CN110188826A - Household electrical appliance operating status non-invasive inspection methods based on intelligent electric meter data - Google Patents

Household electrical appliance operating status non-invasive inspection methods based on intelligent electric meter data Download PDF

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CN110188826A
CN110188826A CN201910468488.3A CN201910468488A CN110188826A CN 110188826 A CN110188826 A CN 110188826A CN 201910468488 A CN201910468488 A CN 201910468488A CN 110188826 A CN110188826 A CN 110188826A
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王俊年
江来伟
于文新
孙嘉轩
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Hunan University of Science and Technology
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Abstract

The invention discloses a kind of household electrical appliance operating status non-invasive inspection methods based on intelligent electric meter data, comprising the following steps: acquire the electricity consumption general power data of subscriber household and the consumption power data of pre- electricity measurer；Electric operation state is labeled, and the power data of acquisition is normalized；Build deep learning network model；The LSTM deep learning network built is trained, trained LSTM network model is obtained；Trained LSTM network model is tested, neural network forecast accuracy is examined；It acquires any family to register one's residence intelligent electric meter power data, as the input of LSTM network model, detection identifies the operating status of multiple household electrical appliance.The present invention has good effect to the running state recognition of load, compared with common single prediction network, the training time of network greatly reduces, and can also reach good prediction effect using transfer learning for different areas, has very high value in production and living.

Description

Household electrical appliance operating status non-invasive inspection methods based on intelligent electric meter data
Technical field
The present invention relates to household electricity field, in particular to a kind of household electrical appliance based on intelligent electric meter data run shape State non-invasive inspection methods.
Background technique
Energy problem is one of maximum challenge of facing mankind in recent decades, in development in science and technology, people's living standard While raising, we also exceedingly develop earth resource.The use of electric power be energy consumption sharply increase it is very big because Element shows according to american energy consumption data handbook, 40 percent primary energy consumption and 70% electric power resource consumption From interior, have a high potential so saving indoor electric consumption.
Innovation Input and construction by many decades, national grid have basically reached wanting for ubiquitous Internet of Things in net side How the data asked, but generated in the user terminal of power grid, nearly 400,000,000 intelligent electric meters, do not obtain sufficiently effective utilization It is the service of electric power energy management value using these data, is the key that the ubiquitous electric power Internet of Things construction of development.Utilize intelligent electricity The operating status of the main electrical appliance of family can be obtained using electric load decomposition technique in the household electricity power data of table acquisition And detailed power consumption situation, demand side management can be carried out for electric system and analysis provides foundation, can such as provide in detail Thin electricity charge inventory helps user's using electricity wisely and detection failure electric appliance, monitors behaviour to look after and is easy to happen danger Crowd, and the accuracy of determination etc. for helping grid company to improve electric load distribution, to realize more effective electric power energy pipe Reason and value service provide safeguard.
Electric load decomposition technique is divided into two major classes: intrusive electric load decomposes (Intrusive load Decomposition, ILM) and non-intrusive electrical load decomposition (Non-intrusive load decomposition, NILM).ILM refers to is installing sensor in power load, detects the power consumption of electric appliance.Although the identification of this method electric appliance is quasi- True rate is higher, but it involves great expense, is easy to influence resident's daily life, not convenient for safeguarding.NILM refers on user's main circuit Intelligent electric meter is installed and obtains the data such as general power and total current, the work of all kinds of electrical equipments of user is identified according to these information State.Its advantages are safety economies, easy to spread, the disadvantage is that recognition accuracy is not high.In recent years, NILM becomes power load The mainstream research direction in lotus decomposition direction.
NILM is earliest by Hart of the Massachusetts Institute of Technology et al. proposition, subsequent bayesian algorithm, Hidden Markov Model (HMM), support vector machines (SVM), neural network model (ANN) scheduling algorithm are used for the field NILM.With rising abruptly for deep learning It rises, convolutional neural networks (CNN) and Recognition with Recurrent Neural Network (RNN) are used for electric load and decompose field, and shot and long term remembers net Network (LSTM) as an improvement after Recognition with Recurrent Neural Network, he, which can solve RNN and can not handle the dependence of long range, asks Topic has good recognition effect to the data of timing input.The ordinal number when input data of NILM is the general power of household electricity According to, modeled using the powerful memory function of LSTM network, for non-intrusive electrical load decompose NILM have it is good Effect.
Summary of the invention
That in order to solve the above technical problem, the present invention provides a kind of algorithms is simple, detection accuracy is high based on intelligent electric meter The household electrical appliance operating status non-invasive inspection methods of data.
Technical proposal that the invention solves the above-mentioned problems is: a kind of household electrical appliance operation shape based on intelligent electric meter data State non-invasive inspection methods, comprising the following steps:
Step 1: acquisition data: acquiring the electricity consumption general power data of multiple subscriber households whithin a period of time and prediction electricity The consumption power data of device, is divided into the data set of acquisition to obtain training set and test set；
Step 2: data mark and pretreatment: electric operation state is labeled according to the consumption power of electric appliance, and The power data of acquisition is normalized；
Step 3: building deep learning network model: LSTM deep learning network is built using TensorFlow framework, Input parameter, the network number of plies of adjustment network obtain optimal LSTM network model；
Step 4: trained and test deep learning network model: the training set generated using step 2 is to the LSTM built Deep learning network is trained, and continues to optimize network, is improved network training precision, is obtained trained LSTM network model；
Step 5: testing trained LSTM network model using the test data set that step 2 generates, and calculates Neural network forecast accuracy rate；
It registers one's residence intelligent electric meter power data Step 6: acquiring any family, as the input of LSTM network model, detection Identify the operating status of multiple household electrical appliance.
The above-mentioned household electrical appliance operating status non-invasive inspection methods based on intelligent electric meter data, in the step 1, Subscriber household electricity consumption general power data are acquired using home intelligent ammeter, the sample frequency of ammeter is 1/6Hz；In household electrical appliance Upper installation intelligent socket, the consumption power data of M pre- electricity measurers of acquisition.
The above-mentioned household electrical appliance operating status non-invasive inspection methods based on intelligent electric meter data, in the step 2, Electric operation state is labeled according to the consumption power of electric appliance, power threshold is arranged to pre- electricity measurer, will be more than threshold value Consumption power is labeled as ' 1 ', is labeled as ' 0 ' lower than threshold value；The power data of acquisition is pre-processed, according to power number According to mark state electric operation state is set, the data of training set test set are normalized, while making data Format meets the input/output format of LSTM network model；
Normalized handles general power data, specific formula using deviation standardized method are as follows:
Wherein xiFor i-th of value of certain general power data, m is the quantity of general power data, xminFor all general powers Minimum value, xmaxFor the maximum value of all general powers, xi' for normalization after data.
The above-mentioned household electrical appliance operating status non-invasive inspection methods based on intelligent electric meter data, in the step 3, It is first directed to processed general power data and correlation data, network hyper parameter is set；Then LSTM neural network is built, In LSTM neural network hidden layer, each LSTM neuron is respectively there are three door is controlled: forgeing door, input gate and output Door；Input variable enters in the neuron of LSTM the operation for passing through three doors:
ft=σ (Wf×[Ht-1,Xt']+bf)
In formula: ftThe output of door, X are forgotten for t momentt' indicate t moment LSTM neural network input matrix, Ht-1It indicates The output of t-1 moment entire neuron, bracket indicate that two vectors are connected and merge that σ is sigmoid function, WfTo forget door Weight, bfFor the bias matrix for forgeing door；
ht=σ (Wh×[Ht-1,Xt']+bh)
In formula: htIndicate the output of t moment input gate,Indicate the candidate matrices of input gate, tanh indicates tanh Function, Wh、bhIt indicates to calculate htWeight and bias matrix, Wc、bcIt indicates to calculateWeight and bias matrix；
The location mode C of t momenttThe output of door and the product of last moment state are forgotten plus the defeated of input gate for t moment Out with the product of candidate matrices:
ot=σ (Wo×[Ht-1,Xt']+bo)
In formula: otFor the output of t moment out gate, WoFor the bias matrix of out gate, boFor the weight matrix of out gate；
The output of the entire neuron of t moment are as follows:
Ht=ot×tanh(Ct)
Each LSTM neuron t moment input variable are as follows: t moment input matrix Xt', the location mode C at t-1 momentt-1 With the output H at t-1 momentt-1, each LSTM neuron t moment output are as follows: location mode CtH is exported with neuront。
The above-mentioned household electrical appliance operating status non-invasive inspection methods based on intelligent electric meter data, in the step 4, According to the output H of entire LSTM neural network hidden layert' come calculate prediction value matrix Y 'M:
Y'M=σ (H 't×Wu+bu)
In formula: Y'MFor neural network forecast value matrix, WuFor the bias matrix of output layer, buFor the weight matrix of output layer；
Pass through Y'MWith comparison matrix YMComparison is done to calculate the prediction error of LSTM neural network, select loss function and Optimizer, the LSTM neural network deconditioning after loss of network drops to 0.0001, and save LSTM neural network.
The above-mentioned household electrical appliance operating status non-invasive inspection methods based on intelligent electric meter data, in the step 4, Loss function is selected as the cross entropy loss function of sigmoid, formula are as follows:
Optimizer is selected as Adam algorithm optimization device.
The above-mentioned household electrical appliance operating status non-invasive inspection methods based on intelligent electric meter data, the step 5 tool Body step are as follows:
In LSTM neural network output layer, the neuron of the second hidden layer is mapped into M output, entire LSTM nerve net The output valve of network are as follows:
yi′'=Ht×W2+b2, i'=1,2 ..., M
In formula: yi′' be LSTM neural network network single output, W2For the weight matrix of the second hidden layer, b2It is The bias matrix of two hidden layers；
The prediction value matrix Y' of entire LSTM neural networkMAre as follows:
Y'M=[y '1,y'2,...,y'M]
By the prediction value matrix Y' of LSTM neural networkM, pass through function:
Prediction result is become ' 0 ', ' 1 ' matrix and comparison matrix YMIt compares, calculates the standard of LSTM neural network prediction True rate, to examine the prediction effect of network.
The above-mentioned household electrical appliance operating status non-invasive inspection methods based on intelligent electric meter data, to different areas, The power of electric appliance and operating status will be different due to weather and people's living habit, if directly by LSTM nerve Network model is applied to the pre- electricity measurer in different regions, and prediction result will not known, if resurveying data to train new net Network, it will a large amount of manpower and material resources are expended, in this regard, coming the bad area of prediction effect using the method for transfer learning, it may be assumed that use A small amount of household electricity general power data and pre- electricity measurer in home intelligent ammeter and intelligent socket acquisition prediction area consume power Then data are labeled and pre-process to data, carry out transfer learning to trained LSTM network with low volume data, instruction White silk finishes the electric operation state that can predict this area.
The beneficial effects of the present invention are: the household electrical appliance proposed by the invention based on intelligent electric meter data run shape State non-invasive inspection methods have good effect to the running state recognition of load, with common single prediction network phase Than the present invention can disposably predict the operating status of multiple electric appliances, and during electric appliance prediction, one family is often needed Predict multiple electric appliances, some is even had tens kinds, is predicted using single network, and data processing, network establishment take time and effort, And method of the invention is used, data handling procedure will simplify, not need to be handled one by one according to each electric appliance, can be with Multiple electric appliance batch processings, the training time of network greatly reduce, and can also reach good using transfer learning for different areas Good prediction effect has very high value in production and living.
Detailed description of the invention
Fig. 1 is flow chart of the invention.
Fig. 2 is the structural schematic diagram of LSTM neural network of the invention.
Fig. 3 is the LSTM network establishment figure under tensorflow frame of the invention.
Specific embodiment
The present invention is further illustrated with reference to the accompanying drawings and examples.
As shown in Figure 1, a kind of household electrical appliance operating status non-invasive inspection methods based on intelligent electric meter data, packet Include following steps:
Step 1: acquisition data.
Subscriber household electricity consumption general power data are acquired using home intelligent ammeter, the sample frequency of ammeter is 1Hz；It is in Intelligent socket is installed on electrical appliance, acquires the consumption power data of pre- electricity measurer.The present embodiment acquires altogether 4 different electricity Device consumes power, is hot-water bottle, micro-wave oven, dish-washing machine and washing machine respectively, and intelligent socket sample frequency is 1/6Hz.It has recorded The power consumption data of 1 family, record total time is more than 20 days, and total amount of data 1700000, the text of data CSV format Part saves, and is divided into the data set of acquisition to obtain training set and test set.
Step 2: data mark and pretreatment.
Electric operation state is labeled according to the consumption power of electric appliance, to hot-water bottle, micro-wave oven, dish-washing machine and is washed Power threshold is arranged in clothing machine, is 2000W, 200W, 10W and 20W respectively, will be more than that the consumption power of threshold value is labeled as ' 1 ', low ' 0 ' is labeled as in threshold value；Then the data marked are handled, adjustment general power data frequency to 1/6Hz uses Sliding window extracts general power and appliance power data, and data is made to be concentrated with 50% data electric appliance in a situation of use Data.100,000 datas are extracted altogether, and every data has the sampled point of 100 general powers and the operating status of 400 different electric appliances Sampled point (100 sampled points of each electric appliance), i.e., 10 minutes sampled datas, it includes general power data and 4 in 10 minutes The running state data of a electric appliance.In 100 sampled points of each electric appliance, if occurring continuous 3 ' 1 ', by this number of segment It according to operating status is identified as, is indicated with ' 1 ', if not occurring, is identified as not running state, is indicated with ' 0 '.Every right Than the combination that data are 4 ' 0 ' and ' 1 ', correlation data total number is identical with general power number of data.By training set test The data of collection are normalized, while data format being made to meet the input/output format of LSTM network model；
Normalized handles general power data, specific formula using deviation standardized method are as follows:
Wherein xiFor i-th of value of certain general power data, m is the quantity of general power data, xminFor all general powers Minimum value, xmaxFor the maximum value of all general powers, xi' for normalization after data.
If training data shares 100, input data matrix are as follows:
Xi'=[x1',x2',...,x100']；
Processed general power data set and correlation data collection are saved to csv file.
Step 3: building deep learning network model: LSTM deep learning network is built using TensorFlow framework, Input parameter, the network number of plies of adjustment network obtain optimal LSTM network model.Detailed process are as follows:
It is first directed to processed general power data and correlation data, network hyper parameter is set, every 50 data is one Batch is inputted, every data inputs 100 total power values, exports 4 predicted values, learning rate 0.0001, network concealed layer is 2 layers, first layer has 128 neurons, and the second layer has 256 neurons；Then LSTM neural network, LSTM nerve net are built In network hidden layer, each LSTM neuron is respectively there are three door is controlled: forgeing door, input gate and out gate；Input variable Into in the neuron of LSTM pass through three doors operation:
ft=σ (Wf×[Ht-1,Xt']+bf)
In formula: ftThe output of door, X are forgotten for t momentt' indicate t moment LSTM neural network input matrix, Ht-1It indicates The output of t-1 moment entire neuron, bracket indicate that two vectors are connected and merge that σ is sigmoid function, WfTo forget door Weight, bfFor the bias matrix for forgeing door；
ht=σ (Wh×[Ht-1,Xt']+bh)
In formula: htIndicate the output of t moment input gate,Indicate the candidate matrices of input gate, tanh indicates tanh Function, Wh、bhIt indicates to calculate htWeight and bias matrix, Wc、bcIt indicates to calculateWeight and bias matrix；t The location mode C at momenttOutput and candidate for the output of t moment forgetting door and the product of last moment state plus input gate The product of matrix:
ot=σ (Wo×[Ht-1,Xt']+bo)
In formula: otFor the output of t moment out gate, WoFor the bias matrix of out gate, boFor the weight matrix of out gate；
The output of the entire neuron of t moment are as follows:
Ht=ot×tanh(Ct)
Each LSTM neuron t moment input variable are as follows: t moment input matrix Xt', the location mode C at t-1 momentt-1 With the output H at t-1 momentt-1, each LSTM neuron t moment output are as follows: location mode CtH is exported with neuront。
Step 4: trained and test deep learning network model: the training set generated using step 2 is to the LSTM built Deep learning network is trained, and continues to optimize network, is improved network training precision, is obtained trained LSTM network model. Detailed process are as follows:
According to the output H of entire LSTM neural network hidden layert' come calculate prediction value matrix Y'M:
Y'M=σ (H 't×Wu+bu)
In formula: Y'MFor neural network forecast value matrix, WuFor the bias matrix of output layer, buFor the weight matrix of output layer；
Pass through Y'MWith comparison matrix YMComparison is done to calculate the prediction error of LSTM neural network, select loss function and Optimizer, the LSTM neural network deconditioning after loss of network drops to 0.0001, and save LSTM neural network.
Loss function is selected as the cross entropy loss function of sigmoid, formula are as follows:
Optimizer is selected as Adam algorithm optimization device.
Step 5: testing trained LSTM network model using the test data set that step 2 generates, and calculates Neural network forecast accuracy rate.Specific steps are as follows:
In LSTM neural network output layer, the neuron of the second hidden layer is mapped into 4 outputs, entire LSTM nerve net The output valve of network are as follows:
yi′'=Ht×W2+b2, i'=1,2 ..., 4
In formula: yi′' be LSTM neural network network single output, W2For the weight matrix of the second hidden layer, b2It is The bias matrix of two hidden layers；
The prediction value matrix Y ' of entire LSTM neural network4Are as follows:
Y′4=[y '1,y'2,...,y'4]
By the prediction value matrix Y ' of LSTM neural network4, pass through function:
Prediction result is become ' 0 ', ' 1 ' matrix and comparison matrix YMIt compares, calculates the standard of LSTM neural network prediction True rate, to examine the prediction effect of network.
It registers one's residence intelligent electric meter power data Step 6: acquiring any family, as the input of LSTM network model, detection Identify the operating status of multiple household electrical appliance such as hot-water bottle, micro-wave oven, dryer and washing machine.Table 1 is LSTM in the present embodiment The predictablity rate of neural network.
Table 1
Predictablity rate
Hot-water bottle 95.6%
Micro-wave oven 91.8%
Dish-washing machine 87.6%
Washing machine 92.7%
To different areas, the power of electric appliance and operating status can be due to weather and people's living habit Difference, if LSTM neural network model is directly applied to the pre- electricity measurer in different regions, prediction result will not known, if again Data are acquired to train new network, it will a large amount of manpower and material resources are expended, in this regard, predicting using the method for transfer learning The bad area of effect, it may be assumed that with a small amount of household electricity general power number of home intelligent ammeter and intelligent socket acquisition prediction area Power data is consumed according to pre- electricity measurer, then data are labeled and are pre-processed, with low volume data to trained LSTM Network carries out transfer learning, and training finishes the electric operation state that can predict this area, and table 2 is LSTM in the present embodiment The training time table of neural network model.
Table 2
Single output network superposition Multi output network
Training time (min) 1416.8 347.86
As can be seen from the results it is proposed by the invention based on the Household appliance switch recognition methods of LSTM neural network to load Running state recognition have good effect.Compared with common single prediction network, the training time of network subtracts significantly It is few, good prediction effect can also be reached using transfer learning for different areas, this network has very in production and living High value.

Claims (8)

1. a kind of household electrical appliance operating status non-invasive inspection methods based on intelligent electric meter data, comprising the following steps:
Step 1: acquisition data: acquiring multiple subscriber households electricity consumption general power data whithin a period of time and pre- electricity measurer Power data is consumed, is divided into the data set of acquisition to obtain training set and test set；
Step 2: data mark and pretreatment: being labeled according to the consumption power of electric appliance to electric operation state, and to acquisition Power data be normalized；
Step 3: building deep learning network model: building LSTM deep learning network using TensorFlow framework, adjust net Input parameter, the network number of plies of network obtain optimal LSTM network model；
Step 4: trained and test deep learning network model: the training set generated using step 2 is to the LSTM depth built Learning network is trained, and continues to optimize network, is improved network training precision, is obtained trained LSTM network model；
Step 5: testing trained LSTM network model using the test data set that step 2 generates, and calculates network Predictablity rate；
It is more to detect identification Step 6: acquiring any family and registering one's residence intelligent electric meter power data as the input of LSTM network model The operating status of a household electrical appliance.
2. the household electrical appliance operating status non-invasive inspection methods according to claim 1 based on intelligent electric meter data, It is characterized in that, acquiring subscriber household electricity consumption general power data, the sampling of ammeter using home intelligent ammeter in the step 1 Frequency is 1/6Hz；Intelligent socket, the consumption power data of M pre- electricity measurers of acquisition are installed on household appliances.
3. the household electrical appliance operating status non-invasive inspection methods according to claim 1 based on intelligent electric meter data, It is characterized in that, being labeled according to the consumption power of electric appliance to electric operation state, being set to pre- electricity measurer in the step 2 Power threshold is set, the consumption power more than threshold value is labeled as ' 1 ', is labeled as ' 0 ' lower than threshold value；To the power data of acquisition It is pre-processed, electric operation state is arranged according to the mark state of power data, the data of training set test set are carried out Normalized, while data format being made to meet the input/output format of LSTM network model；
Normalized handles general power data, specific formula using deviation standardized method are as follows:
Wherein xiFor i-th of value of certain general power data, m is the quantity of general power data, xminFor the minimum of all general powers Value, xmaxFor the maximum value of all general powers, xi' for normalization after data.
4. the household electrical appliance operating status non-invasive inspection methods according to claim 3 based on intelligent electric meter data, It is characterized in that, being first directed to processed general power data and correlation data in the step 3, network hyper parameter is set； Then LSTM neural network is built, in LSTM neural network hidden layer, each LSTM neuron is respectively there are three door is controlled: Forget door, input gate and out gate；Input variable enters in the neuron of LSTM the operation for passing through three doors:
ft=σ (Wf×[Ht-1,Xt']+bf)
In formula: ftThe output of door, X are forgotten for t momentt' indicate t moment LSTM neural network input matrix, Ht-1When indicating t-1 The output of entire neuron is carved, bracket indicates that two vectors are connected and merge that σ is sigmoid function, WfFor the power for forgeing door Weight, bfFor the bias matrix for forgeing door；
ht=σ (Wh×[Ht-1,Xt']+bh)
In formula: htIndicate the output of t moment input gate,Indicating the candidate matrices of input gate, tanh indicates hyperbolic tangent function, Wh、bhIt indicates to calculate htWeight and bias matrix, Wc、bcIt indicates to calculateWeight and bias matrix；
The location mode C of t momenttThe output and time of input gate are added for the output of t moment forgetting door and the product of last moment state Select the product of matrix:
ot=σ (Wo×[Ht-1,Xt']+bo)
In formula: otFor the output of t moment out gate, WoFor the bias matrix of out gate, boFor the weight matrix of out gate；
The output of the entire neuron of t moment are as follows:
Ht=ot×tanh(Ct)
Each LSTM neuron t moment input variable are as follows: t moment input matrix Xt', the location mode C at t-1 momentt-1When with t-1 The output H at quartert-1, each LSTM neuron t moment output are as follows: location mode CtH is exported with neuront。
5. the household electrical appliance operating status non-invasive inspection methods according to claim 4 based on intelligent electric meter data, It is characterized in that, in the step 4, according to the output H of entire LSTM neural network hidden layert' calculate prediction value matrix Y'M:
Y'M=σ (H 't×Wu+bu)
In formula: Y'MFor neural network forecast value matrix, WuFor the bias matrix of output layer, buFor the weight matrix of output layer；
Pass through Y'MWith comparison matrix YMComparison is done to calculate the prediction error of LSTM neural network, selects loss function and optimization Device, the LSTM neural network deconditioning after loss of network drops to 0.0001, and save LSTM neural network.
6. the household electrical appliance operating status non-invasive inspection methods according to claim 4 based on intelligent electric meter data, It is characterized in that, loss function is selected as the cross entropy loss function of sigmoid, formula in the step 4 are as follows:
Optimizer is selected as Adam algorithm optimization device.
7. the household electrical appliance operating status non-invasive inspection methods according to claim 6 based on intelligent electric meter data, It is characterized in that, the step 5 specific steps are as follows:
In LSTM neural network output layer, the neuron of the second hidden layer is mapped M and is exported, entire LSTM neural network it is defeated It is worth out are as follows:
yi′'=Ht×W2+b2, i'=1,2 ..., M
In formula: yi′' be LSTM neural network network single output, W2For the weight matrix of the second hidden layer, b2It is hidden for second The bias matrix of layer；
The prediction value matrix Y' of entire LSTM neural networkMAre as follows:
Y'M=[y '1,y'2,...,y'M]
By the prediction value matrix Y' of LSTM neural networkM, pass through function:
Prediction result is become ' 0 ', ' 1 ' matrix and comparison matrix YMIt compares, calculates the accuracy rate of LSTM neural network prediction, To examine the prediction effect of network.
8. the household electrical appliance operating status non-invasive inspection methods according to claim 7 based on intelligent electric meter data, It is characterized in that, the power of electric appliance and operating status have due to weather and people's living habit to different areas Institute is different, if LSTM neural network model is directly applied to the pre- electricity measurer in different regions, prediction result will not known, if again Data are acquired to train new network, it will a large amount of manpower and material resources are expended, in this regard, predicting to imitate using the method for transfer learning The bad area of fruit, it may be assumed that with a small amount of household electricity general power data of home intelligent ammeter and intelligent socket acquisition prediction area Power data is consumed with pre- electricity measurer, then data are labeled and are pre-processed, with low volume data to trained LSTM net Network carries out transfer learning, and training finishes the electric operation state that can predict this area.
CN201910468488.3A 2019-05-31 2019-05-31 Household electrical appliance operating status non-invasive inspection methods based on intelligent electric meter data Pending CN110188826A (en)

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CN110852655B (en) * 2019-11-29 2023-08-15 湖南科技大学 Household electricity load decomposition system with solar power supply system and household electricity load decomposition method
CN111126489A (en) * 2019-12-24 2020-05-08 广东电网有限责任公司 Power transmission equipment state evaluation method based on ensemble learning
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CN111563827A (en) * 2020-04-10 2020-08-21 杭州电子科技大学 Load decomposition method based on electrical appliance physical characteristics and residential electricity consumption behaviors
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CN111551773A (en) * 2020-05-14 2020-08-18 资明 Intelligent power management system
CN111917184A (en) * 2020-05-19 2020-11-10 国网山东省电力公司济宁供电公司 Power consumption monitoring method and system based on BP neural network
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CN113363979A (en) * 2021-06-23 2021-09-07 中国科学技术大学 Non-contact power load decomposition sensing method and system
CN113363979B (en) * 2021-06-23 2022-12-30 中国科学技术大学 Non-contact power load decomposition sensing method and system
CN113434493A (en) * 2021-06-28 2021-09-24 湘潭大学 Non-invasive load decomposition method based on Transformer
CN113568383A (en) * 2021-06-30 2021-10-29 宁波三星医疗电气股份有限公司 Non-invasive identification method suitable for machining center
CN113837894B (en) * 2021-08-06 2023-12-19 国网江苏省电力有限公司南京供电分公司 Non-invasive resident user load decomposition method based on residual convolution module
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CN113962314A (en) * 2021-10-27 2022-01-21 南京富尔登科技发展有限公司 Non-invasive enterprise load decomposition method based on federal learning
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