CN111896831B - Non-invasive comprehensive energy load monitoring method and system - Google Patents
Non-invasive comprehensive energy load monitoring method and system Download PDFInfo
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Abstract
The present disclosure provides a non-invasive comprehensive energy load monitoring method and system, acquiring the electric control temperature loads of different electrical appliances in an area; acquiring temperature values in the region and outside the region, and calculating a temperature difference; classifying the cold and hot load characteristics of the electric control temperature load; performing association mining between the cold and heat loads and the temperature difference, and determining an association rule; based on the weight generated by the determined association rule, carrying out load decomposition on the acquired electric control temperature loads of different electrical appliances in the region by using a clustering algorithm to obtain a decomposition result; the present disclosure makes the decomposition more persuasive by taking temperature as a factor affecting the state of the appliance.
Description
Technical Field
The disclosure belongs to the technical field of electric power operation control, and relates to a non-invasive comprehensive energy load monitoring method and system.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
In recent years, with the improvement of the standard of living, the increasing rate of indoor electricity consumption is increased year by year, and the usage amount of indoor cold and heat loads is continuously increased. The increase of energy consumption brings huge pressure to economy and environment, and the waste phenomenon of energy is more and more serious. The intelligent electric meter is used and popularized, so that the electric load service condition of a user can be accurately recorded, the user can analyze the user energy using behavior by monitoring the electric load of the user specific to equipment, and more effective energy-saving measures can be made.
The traditional Load Monitoring method is Intrusive resident Load Monitoring (ILM), and each electrical device in the total Load is equipped with a sensor with a digital communication function. The monitoring method has the defects of high cost, low reliability and the like, and related users can be invaded by the circuit construction and reconstruction. A method of Monitoring the power consumption and working state of each electric appliance used by a user by collecting and analyzing the total electric signal of the user through only installing a sensor at an entrance to obtain the power consumption state and power consumption law of the electric appliance is called Non-Intrusive residential Load Monitoring (NILM). Through the decomposition algorithm, the NILM replaces the sensor network of the traditional ILM, has the advantages of simplicity and reliability, and is suitable for general popularization.
According to the knowledge of the inventor, in the existing research, the non-invasive load monitoring only focuses on the use condition of the electric load, and the influence of the energy generated by the electrically-controlled cold and hot load on the non-invasive load monitoring is seldom focused. The research of the comprehensive energy system is a hot spot in the control field, and how to combine cold, heat and electric loads to improve the living environment is worthy of attention. At present, sensors for monitoring temperature in real time are arranged in many home and office buildings, and the output of cold and hot loads controlled by electricity can be adjusted in real time according to the temperature requirement of human bodies or equipment. In the prior art, the load decomposition is carried out by considering the association rule between the electric appliance states, but the influence of the association rule between the temperature and the electrically controlled cold and hot load on the load decomposition precision is not considered.
Disclosure of Invention
The invention aims to solve the problems and provides a non-invasive comprehensive energy load monitoring method and system, which can monitor indoor and outdoor temperatures and simultaneously play a role in improving precision of load decomposition in a room, thereby better understanding the running state of the load, making a relevant running strategy and achieving the effect of saving energy. The living behavior of residents can be adjusted through strategies and the cost of electricity utilization to adjust the room temperature, and cost saving is achieved.
According to some embodiments, the following technical scheme is adopted in the disclosure:
a method of non-intrusive integrated energy load monitoring, comprising the steps of:
acquiring the electric control temperature loads of different electrical appliances in the area;
acquiring temperature values in the region and outside the region, and calculating a temperature difference;
classifying the cold and hot load characteristics of the electric control temperature load;
performing association mining between the cold and heat loads and the temperature difference, and determining an association rule;
and carrying out load decomposition on the acquired electric control temperature loads of different electrical appliances in the region by utilizing a clustering algorithm based on the weight generated by the determined association rule to obtain a decomposition result.
In the technical scheme, the load decomposition that the electrical characteristics of the electrical appliance are only taken into consideration in the existing theory is changed, and the temperature is taken as a factor influencing the state of the electrical appliance, so that the decomposition result is more convincing.
As an alternative embodiment, the method further comprises the following steps:
and predicting the temperature change in the area according to the load decomposition result.
According to the result of load decomposition, the temperature change can be predicted in turn, so that more economical and comfortable energy using behaviors can be established; the benefit of the temperature monitoring equipment is maximized, and comprehensive utilization of energy is better completed.
As an alternative embodiment, the specific process of acquiring the electrically controlled temperature loads of different electrical appliances in the area includes: and sampling the active power and the reactive power of different electrical appliances in the area at regular time.
As an alternative embodiment, the specific process of acquiring the temperature values inside and outside the region includes: and the temperature inside and outside the region is sampled at regular time, and the temperature difference is the difference between the temperature outside the region and the temperature inside the region.
As an alternative embodiment, Apriori algorithm is used to mine the correlation between the cold and heat loads and the temperature difference.
Further, the method comprises the following steps: the method comprises the steps of calculating the temperature difference range inside and outside different season areas by combining the comfortable temperature of a human body, partitioning the temperature difference range at equal intervals to obtain a plurality of intervals, combining the temperature difference acquired at regular time with the acquired sample data of the electric appliance, and counting different states of each temperature difference interval and the electric control cold and hot loads of different electric appliances within a set time according to historical data.
Further, according to Apriori algorithm, the temperature difference delta t between the inside and the outside of the area is a leader A of the association rule, and the current state s of the electrically controlled cold and hot equipment is a successor B of the association rule, namely the rule is as the shape
Calculating support degree D by combining all samples collected in a period of timesuAnd confidence degree DcoWherein D issuInterval Δ t of temperature difference and state s of electric appliancet,FProbability of coincidence, DcoThe temperature difference between the indoor and the outdoor is observed at the moment t
Or
When the state of the electric appliance is st,FWill be lower than DsuOr DcoAnd (3) deleting the combination of the threshold values to obtain a frequent item set, combining every two items in the frequent item set to form a new candidate layer, calculating the promotion degree of the state of the related electric appliance, deleting the state combination with the promotion degree smaller than the set threshold value, and repeating continuously to obtain all frequent item sets meeting the association rule.
As an alternative embodiment, based on the weight generated by the determined association rule, the obtained electrically controlled temperature loads of different electrical appliances in the region are subjected to load decomposition by using a clustering algorithm, and the specific process of obtaining the decomposition result includes: load decomposition is carried out on a period of sampling time by using a clustering algorithm, active power and reactive power are taken from current sampling points, comparison is carried out on the active power and reactive power and load combination, k samples with the shortest distance are selected and attached with weights, the temperature difference inside and outside a current sampling area is compared with the k samples, the combination weight with the electric control cold and hot load state with relevance is multiplied by a relevance factor corresponding to a relevance rule, then weight comparison is carried out, and finally the load decomposition result of each electric appliance is obtained.
A system for non-intrusive integrated energy load monitoring, comprising:
the module is used for acquiring the electric control temperature loads of different electric appliances in the area;
a module for obtaining temperature values within and outside of the zone;
means for calculating a temperature difference;
a module for classifying cold and hot load characteristics of the electrically controlled temperature load;
the module is used for mining the association between the cold and hot loads and the temperature difference and determining an association rule;
and the module is used for carrying out load decomposition on the acquired electric control temperature loads of different electric appliances in the region by utilizing a clustering algorithm based on the weight generated by the determined association rule to obtain a decomposition result.
A computer readable storage medium having stored therein a plurality of instructions adapted to be loaded by a processor of a terminal device and to perform the method of non-intrusive integrated energy load monitoring.
A terminal device comprising a processor and a computer readable storage medium, the processor being configured to implement instructions; the computer readable storage medium stores instructions adapted to be loaded by a processor and to perform a method of non-intrusive integrated energy load monitoring.
Compared with the prior art, the beneficial effect of this disclosure is:
the method changes the load decomposition which simply takes the electrical characteristics of the electrical appliance into consideration in the existing theory, and the temperature is taken as a factor influencing the state of the electrical appliance, so that the decomposition result is more convincing; according to the result of load decomposition, the temperature change can be predicted in turn, so that more economical and comfortable energy using behaviors can be established; the benefit of the temperature monitoring equipment is maximized, and comprehensive utilization of energy is better completed.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and are not to limit the disclosure.
Fig. 1 is a flow chart of the present disclosure.
The specific implementation mode is as follows:
the present disclosure is further described with reference to the following drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
As shown in fig. 1, a mining method for association rules of electric control cold and heat load and indoor and outdoor temperature difference based on Apriori algorithm. Firstly, classifying the electric load characteristics of various electric control temperature loads collected by the intelligent electric meter by using a clustering algorithm, making a difference between indoor and outdoor temperatures collected by a temperature sensor, and mining the correlation between cold and hot loads and the temperature difference by using an Apriori algorithm. And after the association rule is obtained, the load decomposition is carried out on the collected total load by using a clustering algorithm by taking the weight generated by the association rule into account.
Specifically, since the temperature changes slowly relative to the characteristics of the electric appliance, the sampling frequency of the active power, the reactive power and the indoor and outdoor temperatures of the total load is set to 5min (of course, in other embodiments, other time intervals may be substituted). Considering that the cold and hot loads (air conditioner, electric fan, electric heater, etc.) controlled by electricity in summer and winter may change with the change of the indoor and outdoor temperature difference, it is sought whether there is a correlation between them. If the load is not controlled electrically, the room is maintained at a constant temperature for one day, and thus the change of the outdoor temperature difference is not affected.
Let the indoor temperature at time t be tt,iOutdoor temperature of tt,oThen difference between indoor and outdoor temperatures Δ ttIs composed of
Δtt＝tt,o-tt,i
The official data of the China weather bureau show that when the environmental temperature is 18 ℃ to 25 ℃, the human body feels most comfortable. Taking the heating situation in the north as an example, the lowest indoor temperature in winter is 18 ℃, the lowest outdoor temperature is-20 ℃ and the highest outdoor temperature is 10 ℃; the lowest temperature in summer and the highest temperature in summer are 25 ℃ and 40 ℃, so that the indoor and outdoor temperature difference delta t interval in summer is [0, 22 ℃), and the indoor and outdoor temperature difference t in winter is [ -8, -45 ℃).
Dividing the temperature difference into 5 and 8 intervals according to the temperature difference of every 5 ℃ in summer and winter, and marking the corresponding interval for every 5min of data according to the acquired data
And combining the temperature difference delta t collected every 5min with the collected electric appliance sample data. Counting different states s of each temperature difference interval and N electric control cold and heat loads at the moment t through historical datat,F(F＝1，2，…，N)。
According to the Apriori algorithm, the indoor and outdoor temperature difference delta t is a leader A of the association rule, and the current state s of the electrically controlled cold and hot equipment is a successor B of the association rule, namely the rule is like
Calculating support degree D by combining all samples collected in a period of timesuAnd confidence degree Dco：
Dsu＝p(Δtt∩st,F)
Dco＝p(st,F|Δtt)
Wherein D issuInterval Δ t of temperature difference and state s of electric appliancet,FProbability of coincidence, DcoThe temperature difference between the indoor and the outdoor is observed at the moment t
Or
When the state of the electric appliance is st,FThe probability of (c). Will be lower than DsuOr DcoAnd (4) deleting the combination of the threshold values to obtain a frequent item set, and combining every two items in the frequent item set to form a new candidate layer. By calculating the degree of lift Ili(Δt,st,F)：
Ili(Δt,st,F)＝p(st,FΔtt)/p(st,F)
And Kulc (Δ t, s)t,F)：
Kulc(Δt,st,F)＝1/2[p(Δtt∩st,F)+p(st,F|Δtt)]
Wherein, when Ili(Δt,st,F) Less than 1, the occurrence of both is negatively correlated; when I isli(Δt,st,F) Above 1, the occurrence of a and B is positively correlated, meaning that each occurrence implies the occurrence of the other; when I isli(Δt,st,F) Equal to 1, the two are independent and have no correlation between them. Will increase the degree of lifting Ili(Δt,st,F) Less than or equal to 1 and not exceeding Kulc (Deltat, s)t,F) The state combinations of the thresholds are deleted. Repeating the steps to finally select the condition meeting the association rule
All frequent itemsets of (1).
Load decomposition is carried out on a period of sampling time T by utilizing a KNN algorithm, and active power P is taken from a current sampling pointtAnd reactive power QtThe k samples closest to each other are selected and weighted in comparison with the combinations of loads in the set of samples. And using the indoor and outdoor temperature difference delta t of the current samplingtIn contrast to k samples, Kulc (Δ t, s) is multiplied by the combined weight of the electrically controlled cold and heat load states with correlationt,F) Corresponding correlation factors are compared by weight, and finally the load decomposition results of M devices are obtained
Wherein, F is 1, 2, …, M.
As will be appreciated by one skilled in the art, embodiments of the present disclosure may be provided as a method, system, or computer program product. Accordingly, the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein.
The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure, and various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Although the present disclosure has been described with reference to specific embodiments, it should be understood that the scope of the present disclosure is not limited thereto, and those skilled in the art will appreciate that various modifications and changes can be made without departing from the spirit and scope of the present disclosure.
Claims (10)
1. A non-invasive comprehensive energy load monitoring method is characterized by comprising the following steps: the method comprises the following steps:
acquiring the electric control temperature loads of different electrical appliances in the area;
acquiring temperature values in the region and outside the region, and calculating a temperature difference;
classifying the cold and hot load characteristics of the electric control temperature load;
performing association mining between the cold and heat loads and the temperature difference, and determining an association rule;
and carrying out load decomposition on the acquired electric control temperature loads of different electrical appliances in the region by utilizing a clustering algorithm based on the weight generated by the determined association rule to obtain a decomposition result.
2. The method of non-invasive integrated energy load monitoring of claim 1, further comprising: further comprising the steps of:
and predicting the temperature change in the area according to the load decomposition result.
3. The method of non-invasive integrated energy load monitoring of claim 1, further comprising: the specific process for acquiring the electric control temperature loads of different electric appliances in the area comprises the following steps: and sampling the active power and the reactive power of different electrical appliances in the area at regular time.
4. The method of non-invasive integrated energy load monitoring of claim 1, further comprising: the specific process for acquiring the temperature values in and out of the region comprises the following steps: and the temperature inside and outside the region is sampled at regular time, and the temperature difference is the difference between the temperature outside the region and the temperature inside the region.
5. The method of non-invasive integrated energy load monitoring of claim 1, further comprising: and (4) carrying out correlation mining between the cold and hot loads and the temperature difference by using an Apriori algorithm.
6. The method of non-invasive integrated energy load monitoring of claim 5, wherein: the method comprises the following steps: the method comprises the steps of calculating the temperature difference range inside and outside different season areas by combining the comfortable temperature of a human body, partitioning the temperature difference range at equal intervals to obtain a plurality of intervals, combining the temperature difference acquired at regular time with the acquired sample data of the electric appliance, and counting different states of each temperature difference interval and the electric control cold and hot loads of different electric appliances within a set time according to historical data.
7. The method of non-invasive integrated energy load monitoring of claim 5, wherein: according to the Apriori algorithm, the temperature difference delta t between the inside and the outside of the area is a leader A of the association rule, the current state s of the electrically controlled cold and hot equipment is a successor B of the association rule, namely the rule is as the same as
Calculating support degree D by combining all samples collected in a period of timesuAnd confidence degree DcoWherein D issuInterval Δ t of temperature difference and state s of electric appliancet,FProbability of coincidence, DcoIs that the indoor and outdoor temperature difference is observed at the moment t and is in the interval delta tl1Or Δ tl2When the state of the electric appliance is st,FWill be lower than DsuOr DcoAnd (3) deleting the combination of the threshold values to obtain a frequent item set, combining every two items in the frequent item set to form a new candidate layer, calculating the promotion degree of the state of the related electric appliance, deleting the state combination with the promotion degree smaller than the set threshold value, and repeating continuously to obtain all frequent item sets meeting the association rule.
8. The method of non-invasive integrated energy load monitoring of claim 1, further comprising: based on the weight generated by the determined association rule, the obtained electric control temperature loads of different electric appliances in the region are subjected to load decomposition by using a clustering algorithm, and the specific process of obtaining the decomposition result comprises the following steps: load decomposition is carried out on a period of sampling time by using a clustering algorithm, active power and reactive power are taken from current sampling points, comparison is carried out on the active power and reactive power and load combination, k samples with the shortest distance are selected and attached with weights, the temperature difference inside and outside a current sampling area is compared with the k samples, the combination weight with the electric control cold and hot load state with relevance is multiplied by a relevance factor corresponding to a relevance rule, then weight comparison is carried out, and finally the load decomposition result of each electric appliance is obtained.
9. A system for non-invasive comprehensive energy load monitoring is characterized in that: the method comprises the following steps:
the module is used for acquiring the electric control temperature loads of different electric appliances in the area;
a module for obtaining temperature values within and outside of the zone;
means for calculating a temperature difference;
a module for classifying cold and hot load characteristics of the electrically controlled temperature load;
the module is used for mining the association between the cold and hot loads and the temperature difference and determining an association rule;
and the module is used for carrying out load decomposition on the acquired electric control temperature loads of different electric appliances in the region by utilizing a clustering algorithm based on the weight generated by the determined association rule to obtain a decomposition result.
10. A computer-readable storage medium characterized by: a plurality of instructions stored therein, the instructions adapted to be loaded by a processor of a terminal device and to perform a method of non-intrusive integrated energy load monitoring as set forth in any of claims 1-8.
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