the present invention.
Fig. 2 is a schematic configuration diagram showing an apparatus for analyzing power consumption of an air conditioner room using a neural network according to embodiment 1 of the present invention.
Fig. 3 is a schematic configuration diagram showing an apparatus for analyzing power consumption of an air conditioner room using a neural network according to embodiment 2 of the present invention.
Fig. 4 is a schematic configuration diagram showing an apparatus for analyzing power consumption of an air conditioner room using a neural network according to embodiment 3 of the present invention.
Fig. 5 is a schematic configuration diagram showing an apparatus for analyzing power consumption of an air conditioning room using a neural network according to embodiment 4 of the present invention.
Fig. 6 is a schematic configuration diagram showing an apparatus for analyzing power consumption of an air conditioning room using a neural network according to embodiment 5 of the present invention.
Fig. 7 is a flowchart illustrating steps of a method for analyzing power consumption of an air conditioner room using a neural network according to embodiment 6 of the present invention.
Detailed Description
Air conditioners (Air conditioners) are Air conditioners. The device is used for manually regulating and controlling parameters such as temperature, humidity, flow rate and the like of ambient air in a building or a structure. Air conditioners generally comprise a cold source/heat source device, a cold and hot medium delivery system, a tail end device and other auxiliary devices. The cold source/heat source equipment mainly comprises a refrigeration host machine, a water pump, a fan and a pipeline system. The end device is responsible for utilizing the cold and heat from the transmission and distribution to specifically process the air state so as to enable the air parameters of the target environment to reach certain requirements.
The air conditioner is an indispensable part of people in modern life, the air conditioner provides cool for people, meanwhile, the air conditioner also brings increase of carbon emission, especially for a machine room accommodating a large number of air conditioners simultaneously, all air conditioners in the machine room are simultaneously turned on and kept in high-grade operation, the carbon emission per second is considerable, and therefore, the carbon emission of the machine room of the air conditioner needs to be controlled.
However, because the cooling and heating preferences of each user entering the air conditioner room are different, the air conditioner temperature set by each user is different, and the identity of the user allowed to enter the air conditioner room every day is not fixed, which results in that the power consumed by the air conditioner room every day and the carbon emission of the air conditioner room are not realistic by adopting a simple and rough prediction model. Therefore, an intelligent prediction model is needed, which can intelligently predict the daily power consumed by the air conditioner room according to the daily user information allowed to enter the air conditioner room, and further determine whether to dispatch manpower to perform field temperature adjustment, so as to avoid the overhigh daily carbon emission of the air conditioner room.
In order to overcome the defects, the invention builds an air conditioner room power consumption analysis device by utilizing a neural network, a convolutional neural network model is selected as an intelligent model, input data input into the intelligent model are extracted from user information allowed to enter the air conditioner room every day, a reinforcement learning process of the intelligent model is developed, and the power consumed by the air conditioner room every day is obtained and is used as single output data of the reinforcement learning intelligent model, so that the intelligent prediction of the power consumed by the air conditioner room every day is realized.
As shown in fig. 1, a technical flowchart of an apparatus for analyzing power consumption of an air conditioner room using a neural network according to the present invention is shown. As shown in fig. 1, the specific technical process of the present invention is as follows:
the method comprises the steps that firstly, before an air conditioner room reaches a working time period every day, reservation information of each reservation user entering the air conditioner room on the same day is obtained, and the reservation information of each reservation user comprises a user name of the reservation user, a reserved working time and an air conditioner habit set temperature;
secondly, screening out each reservation user with the reservation working time length exceeding a set time length threshold from each reservation user to serve as each target user of the current day so as to obtain reservation information of each target user;
thirdly, inputting the set temperature of each air conditioning habit of each target user as input data to a reinforcement learning intelligent agent model completing the set reinforcement learning process, and obtaining the power consumed by the air conditioner room on the same day as single output data of the reinforcement learning intelligent agent model, wherein the reinforcement learning intelligent agent model is based on a convolutional neural network;
and fourthly, carrying out numerical judgment on the acquired daily consumed power of the air conditioner room, and sending a manual intervention request to request a temperature control manager to arrive at the air conditioner room to execute manual intervention of temperature control when the daily consumed power of the air conditioner room is more than or equal to a set power consumption threshold value, so that whether the manual intervention of temperature control on the air conditioner room on the whole follow-up day is required or not is predicted in advance, and the scene of excessive carbon emission is effectively avoided.
The key point of the invention is that a reinforcement learning intelligent agent model for finishing the setting reinforcement learning process is adopted to predict whether a scene of excessive carbon emission occurs in the air conditioner room on site in a user self-regulation mode in advance, and when the scene of excessive carbon emission occurs is judged, a temperature control manager is dispatched to execute the site temperature control operation in advance, so that the carbon emission control effect and the labor cost are both considered.
Hereinafter, the apparatus for analyzing power consumption of an air conditioner room using a neural network according to the present invention will be described in detail in an embodiment.
Embodiment 1
Fig. 2 is a schematic configuration diagram showing an apparatus for analyzing power consumption of an air conditioning room using a neural network according to embodiment 1 of the present invention. As shown in fig. 2, the apparatus for analyzing power consumption of an air conditioner room using a neural network includes the following components:
the system comprises information analysis equipment, a control equipment and a control equipment, wherein the information analysis equipment is used for acquiring the reservation information of each reservation user authorized to enter the air-conditioning room before the air-conditioning room is started every day, and the reservation information of each reservation user comprises a user name of the reservation user, the reserved working time and the air-conditioning habit set temperature;
illustratively, for a certain day, there may be four reservation users A, B, C and D, whose respective reservation information is as follows:
reservation information of reservation user a: 2.0 hours, 25 ℃;
reservation information of the reservation user B: 6.5 hours, 22 ℃;
reservation information of reservation user C: 3.5 hours at 20 ℃;
reservation information of the reservation user D: 8.0 hours, 26 degrees celsius.
The time length screening equipment is connected with the information analysis equipment and is used for acquiring the reservation information of each reservation user and taking the reservation user with the reserved working time length exceeding a set time length threshold value as a target user so as to acquire the reservation information of a plurality of target users;
also for the above example, when the set time length threshold is selected to be 3 hours, the reservation information of a plurality of target users is filtered as follows:
reservation information of the reservation user B: 6.5 hours, 22 ℃;
reservation information of reservation user C: 3.5 hours at 20 ℃;
reservation information of the reservation user D: 8.0 hours, 26 degrees celsius.
The data integration equipment is connected with the duration screening equipment and is used for taking a plurality of air conditioner habit set temperatures respectively corresponding to a fixed number of target users with the longest reserved working duration in the plurality of target users as input data;
the reinforcement learning equipment is connected with the data integration equipment and is used for taking a convolutional neural network model as an intelligent agent model, inputting the input data into the intelligent agent model and developing the reinforcement learning process of the intelligent agent model to obtain the power consumption of the air conditioner room on the same day as single output data of the reinforcement learning intelligent agent model;
the strategy selection equipment is connected with the reinforcement learning equipment and used for sending a manual intervention request to request a temperature control manager to arrive at the air conditioner room to execute manual intervention of temperature control on site when the daily consumed power of the received air conditioner room serving as prediction data is larger than or equal to a set power consumption threshold;
the strategy selection equipment is also used for sending a self-management instruction to refuse to dispatch a temperature control manager to reach the air conditioner room to execute manual intervention of temperature control on site when the daily consumed power of the received air conditioner room serving as prediction data is smaller than the set power consumption threshold;
the method for acquiring the reservation information of a plurality of target users by taking the reservation users with the reservation working time length exceeding the set time length threshold as the target users comprises the following steps: and the value of the set time length threshold is in monotone positive correlation with the value of the opening duration of the air conditioner room.
Embodiment 2
Fig. 3 is a schematic configuration diagram showing an apparatus for analyzing power consumption of an air conditioning room using a neural network according to embodiment 2 of the present invention. As shown in fig. 3, unlike embodiment 1 of the present invention, the apparatus for analyzing power consumption of an air conditioner room using a neural network further includes:
the resource allocation equipment is connected with the strategy selection equipment and used for dispatching a set number of temperature control management personnel to reach an air conditioner room to execute manual intervention of temperature control when receiving a manual intervention request;
when receiving a manual intervention request, dispatching a set number of temperature control management personnel to arrive at an air conditioner room to execute the manual intervention of temperature control on site comprises the following steps: and the value of the set number is positively associated with the area of the air conditioner room.
For example, a value of the set number may be selected to be directly proportional to the area positive correlation of the air conditioner room, the value of the set number is 2 when the area of the air conditioner room is 400 square meters, the value of the set number is 4 when the area of the air conditioner room is 800 square meters, and the value of the set number is 6 when the area of the air conditioner room is 1200 square meters.
Embodiment 3
Fig. 4 is a schematic configuration diagram showing an apparatus for analyzing power consumption of an air conditioner room using a neural network according to embodiment 3 of the present invention. As shown in fig. 4, unlike embodiment 2 of the present invention, the apparatus for analyzing power consumption of an air conditioner room using a neural network further includes:
a data transceiving interface, disposed between the resource allocating device and the policy selecting device, for establishing a bidirectional wireless data communication link between the resource allocating device and the policy selecting device;
wherein the step of establishing a bidirectional wireless data communication link between the resource allocating device and the policy selecting device comprises: the established bidirectional wireless data communication link is a WIFI communication link or a ZIGBEE communication link.
Fig. 5 is a schematic configuration diagram showing an apparatus for analyzing power consumption of an air conditioner room using a neural network according to embodiment 4 of the present invention. As shown in fig. 5, unlike embodiment 1 of the present invention, the apparatus for analyzing power consumption of an air conditioner room using a neural network further includes:
and the parameter storage equipment is connected with the reinforcement learning equipment and is used for storing various network configuration parameters of the reinforcement learning intelligent agent model.
In any one of the above embodiments, optionally, in the apparatus for analyzing power consumption of an air conditioner room using a neural network:
taking the convolutional neural network model as an intelligent agent model, inputting the input data into the intelligent agent model and developing the reinforcement learning process of the intelligent agent model, and obtaining the current-day power consumption of the air conditioner room as single output data of the reinforcement learning intelligent agent model comprises the following steps: inputting input data of a certain day in the historical date of the air conditioner room into the intelligent agent model, and executing a reinforcement learning process on the intelligent agent model by taking the energy saving value of the certain day of the air conditioner room as a reward signal for reinforcement learning on the intelligent agent model;
the method comprises the following steps of taking a convolutional neural network model as an intelligent agent model, inputting the input data into the intelligent agent model and developing the reinforcement learning process of the intelligent agent model, and obtaining the current power consumption of the air conditioner room as single output data of the reinforcement learning intelligent agent model, wherein the single output data further comprises the following steps: the reinforcement learning intelligent agent model is an intelligent agent model which is subjected to a plurality of reinforcement learning processes;
wherein, reinforcement study intelligent agent model includes for the intelligent agent model through many times reinforcement study process: the times of the reinforcement learning process of the reinforcement learning intelligent agent model is in direct proportion to the number of the air conditioners in the air conditioner room.
In any one of the above embodiments, optionally, in the apparatus for analyzing power consumption of an air conditioner room using a neural network:
the step of using the plurality of air conditioning habit set temperatures respectively corresponding to the fixed number of target users with the longest reserved working time in the plurality of target users as input data comprises the following steps: when the total number of the target users is smaller than the fixed number, zero filling operation is carried out on more than one air conditioning habit set temperature of the difference number so as to obtain a plurality of air conditioning habit set temperatures of the fixed number;
wherein, the step of using the plurality of air conditioning habit set temperatures respectively corresponding to the fixed number of target users with the longest reserved working time as input data comprises the following steps: and when the total number of the target users is equal to the fixed number, directly taking the air-conditioning habit set temperatures respectively corresponding to the target users as input data.
In any one of the above embodiments, optionally, in the apparatus for analyzing power consumption of an air conditioner room using a neural network:
the monotonous positive correlation between the value of the set time length threshold and the value of the opening duration of the air conditioner room comprises the following steps: and the value of the opening duration of the air conditioner room is a multiple of the value of the set duration threshold.
And in any one of the above embodiments, optionally, the apparatus for analyzing power consumption of an air conditioner room using a neural network may further include:
the parallel communication component is respectively connected with the information analysis equipment, the duration screening equipment, the data integration equipment, the reinforcement learning equipment and the strategy selection equipment;
the parallel communication component is used for establishing a parallel communication link between the information analysis equipment, the duration screening equipment, the data integration equipment, the reinforcement learning equipment and the strategy selection equipment;
the parallel communication component is configured to establish a parallel communication link between the information analysis device, the duration screening device, the data integration device, the reinforcement learning device, and the policy selection device, and includes: the established parallel communication link is 8 bits or 16 bits.
Embodiment 5
Fig. 6 is a block diagram showing the configuration of an apparatus for analyzing power consumption of an air conditioning room using a neural network according to embodiment 5 of the present invention. As shown in fig. 6, the apparatus for analyzing power consumption of an air conditioner room using a neural network includes a memory and N processors, where N is a positive integer greater than or equal to 1, the memory stores a computer program configured to be executed by the N processors to perform the steps of:
the method comprises the steps that reservation information of each reservation user authorized to enter the air conditioner room is obtained before the air conditioner room is started every day, and the reservation information of each reservation user comprises a user name of the reservation user, a reserved working time and an air conditioner habit set temperature;
acquiring reservation information of each reservation user, and taking the reservation user with the reserved working time length exceeding a set time length threshold value as a target user to acquire reservation information of a plurality of target users;
a plurality of air conditioner habit set temperatures respectively corresponding to a plurality of target users with the longest reserved working time and a fixed number of target users are used as input data;
the convolutional neural network model is used as an intelligent agent model, the input data are input into the intelligent agent model, the reinforcement learning process of the intelligent agent model is carried out, and the power consumed by the air conditioner room on the same day is obtained and used as single output data of the reinforcement learning intelligent agent model;
when the daily consumed power of the air conditioner room which is received as the prediction data is more than or equal to the set power consumption threshold, sending a manual intervention request to request a temperature control manager to arrive at the air conditioner room to perform manual intervention of temperature control;
when the daily consumed power of the received air conditioner room serving as the prediction data is smaller than the set power consumption threshold, sending a self-management instruction to refuse to send a temperature control manager to arrive at the air conditioner room to perform manual intervention of temperature control on site;
the method for acquiring the reservation information of a plurality of target users by taking the reservation users with the reservation working time length exceeding the set time length threshold as the target users comprises the following steps: and the value of the set time length threshold is monotonically and positively associated with the value of the opening duration of the air conditioner room.
Embodiment 6
Fig. 7 is a flowchart illustrating steps of a method for analyzing power consumption of an air conditioner room using a neural network according to embodiment 6 of the present invention. As shown in fig. 7, the method for analyzing the power consumption of the air conditioner room using the neural network includes:
the method comprises the steps that reservation information of each reservation user authorized to enter an air conditioner room is obtained before the air conditioner room is started every day, wherein the reservation information of each reservation user comprises a user name of the reservation user, a reserved working time and an air conditioner habit set temperature;
acquiring reservation information of each reservation user, and taking the reservation user with the reserved working time length exceeding a set time length threshold value as a target user to acquire reservation information of a plurality of target users;
a plurality of air conditioner habit set temperatures respectively corresponding to a plurality of target users with the longest reserved working time and a fixed number of target users are used as input data;
the convolutional neural network model is used as an intelligent agent model, the input data are input into the intelligent agent model, the reinforcement learning process of the intelligent agent model is carried out, and the power consumed by the air conditioner room on the same day is obtained and used as single output data of the reinforcement learning intelligent agent model;
when the daily consumed power of the air conditioner room which is received as the prediction data is more than or equal to the set power consumption threshold, sending a manual intervention request to request a temperature control manager to arrive at the air conditioner room to perform manual intervention of temperature control;
when the daily consumed power of the received air conditioner room serving as the prediction data is smaller than the set power consumption threshold, sending a self-management instruction to refuse to send a temperature control manager to arrive at the air conditioner room to perform manual intervention of temperature control on site;
the method for acquiring the reservation information of a plurality of target users by taking the reservation users with the reservation working time length exceeding the set time length threshold as the target users comprises the following steps: and the value of the set time length threshold is in monotone positive correlation with the value of the opening duration of the air conditioner room.
In addition, the data transceiver interface, disposed between the resource allocating device and the policy selecting device, is configured to establish a bidirectional wireless data communication link between the resource allocating device and the policy selecting device, and includes: the data receiving and sending interface is a mobile communication interface, is arranged between the resource allocation equipment and the strategy selection equipment, and is used for establishing a bidirectional mobile data communication link between the resource allocation equipment and the strategy selection equipment;
or, the data transceiving interface, configured between the resource allocating device and the policy selecting device, and configured to establish a bidirectional wireless data communication link between the resource allocating device and the policy selecting device includes: the data receiving and sending interface is a WIFI communication interface, is arranged between the resource allocation equipment and the strategy selection equipment, and is used for establishing a bidirectional WIFI data communication link between the resource allocation equipment and the strategy selection equipment.
It should be noted that, in this document, 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.
Through the above description of the embodiments, those skilled in the art can clearly understand that the above method embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method embodiment of the present invention.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (8)
1. An apparatus for analyzing a power consumption of an air conditioner room using a neural network, the apparatus comprising:
the system comprises information analysis equipment, a control equipment and a control equipment, wherein the information analysis equipment is used for acquiring the reservation information of each reservation user authorized to enter the air-conditioning room before the air-conditioning room is started every day, and the reservation information of each reservation user comprises a user name of the reservation user, the reserved working time and the air-conditioning habit set temperature;
the time length screening equipment is connected with the information analysis equipment and is used for acquiring the reservation information of each reservation user and taking the reservation user with the reserved working time length exceeding a set time length threshold value as a target user so as to acquire the reservation information of a plurality of target users;
the data integration equipment is connected with the duration screening equipment and is used for taking a plurality of air conditioner habit set temperatures respectively corresponding to a fixed number of target users with the longest reserved working duration in the plurality of target users as input data;
the reinforcement learning equipment is connected with the data integration equipment and is used for taking a convolutional neural network model as an intelligent agent model, inputting the input data into the intelligent agent model and developing the reinforcement learning process of the intelligent agent model to obtain the current power consumption of the air conditioner room as single output data of the reinforcement learning intelligent agent model;
the strategy selection equipment is connected with the reinforcement learning equipment and used for sending a manual intervention request to request a temperature control manager to arrive at the air conditioner room to execute manual intervention of temperature control on site when the daily consumed power of the received air conditioner room serving as prediction data is greater than or equal to a set power consumption threshold;
the strategy selection equipment is also used for sending a self-management instruction to refuse to send a temperature control manager to arrive at the air conditioner room to execute manual intervention of temperature control on site when the daily consumed power of the received air conditioner room serving as prediction data is smaller than the set power consumption threshold;
the method for acquiring the reservation information of a plurality of target users by taking the reservation users with the reservation working time length exceeding the set time length threshold as the target users comprises the following steps: the value of the set time length threshold is in monotone positive correlation with the value of the opening duration of the air conditioner room;
the method comprises the following steps of taking a convolutional neural network model as an intelligent agent model, inputting the input data into the intelligent agent model and developing a reinforcement learning process of the intelligent agent model, and obtaining the current-day power consumption of the air conditioner room as single output data of the reinforcement learning intelligent agent model, wherein the single output data comprises the following steps: inputting input data of a certain day in the historical date of the air conditioner room into the intelligent agent model, and executing a reinforcement learning process on the intelligent agent model by taking the energy saving value of the certain day of the air conditioner room as a reward signal for reinforcement learning on the intelligent agent model;
the method comprises the following steps of taking a convolutional neural network model as an intelligent agent model, inputting the input data into the intelligent agent model and developing the reinforcement learning process of the intelligent agent model, and obtaining the current power consumption of the air conditioner room as single output data of the reinforcement learning intelligent agent model, wherein the single output data further comprises the following steps: the reinforcement learning intelligent agent model is an intelligent agent model which is subjected to a plurality of reinforcement learning processes;
wherein, reinforcement study intelligent agent model includes for the intelligent agent model through many times reinforcement study process: the times of the reinforcement learning process of the reinforcement learning intelligent agent model is in direct proportion to the number of the air conditioners in the air conditioner room.
2. The apparatus for analyzing power consumption of an air conditioner room using a neural network as set forth in claim 1, further comprising:
the resource allocation equipment is connected with the strategy selection equipment and used for dispatching a set number of temperature control management personnel to reach an air conditioner room to execute manual intervention of temperature control when receiving a manual intervention request;
when receiving a manual intervention request, dispatching a set number of temperature control management personnel to arrive at an air conditioner room to execute the manual intervention of temperature control on site comprises the following steps: and the value of the set number is positively associated with the area of the air conditioner room.
3. The apparatus for analyzing power consumption of an air conditioner room using a neural network as set forth in claim 2, further comprising:
and the data transceiving interface is arranged between the resource allocating device and the strategy selecting device and used for establishing a bidirectional wireless data communication link between the resource allocating device and the strategy selecting device.
4. The apparatus for analyzing power consumption of an air conditioner room using a neural network as set forth in claim 1, further comprising:
and the parameter storage equipment is connected with the reinforcement learning equipment and is used for storing various network configuration parameters of the reinforcement learning intelligent agent model.
5. The apparatus for analyzing power consumption of an air conditioner room using a neural network as set forth in any one of claims 1 to 4, wherein:
the step of using the plurality of air conditioning habit set temperatures respectively corresponding to the fixed number of target users with the longest reserved working time in the plurality of target users as input data comprises the following steps: and when the total number of the target users is smaller than the fixed number, performing zero filling operation on more than one air conditioning habit set temperature of the difference number to obtain a plurality of air conditioning habit set temperatures of the fixed number.
6. The apparatus for analyzing power consumption of an air conditioner room using a neural network as set forth in claim 5, wherein:
the step of using the plurality of air conditioning habit set temperatures respectively corresponding to the fixed number of target users with the longest reserved working time in the plurality of target users as input data comprises the following steps: and when the total number of the target users is equal to the fixed number, directly taking the air-conditioning habit set temperatures respectively corresponding to the target users as input data.
7. The apparatus for analyzing power consumption of an air conditioner room using a neural network as set forth in any one of claims 1 to 4, wherein:
the monotonous positive correlation between the value of the set time length threshold and the value of the opening duration of the air conditioner room comprises the following steps: and the value of the opening duration of the air conditioner room is a multiple of the value of the set duration threshold.
8. An apparatus for analyzing power consumption of an air conditioning room using a neural network, the apparatus comprising a memory and one or more processors, the memory storing a computer program configured to be executed by the one or more processors to perform the steps of:
the method comprises the steps that reservation information of each reservation user authorized to enter the air conditioner room is obtained before the air conditioner room is started every day, and the reservation information of each reservation user comprises a user name of the reservation user, a reserved working time and an air conditioner habit set temperature;
acquiring reservation information of each reservation user, and taking the reservation user with the reserved working time length exceeding a set time length threshold value as a target user to acquire reservation information of a plurality of target users;
a plurality of air conditioner habit set temperatures respectively corresponding to a plurality of target users with the longest reserved working time and a fixed number of target users are used as input data;
taking the convolutional neural network model as an intelligent agent model, inputting the input data into the intelligent agent model and developing the reinforcement learning process of the intelligent agent model to obtain the power consumed by the air conditioner room on the same day to serve as single output data of the reinforcement learning intelligent agent model;
when the daily consumed power of the air conditioner room which is received as the prediction data is more than or equal to a set power consumption threshold value, sending a manual intervention request to request a temperature control manager to arrive at the air conditioner room to execute manual intervention of temperature control on site;
when the daily consumed power of the received air conditioner room serving as the prediction data is smaller than the set power consumption threshold, sending a self-management instruction to refuse to send a temperature control manager to arrive at the air conditioner room to perform manual intervention of temperature control on site;
the method for acquiring the reservation information of a plurality of target users by taking the reservation users with the reservation working time length exceeding the set time length threshold as the target users comprises the following steps: the value of the set time length threshold is in monotone positive correlation with the value of the opening duration of the air conditioner room;
the method comprises the following steps of taking a convolutional neural network model as an intelligent agent model, inputting the input data into the intelligent agent model and developing a reinforcement learning process of the intelligent agent model, and obtaining the current-day power consumption of the air conditioner room as single output data of the reinforcement learning intelligent agent model, wherein the single output data comprises the following steps: inputting input data of a certain day in the historical date of the air conditioner room into the intelligent agent model, and executing a reinforcement learning process on the intelligent agent model by taking the energy saving value of the certain day of the air conditioner room as a reward signal for reinforcement learning on the intelligent agent model;
the method comprises the following steps of taking a convolutional neural network model as an intelligent agent model, inputting the input data into the intelligent agent model and developing the reinforcement learning process of the intelligent agent model, and obtaining the current power consumption of the air conditioner room as single output data of the reinforcement learning intelligent agent model, wherein the single output data further comprises the following steps: the reinforcement learning intelligent agent model is an intelligent agent model which is subjected to a plurality of reinforcement learning processes;
wherein, reinforcement study intelligent agent model includes for the intelligent agent model through many times reinforcement study process: the times of the reinforcement learning process of the reinforcement learning intelligent agent model is in direct proportion to the number of the air conditioners in the air conditioner room.
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