CN105184408B - Power-economizing method based on electric appliance effectiveness classification and user power utilization Activity recognition - Google Patents

Power-economizing method based on electric appliance effectiveness classification and user power utilization Activity recognition Download PDF

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CN105184408B
CN105184408B CN201510581332.8A CN201510581332A CN105184408B CN 105184408 B CN105184408 B CN 105184408B CN 201510581332 A CN201510581332 A CN 201510581332A CN 105184408 B CN105184408 B CN 105184408B
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electric appliance
degree
user
energy
energy saving
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CN201510581332.8A
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CN105184408A (en
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何光宇
何勃兴
何果红
李川江
赵雪霖
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上海上塔软件开发有限公司
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    • Y02E40/76
    • Y04S10/545

Abstract

The present invention relates to a kind of power-economizing methods based on electric appliance effectiveness classification and user power utilization Activity recognition, comprising the following steps: step 1: provides the objective function of energy saving optimizing.Step 2: the basic constraint condition of energy saving optimizing is provided.Step 3: user power utilization behavior self-identifying is carried out according to collected electric operation state s (d, t) and obtains the relevance between the electricity consumption behavior of user by establishing hidden Markov model.Step 5: control strategy is judged according to degree of utility.Step 6: judging whether next scheduling time section continues energy conservation, if continuing, skips to step 2;If not going on, energy saving optimizing terminates.This method targetedly can carry out analysis identification to user power utilization behavior, and provide Energy Saving Strategy automatically, and fractional energy savings is high, and effect is good, to realize that automatic energy saving and home energy management provide good technical support.

Description

Power-economizing method based on electric appliance effectiveness classification and user power utilization Activity recognition

Technical field

The invention belongs to technical field of power systems, and in particular to and it is a kind of in the intelligent power environment of electric power demand side, Based on electric appliance degree of utility division methods, the power mode of dynamic auto optimization power consumer, realization in real time is with energy conservation The electric energy management method of main target.

Background technique

In recent years, the severe energy is very urgent with the energy-saving and emission-reduction that environmental issue makes electric system.And electricity needs Energy consumption waste in side is serious, is the primary study object in electric system energy-saving and emission-reduction problem.The saving of electric power demand side is used It is electric mainly to pass through electric energy management, i.e., it is realized by changing user power utilization mode.Optimize control by the power mode of target of energy conservation System finally implements in the operating status adjustment of electric appliance load.

With energy conservation for main syllabus target user power utilization mode dynamic optimization, need to reduce as far as possible in the target time period Energy consumption total value in target area.Foreign countries are mostly to comprehensively consider electricity price, energy consumption, temperature, use in the energy saving model of electric power demand side The multi-objective optimization question of the factors such as family comfort level, optimization method includes genetic algorithm, neural network, particle group optimizing, random Optimization algorithm etc. based on historical load curve prediction electricity consumption and provides energy saving optimizing scheme.Section of the country in electric power demand side Energy area research is mostly the design and realization of user side Energy Management System, further models and optimizes still to how to be based on system Need more in-depth study.

This patent is used to specifically design a kind of energy-saving square based on electric appliance effectiveness classification and user power utilization Activity recognition Method.

Summary of the invention

For above situation, the present invention provides a kind of section based on electric appliance effectiveness classification and user power utilization Activity recognition Energy method, the history power information based on electric power establish hidden Markov model and carry out automatic knowledge in real time to user power utilization behavior Not, the electricity consumption behavior of user and the association probability of electric appliance can be better understood by.Further combined with electric appliance grade classification side Method obtains electric appliance degree of utility, and formulates corresponding Energy Saving Control strategy according to grade.This method can be targeted Analysis identification is carried out to user power utilization behavior, and provides Energy Saving Strategy automatically, fractional energy savings is high, and effect is good, to realize automatic energy saving Good technical support is provided with home energy management.

To achieve the goals above, technical scheme is as follows:

Power-economizing method based on electric appliance effectiveness classification and user power utilization Activity recognition, comprising the following steps: step 1: Provide the objective function of energy saving optimizing.With energy conservation for main syllabus target user power utilization mode dynamic optimization, need in the object time The energy consumption total value in target area is reduced in section as far as possible.It is indicated to pass through the time from past a certain reference time point 0 with [0, T] The research duration of section T, it is believed that T is a longer time period, such as a week or one month.It studies from moment t0Start Energy saving optimizing problem.

Objective function f1 is electricity consumption degree of utility the sum of of the target area R in period [t0, T+t0], is indicated are as follows:

Wherein s (d, t) is operating status of the electric appliance d in moment t, and V (r, t) is that relevant environment of the room r in moment t is joined Number, ut (s (d, t), V (r, t)) are degree of utility of the electric appliance d in moment t, and grade is divided into effective, inefficient use and disutility, most Electricity consumption degree of utility summation in the bigization period [t0, T+t0], to ensure that user obtains in terms of time and temperature two The electricity consumption effectiveness obtained.

Step 2: the basic constraint condition of energy saving optimizing is provided.Basic constraint condition indicates are as follows:

ACTall[t0,T+t0]=ACTall[0,T]

Sall[t0,T+t0]=Sall[0,T]

Vall[t0,T+t0]=Vall[0,T]

Wherein ACTall [0, T] indicates all User Activity state sets in period [0, T], Sall [0, T] table Show that all electric operation state sets in period [0, T], Vall [0, T] indicate all shadows in period [0, T] Ring the parameter state set of user power utilization degree of utility.Think ACTall [0, T], Sall [0, T] and Vall [0, T] are known State, and the ACTall [0, T] in period [t0, T+t0], Sall [0, T] and Vall [0, T] scene state set with [0, T] the scene state set in the period is all identical.

Step 3: user power utilization behavior self-identifying is carried out according to collected electric operation state s (d, t), passes through foundation Hidden Markov model learns historical data, and obtains user power utilization behavior using learning algorithm and decoding algorithm and know Not as a result, obtaining the electricity consumption behavior of user.

Step 4: the active user electricity consumption behavior identified according to step 2, further according to collected electric operation state s (d, t) and environmental parameter V (r, t) obtains degree of utility according to the electric appliance degree of utility division methods based on utility theory Recognition result ut (s (d, t), V (r, t)).

Step 5: control strategy is judged according to degree of utility:

(1) if electric appliance is in disutility grade, electric appliance is closed.

(2) it if electric appliance is in low degree of utility, for cold-storage and thermal storage type electric appliance, adjusts its operating status but does not close electricity Device, control method generally adjust temperature;For non-accumulation energy type electric appliance, electric appliance is closed.

(3) if electric appliance is in effective grade, keep its operating status constant.

Step 6: judging whether next scheduling time section continues energy conservation, if continuing, skips to step 2;If no Continue, then energy saving optimizing terminates.

The invention has the advantages that history power information of this method based on electric power, establishes hidden Markov model to user Electricity consumption behavior carries out real-time automatic identification, can be better understood by electricity consumption behavior and the association probability of electric appliance of user.Further In conjunction with electric appliance rank division method, electric appliance degree of utility is obtained, and corresponding Energy Saving Control plan is formulated according to grade Slightly.This method targetedly can carry out analysis identification to user power utilization behavior, and provide Energy Saving Strategy automatically, fractional energy savings Height, effect is good, to realize that automatic energy saving and home energy management provide good technical support.

Detailed description of the invention

Fig. 1 is 24 hours power curve of winter day actual measurement electric appliance.

Fig. 2 is the indoor temperature change generated in case situation that integral point measures air-conditioning set temperature and actual measurement.

Fig. 3 is the leaving water temperature changing condition that integral point measures water heater set temperature and actual measurement.

Fig. 4 is air conditioning electricity degree of utility classification results.

Fig. 5 is electrical water heater electric degree of utility classification results.

Fig. 6 is desk lamp electricity consumption degree of utility classification results.

Fig. 7 is notebook computer degree of utility classification results.

Fig. 8 is wireless router electricity consumption degree of utility classification results.

Fig. 9 water dispenser electricity consumption degree of utility classification results.

Figure 10 television set electricity consumption degree of utility classification results.

Figure 11 set-top box electricity consumption degree of utility classification results.

Specific embodiment

In order to be easy to understand the technical means, the creative features, the aims and the efficiencies achieved by the present invention, tie below Conjunction is specifically illustrating, and the present invention is further explained.

Referring to Fig. 1~Figure 11, energy saving optimizing problem is studied by taking certain family as an example.The family includes bedroom, parlor, toilet Three rooms, major electrical equipment include air-conditioning, electric heater, television set, set-top box, water dispenser, laptop, without route By device etc..Table 1 provides electric appliance and room distribution situation and its rated power.

The distribution of 1 certain household appliances of table and power situation

24 hours power curve of winter day actual measurement electric appliance are selected, as shown in Fig. 1.Attached drawing 2 is that integral point measures air-conditioning The indoor temperature change generated in case situation of set temperature and actual measurement.Attached drawing 3 is the water temperature out that integral point measures water heater set temperature and actual measurement Spend changing condition.The energy-saving potential of goals research region R is calculated and analyzed.

Step 1: the objective function of energy saving optimizing is provided.

Step 2: the basic constraint condition of energy saving optimizing is provided.

Step 3: user power utilization behavior self-identifying is carried out according to collected electric operation state s (d, t), passes through foundation Hidden Markov model learns historical data, and obtains user power utilization behavior using learning algorithm and decoding algorithm and know Not as a result, obtaining the electricity consumption behavior of user.It is shown in Table 2.

2 user power utilization behavior of table

Serial number Behavior Associated appliance Time 1 Breakfast Electric heater (provides kitchen hot water) 6:30-7:30 2 News Wireless router 7:30-8:00 3 Office Laptop, wireless router, air-conditioning 9:00-11:30 4 Amusement TV, set-top box, air-conditioning 11:30-12:00 5 Lunch Electric heater (provides kitchen hot water) 12:00-13:00 6 Lunch break Air-conditioning 13:00-14:00 7 Office Laptop, wireless router, desk lamp, air-conditioning 14:00-17:00 8 Dinner Electric heater (provides kitchen hot water) 17:30-18:30 9 Amusement TV, set-top box, air-conditioning 20:00-22:00 10 It washes one's face and rinses one's mouth Electric heater, air-conditioning 22:00-22:30 11 Sleep Nothing 22:30-6:30

Step 4: the active user electricity consumption behavior identified according to step 2, further according to collected electric operation state s (d, t) and environmental parameter V (r, t) obtains degree of utility according to the electric appliance degree of utility division methods based on utility theory Recognition result ut (s (d, t), V (r, t)).Attached drawing 4- attached drawing 11 provides each electric appliance degree of utility classification results.

Step 5: control strategy is judged according to degree of utility:

(1) if electric appliance is in disutility grade, electric appliance is closed.

(2) it if electric appliance is in low degree of utility, for cold-storage and thermal storage type electric appliance, adjusts its operating status but does not close electricity Device, control method generally adjust temperature;For non-accumulation energy type electric appliance, electric appliance is closed.

(3) if electric appliance is in effective grade, keep its operating status constant.

Step 6: judging whether next scheduling time section continues energy conservation, if continuing, skips to step 2;If no Continue, then energy saving optimizing terminates.

It obtains electric appliance energy consumption before and after energy conservation and total energy consumption control is as shown in table 3.The total power consumption for 24 hours of the family before energy saving For 26.81kWh, total power consumption is reduced to 16.85kWh, amount of energy saving 3.61kWh after energy conservation, and fractional energy savings reaches 37.2%.

Energy consumption check analysis before and after 3 electric appliance energy-saving of table

Before energy conservation/kWh After energy conservation/kWh Amount of energy saving/kWh Fractional energy savings Amount of energy saving accounting Air-conditioning 14.78 11.17 3.61 24.4% 36.2% Water heater 8.88 3.68 5.2 58.6% 52.2% Desk lamp 0.08 0.04 0.04 50% 0.40% Notebook 0.306 0.304 0.002 0.65% 0.02% Router 0.11 0.07 0.04 36.4% 0.40% Water dispenser 1.07 0.98 0.09 8.41% 0.90% Television set 1.11 0.44 0.67 60.4% 6.73% Set-top box 0.45 0.11 0.34 75.6% 3.41% Summarize 26.81 16.85 9.96 37.2% 100%

Effective grade accounting is as shown in table 4.The efficient overall of electric appliance is improved with grade accounting by 53.9% before energy conservation 89.1% after having arrived energy conservation.The raising of degree of utility means the saving of energy consumption, therefore the user power utilization based on degree of utility Model-based optimization can realize good energy-saving effect on the basis of ensureing user satisfaction.

Effective grade accounting check analysis before and after 4 electric appliance energy-saving of table

The basic principles, main features and advantages of the present invention have been shown and described above.The technology of the industry Personnel are it should be appreciated that the present invention is not limited to the above embodiments, and what is described in the above embodiment and the description is only the present invention Principle, various changes and improvements may be made to the invention without departing from the spirit and scope of the present invention, these variation and Improvement is both fallen in the range of claimed invention.The present invention claims protection scope by appended claims and its Equivalent defines.

Claims (1)

1. the power-economizing method based on electric appliance effectiveness classification and user power utilization Activity recognition, which is characterized in that including following step It is rapid:
Step 1: providing the objective function of energy saving optimizing, is indicated to pass through period T from past a certain reference time point 0 with [0, T] Research duration, research is from moment t0The energy saving optimizing problem of beginning;
Objective function f1 is electricity consumption degree of utility the sum of of the target area R in period [t0, T+t0], is indicated are as follows:
Wherein s (d, t) be electric appliance d moment t operating status, V (r, t) be room r moment t environmental parameter, ut (s (d, T), V (r, t)) it is degree of utility of the electric appliance d in moment t, grade is divided into effective, inefficient use and disutility, maximizes the period Electricity consumption degree of utility summation in [t0, T+t0], to ensure the electricity consumption effect that user obtains in terms of time and temperature two With;
Step 2: providing the basic constraint condition of energy saving optimizing, and basic constraint condition indicates are as follows:
ACTall[t0,T+t0]=ACTall[0,T]
Sall[t0,T+t0]=Sall[0,T]
Vall[t0,T+t0]=Vall[0,T]
Wherein ACTall [0, T] indicates that all User Activity state sets in period [0, T], Sall [0, T] indicate All electric operation state sets in period [0, T], Vall [0, T] indicate the had an impact use in period [0, T] The parameter state set of family electricity consumption degree of utility marks ACTall [0, T], and Sall [0, T] and Vall [0, T] are known shapes State, and the ACTall [0, T] in period [t0, T+t0], Sall [0, T] and Vall [0, T] scene state set and [0, T] Scene state set in period is all identical;
Step 3: user power utilization behavior self-identifying is carried out according to collected electric operation state s (d, t), by establishing hidden horse Er Kefu model, learns historical data, and obtains user power utilization Activity recognition knot using learning algorithm and decoding algorithm Fruit obtains the electricity consumption behavior of user;
Step 4: the active user electricity consumption behavior identified according to step 2, further according to collected electric operation state s (d, t) Degree of utility identification is obtained according to the electric appliance degree of utility division methods based on utility theory with environmental parameter V (r, t) As a result ut (s (d, t), V (r, t));
Step 5: control strategy is judged according to degree of utility;
Step 6: judging whether next scheduling time section continues energy conservation, if continuing, skips to step 2;If not continuing It carries out, then energy saving optimizing terminates;
Degree of utility in the step 5 judges control strategy specific steps are as follows:
If one, electric appliance is in disutility grade, electric appliance is closed;
If two, electric appliance is in low degree of utility, for cold-storage and thermal storage type electric appliance, adjusts its operating status but do not close electric appliance, controls Method processed is adjustment temperature;For non-accumulation energy type electric appliance, electric appliance is closed;
If three, electric appliance is in effective grade, keep its operating status constant.
CN201510581332.8A 2015-09-14 2015-09-14 Power-economizing method based on electric appliance effectiveness classification and user power utilization Activity recognition CN105184408B (en)

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CN105739308B (en) * 2016-02-01 2019-01-08 北方工业大学 Power optimization control method and system applied to temperature control electric appliance
CN107276227B (en) * 2017-07-05 2020-04-21 国网山东省电力公司微山县供电公司 Power dispatching method and device

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103138395A (en) * 2011-12-05 2013-06-05 中国科学院沈阳自动化研究所 Building intelligent power consumption management system
CN103702401A (en) * 2013-12-17 2014-04-02 无锡清华信息科学与技术国家实验室物联网技术中心 User behavior analysis-based energy-saving method for mobile network

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103138395A (en) * 2011-12-05 2013-06-05 中国科学院沈阳自动化研究所 Building intelligent power consumption management system
CN103702401A (en) * 2013-12-17 2014-04-02 无锡清华信息科学与技术国家实验室物联网技术中心 User behavior analysis-based energy-saving method for mobile network

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