CN111524326B - Resident temperature-sensitive load electricity utilization excess alarm method based on historical electricity utilization data - Google Patents
Resident temperature-sensitive load electricity utilization excess alarm method based on historical electricity utilization data Download PDFInfo
- Publication number
- CN111524326B CN111524326B CN202010258112.2A CN202010258112A CN111524326B CN 111524326 B CN111524326 B CN 111524326B CN 202010258112 A CN202010258112 A CN 202010258112A CN 111524326 B CN111524326 B CN 111524326B
- Authority
- CN
- China
- Prior art keywords
- temperature
- sensitive load
- user
- data
- sensitive
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/18—Status alarms
- G08B21/182—Level alarms, e.g. alarms responsive to variables exceeding a threshold
Landscapes
- Business, Economics & Management (AREA)
- Emergency Management (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Alarm Systems (AREA)
- Air Conditioning Control Device (AREA)
Abstract
The invention discloses a resident temperature-sensitive load electricity utilization excess warning method based on historical electricity utilization data, which comprises the following specific steps: for the temperature-sensitive loads such as an air conditioner and a warmer, piecewise linear fitting is carried out according to electricity utilization data of a conventional user and temperature data of the region, then, the quantized temperature-sensitive load operation index is obtained by calculation according to the turning point and the broken line slope, neighborhood comparison is carried out on the temperature-sensitive loads in a target region, and whether the user is in an alarm region or not is judged. The invention realizes demand side information response by adopting a non-control means, can provide information service for users, is beneficial to understanding the energy consumption condition of temperature-sensitive load electrical equipment by resident users, promotes scientific electricity utilization of the user side, further improves the energy utilization efficiency, promotes new energy consumption and realizes sustainable development of energy.
Description
Technical Field
The invention relates to a resident temperature-sensitive load electricity excess warning method based on historical electricity utilization data, and belongs to the technical field of load side resource management.
Background
In recent years, the development of renewable energy has become a global consensus. The root cause of the method comprises: potential energy crisis, rising energy prices, concerns over climate change. However, due to the inherent randomness and uncertainty of renewable energy sources themselves, they cannot be as flexibly scheduled and used as traditional power generation resources. In order to maintain a safe and stable operation of the energy system while achieving a high percentage of renewable energy access, researchers are continually seeking new solutions. The development of low-cost and high-efficiency energy storage equipment and the research of a cooperative control method of load side resources are two main technical routes. In the research for the latter, the types of loads are various. The polymerization control performance of the highly controllable industrial production load is good, the response is fast, but the normalized continuous adjustment is difficult to realize, and the economic production is influenced; the centralized control of the loads of residents such as air conditioners, electric water heaters, dehumidifiers and the like has great excavation potential due to the characteristics of small influence, flexible control mode and the like.
With the rapid development of national economy and the rapid adjustment and upgrade of industrial structures, the intelligent power grid is constructed, and higher requirements for developing new energy, saving energy, reducing emission, improving the running rate of the power grid, improving the quality of power supply service and the like are provided. Meanwhile, with the development of ubiquitous power internet of things, the intelligent terminal is gradually popularized, and the uploaded user power load data is in a massive growth situation, so that a considerable data base and direct regulation and control capability are provided for analyzing the power consumption behaviors of residents.
The temperature-sensitive load electricity utilization excess warning method based on historical electricity utilization data is innovative, can provide quantized temperature-sensitive load operation indexes for user groups, is beneficial to understanding the condition that temperature-sensitive load electrical equipment consumes electric energy at different time intervals by resident users, saves electricity in a targeted manner, and reduces electricity charge expenditure; the intelligent power supply system has a guiding function for the user to develop consciousness and habit of scientific power utilization, ordered power utilization, power saving and intelligent power utilization. In a wide sense, the temperature-sensitive load electricity utilization excess warning method has important practical significance for improving the energy utilization efficiency of China, realizing the sustainable development of energy, building a conservation-oriented society, relieving the pressure of the energy and the like.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the method for alarming the excess electricity consumption of the resident temperature-sensitive load based on the historical electricity consumption data is provided, the intelligent early warning function of the peak electricity price is realized, the user is guided to save the electricity in a targeted manner, and the method has wide social value and scientific research value.
The invention adopts the following technical scheme for solving the technical problems:
a resident temperature-sensitive load electricity excess warning method based on historical electricity utilization data comprises the following steps:
step 1, setting a certain geographical range and a target time range, acquiring power consumption data of each conventional user in the target time range and temperature data of each user in the geographical range for all conventional users in the certain geographical range, taking the temperature data as a horizontal coordinate and the power consumption data as a vertical coordinate, and constructing a two-dimensional point cloud chart;
step 2, dividing the two-dimensional point cloud chart into three parts according to point cloud distribution on the two-dimensional point cloud chart, performing linear fitting on each part respectively to obtain three linear fitting curves, recording the linear fitting curves as a low-temperature side curve, a medium-temperature side curve and a high-temperature side curve, recording the slope of each curve, recording the inflection point of the low-temperature side curve and the medium-temperature side curve as a low-temperature threshold point, and recording the inflection point of the medium-temperature side curve and the high-temperature side curve as a high-temperature threshold point;
step 3, calculating a temperature-sensitive load operation index of each conventional user according to the low-temperature side curve slope, the high-temperature side curve slope, the low-temperature threshold point and the high-temperature threshold point obtained in the step 2;
and 4, carrying out neighborhood comparison on temperature-sensitive load operation indexes of all conventional users in a certain geographical range set in the step 1, setting an alarm threshold value at the same time, and judging whether to send an alarm to the conventional users or not according to the alarm threshold value.
In a preferred embodiment of the present invention, the power consumption data and the temperature data for each day in step 1 correspond to daily load and daily average temperature, respectively.
As a preferred scheme of the present invention, the calculation formula of the temperature-sensitive load operation index of each regular user in step 3 is:
wherein E isti,iIndicating a temperature-sensitive load operation index, c, of the ith regular user1,c2,c3,c4Are all larger than 0, and are all larger than 0,
respectively representing the slope of a high-temperature side curve, the slope of a low-temperature side curve, a high-temperature threshold point and a low-temperature threshold point of the ith conventional user.
As a preferred embodiment of the present invention, the specific process of step 4 is as follows:
setting a certain geographical range to AkSorting the temperature-sensitive load operation indexes of all the conventional users in the geographic range from large to small, numbering the sorted temperature-sensitive load operation indexes from 1, and recording the serial number corresponding to the temperature-sensitive load operation index of the ith conventional user as
The quantile corresponding to the temperature-sensitive load operation index of the ith conventional user is as follows:
wherein, size (A)k) Representing a geographical area AkThe number of all regular users in the system;
setting alarm threshold value as M, when quantile of some conventional user
And when the value is less than M, giving an alarm to the conventional user.
Compared with the prior art, the invention adopting the technical scheme has the following technical effects:
1. according to the invention, through massive resident user load data, through a non-invasive method, no additional equipment is needed, the user can know the temperature-sensitive load using condition with the maximum personal occupied electricity proportion, intelligent reminding is realized through neighborhood comparison of the temperature-sensitive load, the electricity consumption behavior of residents is finely analyzed, and the user behavior is changed. The mode of on-line public numbers or APP can be adopted to carry out alarm and prompt service on the user and help the user to know the electricity utilization information.
2. The invention is beneficial to saving electricity consumption of resident users in a targeted manner and reducing the electricity expense; the intelligent power supply system has a guiding function for the user to develop consciousness and habit of scientific power utilization, ordered power utilization, power saving and intelligent power utilization. In a broad sense, with the construction of the ubiquitous power internet of things and the arrival of the intelligent power utilization era, the application of the temperature-sensitive load power utilization excess warning method based on the historical power utilization data has important promotion effects on improving the power utilization experience of residential users, maintaining the safe and stable operation of a power grid system, improving the energy utilization efficiency of China and the like.
Drawings
FIG. 1 is a flow chart of a method for alarming the excess electricity consumption of the temperature-sensitive load of residents based on historical electricity consumption data.
Fig. 2 is a sample illustration of a typical user1 of a temperature sensitive load according to the present invention.
Fig. 3 is a sample illustration of a typical user2 of a temperature sensitive load according to the present invention.
FIG. 4 is a graph comparing two typical user1 and user2 high temperature zones versus medium temperature zones for temperature sensitive loads of the present invention.
Figure 5 is a graph comparing multi-user temperature-sensitive loads.
Fig. 6 is a schematic diagram of temperature-sensitive load alerting for users of conventional users, temperature-sensitive and non-invasive measuring devices, wherein (a) is the conventional user and (b) is the non-invasive measuring device user.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
For reminding of temperature-sensitive loads (air conditioners/heaters), aiming at conventional users, historical electricity utilization data is used as support, a user temperature-sensitive load model is established by adopting a piecewise linear regression method, characteristics of temperature-sensitive users are analyzed, temperature-sensitive load operation indexes are defined, quantiles where the user indexes are located are found in a target area, and whether alarm is given or not is judged. Aiming at a user installing non-invasive equipment, the identified temperature-sensitive load is directly applied, a user temperature-sensitive load usage model is established, the quantile of the user in a target area is found out, and whether to give an alarm or not is judged.
As shown in fig. 1, a flow chart of a method for alarming temperature-sensitive load electricity excess for residents based on historical electricity consumption data of the present invention includes the following steps:
step 1: and aiming at the conventional user, acquiring the electricity utilization data and the area temperature data of the user in a certain geographical range and a target time range, and performing piecewise linear fitting on the electricity utilization data (Y) and the area temperature data (X). The number of segments is currently set to 3, namely a low temperature segment, a medium temperature segment and a high temperature segment.
The temperature-sensitive load (air conditioner/heater) is the most important and largest component of the electricity consumption of residents, so the method has great significance for analyzing the temperature-sensitive load.
For a conventional user, the use condition of each electrical appliance cannot be observed. But the historical energy consumption data of the user is analyzed finely, and the user still has an opportunity to identify the temperature-sensitive load. The daily load (kWh) and the daily average air temperature (deg.c) are taken as the ordinate and abscissa axes, respectively, and the formed two-dimensional image often has an obvious rule. Two typical user samples based on real data are shown in fig. 2 and fig. 3, respectively. It is easy to find that when the air temperature of the user1 and the user2 is higher than a certain threshold value, the daily electricity quantity obviously increases along with the rise of the air temperature; when the air temperature is lower than a certain threshold, the daily electricity consumption obviously increases along with the decrease of the air temperature. Based on this observation, it is reasonably inferred that the load on the right side of the user1 and user2 images is increasing (e.g., slope K in the graph)3And K5Straight line of (b) is associated with the use of summer temperature-sensitive loads (air conditioning); load trend (e.g., slope K in the graph) on the left side of the user2 image1Indicated by the straight line) is related to the use of a winter temperature-sensitive load (electric heater); and the user1 does not rely on the electric heating equipment for heating in winter or the weather conditions in the current observation range do not trigger the user1 to start the electric heating equipment. Piecewise linear regression can be fitted to the scatter plots of fig. 2 and 3, respectively, to obtain a piecewise curve represented by the line segments in the plots.
Step 2: and (3) recording the slope of the high-temperature side curve, the slope of the medium-temperature side curve and the slope of the low-temperature side curve of the linear fitting curve in the step (1), wherein the inflection point of the high-temperature side curve and the medium-temperature side curve is a high-temperature threshold point, and the inflection point of the low-temperature side curve and the medium-temperature side curve is recorded as a low-temperature threshold point.
Comparing the medium and high temperature zones of user1 and user2, as shown in fig. 4, we can find that user1 is relatively insensitive to temperature and user2 is more likely to activate devices such as air conditioners/heaters. Without loss of generality, we count the slope of the user i high temperature side curve
The slope (if any) of the curve at the mid-temperature side is taken as
The slope of the curve on the low temperature side (if any) is taken
The inflection point of the high-temperature side curve and the medium-temperature side curve is recorded as a high-temperature threshold point
The inflection point of the low-temperature side curve and the medium-temperature side curve is recorded as a low-temperature threshold point
In connection with fig. 5, the following reasoning can be made.
The more sensitive to temperature, the more easily it is for a user to activate a device of the air-conditioning/warmer type to have the following characteristics:
1.
the value of the water-soluble polymer is large,
2.
the value is small and the number of the first and second,
3.
to the left
4.
To the right
And step 3: and (3) calculating a temperature-sensitive load operation index based on the high and low temperature threshold points and the high, medium and low temperature measurement curve slopes obtained in the step (1) and the step (2).
The temperature-sensitive load operation indexes defined for the conventional user i are as follows:
wherein, c1,c2,c3,c4> 0, e.g.
Any term in the absence is replaced with 0.
And 4, step 4: and carrying out neighborhood comparison on the temperature-sensitive load in the target area, identifying the position of the power consumption of the temperature-sensitive load of the user, and judging whether to give an alarm or not according to an alarm threshold set by an administrator.
For regular users, with AkGeographic extent, d0To d0The + N target time is the study range. Calculating the target area A according to the above formulakUsers within range
The temperature-sensitive load operation index. Then, for Eti,iCarry out the positive sequence sorting (from big to small) operation, record the sorting Eti,iIs numbered as
The user is at akGeographic extent, d0To d0The quantile of the temperature-sensitive load operation index in the target time + N is as follows:
user i is at AkGeographic extent, d0To d0The temperature-sensitive load index of the + N target time is lower than that of the area
User, can set
The user is in the state shown in (a) of FIG. 6And the warning area is selected according to the actual situation and is determined by a system administrator.
For a user installed with a non-invasive device, a temperature sensitive load is identified. Only the existing data needs to be processed with similar electricity consumption. With BkGeographic extent, t0To t0The + T target time range is for example. First, a target area BkUsers within range
Respectively calculating the total electric quantity delta E used by the temperature-sensitive load owned by each userts,j：
Then, for Δ Ets,jCarry out positive sequence sorting (from large to small) operation, record sorting delta Ets,jIs numbered as
The user is at BkGeographic extent, t0To t0The quantile of the temperature-sensitive load using electric quantity in the target time + T is as follows:
user j is at BkGeographical range t0To t0+ T target hours Total Power consumption lower than that area
User, can set
The user is in an alarm area as shown in fig. 6 (b), where the value of M is selected according to the actual situation and is decided by the system administrator.
The above embodiments are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modifications made on the basis of the technical scheme according to the technical idea of the present invention fall within the protection scope of the present invention.
Claims (3)
1. A resident temperature-sensitive load electricity excess warning method based on historical electricity utilization data is characterized by comprising the following steps:
step 1, setting a certain geographical range and a target time range, acquiring power consumption data of each conventional user in the target time range and temperature data of each user in the geographical range for all conventional users in the certain geographical range, taking the temperature data as a horizontal coordinate and the power consumption data as a vertical coordinate, and constructing a two-dimensional point cloud chart;
step 2, dividing the two-dimensional point cloud chart into three parts according to point cloud distribution on the two-dimensional point cloud chart, performing linear fitting on each part respectively to obtain three linear fitting curves, recording the linear fitting curves as a low-temperature side curve, a medium-temperature side curve and a high-temperature side curve, recording the slope of each curve, recording the inflection point of the low-temperature side curve and the medium-temperature side curve as a low-temperature threshold point, and recording the inflection point of the medium-temperature side curve and the high-temperature side curve as a high-temperature threshold point;
step 3, calculating a temperature-sensitive load operation index of each conventional user according to the low-temperature side curve slope, the high-temperature side curve slope, the low-temperature threshold point and the high-temperature threshold point obtained in the step 2;
the temperature-sensitive load operation index of each conventional user is calculated by the following formula:
wherein E isti,iIndicating a temperature-sensitive load operation index, c, of the ith regular user1,c2,c3,c4Are all larger than 0, and are all larger than 0,
respectively representing the slope of the curve at the high temperature side and the low temperature side of the ith conventional userA curve slope, a high temperature threshold point, a low temperature threshold point;
and 4, carrying out neighborhood comparison on temperature-sensitive load operation indexes of all conventional users in a certain geographical range set in the step 1, setting an alarm threshold value at the same time, and judging whether to send an alarm to the conventional users or not according to the alarm threshold value.
2. The method for alarming excess electricity consumption of temperature-sensitive load of residents according to claim 1, wherein the electricity consumption data and the temperature data of each day in step 1 correspond to daily load and daily average temperature respectively.
3. The method for warning the excess electricity consumption of the temperature-sensitive load of the residents based on the historical electricity consumption data according to claim 1, wherein the specific process of the step 4 is as follows:
setting a certain geographical range to AkSorting the temperature-sensitive load operation indexes of all the conventional users in the geographic range from large to small, numbering the sorted temperature-sensitive load operation indexes from 1, and recording the serial number corresponding to the temperature-sensitive load operation index of the ith conventional user as
The quantile corresponding to the temperature-sensitive load operation index of the ith conventional user is as follows:
wherein, size (A)k) Representing a geographical area AkThe number of all regular users in the system;
setting alarm threshold value as M, when quantile of some conventional user
And when the value is less than M, giving an alarm to the conventional user.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010258112.2A CN111524326B (en) | 2020-04-03 | 2020-04-03 | Resident temperature-sensitive load electricity utilization excess alarm method based on historical electricity utilization data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010258112.2A CN111524326B (en) | 2020-04-03 | 2020-04-03 | Resident temperature-sensitive load electricity utilization excess alarm method based on historical electricity utilization data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111524326A CN111524326A (en) | 2020-08-11 |
| CN111524326B true CN111524326B (en) | 2022-03-08 |
Family
ID=71910595
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010258112.2A Active CN111524326B (en) | 2020-04-03 | 2020-04-03 | Resident temperature-sensitive load electricity utilization excess alarm method based on historical electricity utilization data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111524326B (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103617565A (en) * | 2013-12-06 | 2014-03-05 | 国网山东省电力公司 | Method for establishing relationship between city micro weather and electric power air conditioner loads |
| CN103971296A (en) * | 2014-05-16 | 2014-08-06 | 国家电网公司 | Power purchase method for mathematic model based on electrical loads and temperature |
| CN105825294A (en) * | 2016-03-10 | 2016-08-03 | 国家电网公司 | Meteorological-factor-based grid power load prediction method and system |
| WO2019063925A1 (en) * | 2017-09-26 | 2019-04-04 | Ineo Hauts-De-France | Device forming an item of personal protective equipment for persons moving in an electrical risk area |
| CN109637067A (en) * | 2019-01-23 | 2019-04-16 | 北京交通大学 | A kind of charging station Electrical Safety monitoring device and method for early warning |
| CN110333402A (en) * | 2019-05-23 | 2019-10-15 | 广西电网有限责任公司 | A kind of user's electric voltage exception cognitive method and system based on edge calculations |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| SE465842B (en) * | 1990-04-17 | 1991-11-04 | Granstroem Konsult Ab T | TEMPERATURE MONITORING ALARM DEVICE FOR A COLD SPACE |
| CN103018684B (en) * | 2012-11-28 | 2015-04-22 | 广东电网公司电力科学研究院 | Intelligent online monitoring and early warning method of AC/DC (Alternating Current/Direct Current) integrated power supply for substation |
| CN103048629B (en) * | 2013-01-24 | 2015-07-22 | 圣邦微电子（北京）股份有限公司 | Method and device for metering lithium battery electric quantity |
| CN103136895B (en) * | 2013-01-25 | 2015-02-25 | 东北大学 | Furnace group energy consumption system and method in fused magnesia smelting process |
| CN103529397B (en) * | 2013-10-24 | 2016-06-22 | 华南师范大学 | A kind of method estimating battery electric quantity and battery electric quantity management system |
| CN103683498B (en) * | 2013-11-26 | 2015-09-02 | 杭州务实科技有限公司 | Power network dispatching system |
-
2020
- 2020-04-03 CN CN202010258112.2A patent/CN111524326B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103617565A (en) * | 2013-12-06 | 2014-03-05 | 国网山东省电力公司 | Method for establishing relationship between city micro weather and electric power air conditioner loads |
| CN103971296A (en) * | 2014-05-16 | 2014-08-06 | 国家电网公司 | Power purchase method for mathematic model based on electrical loads and temperature |
| CN105825294A (en) * | 2016-03-10 | 2016-08-03 | 国家电网公司 | Meteorological-factor-based grid power load prediction method and system |
| WO2019063925A1 (en) * | 2017-09-26 | 2019-04-04 | Ineo Hauts-De-France | Device forming an item of personal protective equipment for persons moving in an electrical risk area |
| CN109637067A (en) * | 2019-01-23 | 2019-04-16 | 北京交通大学 | A kind of charging station Electrical Safety monitoring device and method for early warning |
| CN110333402A (en) * | 2019-05-23 | 2019-10-15 | 广西电网有限责任公司 | A kind of user's electric voltage exception cognitive method and system based on edge calculations |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111524326A (en) | 2020-08-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11782465B2 (en) | Optimization of energy use through model-based simulations | |
| Hao et al. | Aggregate flexibility of thermostatically controlled loads | |
| CN110864414B (en) | Air conditioner power utilization load intelligent control scheduling method based on big data analysis | |
| CN103017290B (en) | Air-conditioning electric energy management method | |
| JP6738801B2 (en) | Energy management system and method | |
| CN202254110U (en) | Air conditioner power control device | |
| Baek et al. | Resident behavior detection model for environment responsive demand response | |
| CN115047151A (en) | Carbon emission monitoring system for building | |
| CN111199065B (en) | Zero-energy-consumption building design method and device and terminal equipment | |
| CN103471178B (en) | Heat supply energy-saving control system and energy-saving control method | |
| Wu et al. | Regional assessment of virtual battery potential from building loads | |
| CN102156918A (en) | Smart grid broadcast system and method | |
| CN114386822A (en) | Weighted cosine similarity-based method for analyzing power consumption behavior abnormity of key personnel | |
| CN111524326B (en) | Resident temperature-sensitive load electricity utilization excess alarm method based on historical electricity utilization data | |
| CN111915107A (en) | Load clustering control method based on dynamic clustering | |
| CN112215472A (en) | Clustering-based electric heating load response control method and device | |
| Gong et al. | Analysis of electricity consumption behavior of air conditioning based on the perspective of power demand response | |
| CN117091242A (en) | Evaluation method, temperature setting method and system for air conditioner temperature control load cluster | |
| CN108090647B (en) | Method for obtaining load operation probability of power consumers and method for obtaining load operation probability of power consumer group | |
| Li et al. | The impacts of temperature on residential electricity consumption in Anhui, China: does the electricity price matter? | |
| CN105184408A (en) | Energy-saving method based on electrical appliance electricity utilization effectiveness grading and user electricity utilization behavior recognition | |
| CN113531641B (en) | Sauna room carbon crystal heating plate management and control system based on big data platform | |
| CN113947255A (en) | Electric water heater short-term load prediction method and system based on Monte Carlo method | |
| CN115051374A (en) | Control method and device for electric heating equipment to participate in electric power peak regulation and storage medium | |
| CN115271168A (en) | Method and device for predicting response potential of electrical load and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |