KR20200114073A - Method for matching optimal tou pricing option using consumer persona - Google Patents

Abstract

Acquiring smart metering data based on real-time energy usage measured by the smart meter; Acquiring consumer data including information on consumers through a consumer analysis application; Matching with one of at least one or more personas preset using the smart metering data and the consumer data; And recommending at least one or more of a plurality of different timing plans based on the persona matched in the matching step.

Description

How to match rates for optimal timing using consumer personas {METHOD FOR MATCHING OPTIMAL TOU PRICING OPTION USING CONSUMER PERSONA}

The present invention relates to a method of matching an optimal timed rate plan using a consumer persona. More specifically, the best timed rate plan is recommended to consumers by converting consumer characteristics in terms of electricity consumption into persona, and through this in advance, It relates to a method of improving the accuracy of the electric energy demand prediction of a residential area or apartment complex.

The time-of-use (time-of-use, hereinafter referred to as TOU) rate plan is not a progressive rate currently charged to residential consumers, but a rate plan that induces consumer demand response by applying different rates for each time of the day. Higher rates are charged during the day when electricity consumption is high during the day, and lower rates are charged during the late night or morning hours. The TOU plan is basically a model that can be applied only when a smart meter capable of real-time metering is installed. Based on real-time metering, the amount of electricity used during the time period is measured, and the measured electricity consumption is This is a method in which the total fee is calculated by multiplying the unit price.

The TOU rate plan is already applied to industrial and commercial consumers. Industrial and commercial consumers have an incentive for demand response because electricity consumption is higher than that of residential consumers and the corresponding electricity bill is high, but in the case of residential consumers, it is not possible to strongly give a response incentive due to relatively low electricity consumption and charges. Exists. Therefore, residential consumers are also more incentive to not respond to electricity bills rather than paying attention to electricity bills, and as a result, the uncertainty in the overall electricity demand forecast increases.

In this context, in order to more accurately predict the electricity consumption pattern of residential consumers, other auxiliary information collection methods and incentive designs to elicit consumer reactions are needed.

Accordingly, the technical problem of the present invention was conceived in this respect, and the object of the present invention is to maximize consumer engagement by recommending the optimal TOU rate system to consumers based on data related to behavior patterns and smart metering data of residential consumers. It is to provide an optimal method of matching the rate plan for each time period using consumer persona.

According to an embodiment for realizing the object of the present invention, a method for matching an optimal time-based rate plan using a consumer persona includes: obtaining smart metering data based on real-time energy consumption measured by a smart meter; Acquiring consumer data including information on consumers through a consumer analysis application; Matching with one of at least one or more personas preset using the smart metering data and the consumer data; And recommending at least one or more of a plurality of different time-based rate plans based on the persona matched in the matching step.

In an embodiment of the present invention, the obtaining of the consumer data includes: obtaining static data including at least one of age, occupation, income level, and education level of the consumer through the consumer analysis application; And acquiring dynamic data including at least one of consumer life pattern data or energy preference data through the consumer analysis application, wherein the consumer data may include the static data and the dynamic data. .

In an embodiment of the present invention, the energy preference data may include information on a consumer's response to whether there is a willingness to reduce or stop using electricity in a specific time period corresponding to the plurality of time-based rate plans.

According to this method of matching the best time plan using consumer personas, by setting and introducing consumer personas, it is possible to anonymize personal information and collect personalized data of consumers, and share personal information and neighbors based on persona. Social incentives can be developed to complement the financial incentives based on the TOU rate plan through linking with, and by combining personal data based on persona and smart metering data, it is possible to increase the predictability of electricity usage and responsiveness of residential consumers. There is, and the reward system can be strengthened afterwards.

1 is a conceptual diagram illustrating a method of matching an optimal time-based rate plan using a consumer persona according to an embodiment of the present invention.
FIG. 2 is a conceptual diagram illustrating the persona matching process of FIG. 1 in more detail.

Hereinafter, the present invention will be described in more detail with reference to the accompanying drawings.

1 is a conceptual diagram illustrating a method of matching an optimal time-based rate plan using a consumer persona according to an embodiment of the present invention. FIG. 2 is a conceptual diagram illustrating the persona matching process of FIG. 1 in more detail.

Referring to FIGS. 1 and 2, according to an embodiment of the present invention, a method for matching optimal time periods using a consumer persona obtains smart metering data 11 based on real-time energy consumption measured by a smart meter 10. Step, obtaining consumer data 21 including information about consumers through the consumer analysis application 20, at least one persona 30 preset using the smart metering data 11 and the consumer data 21 , 31, 32), and recommending at least one of a plurality of different time-by-time plans (40, 41, 42) based on the persona matched in the matching step. have.

The consumer's persona may be determined by considering the smart metering data 11 and the consumer data 21 input and acquired by the consumer through a separate application (ie, consumer analysis application 20). In one embodiment, the smart metering data 11 may include real-time energy usage measured through the smart meter 10 installed in each home. For example, the smart metering data 11 may include a data set on real-time energy usage accumulated over a past certain period, and may include information on energy usage by time slot.

The data that can be obtained from the smart meter 10 is real-time energy consumption. In the case of the whole power system, the statistical logarithmic law or inertia acts, and the predicted level of the load decreases to within the average level of error, but if this falls to individual consumers, uncertainty increases, making prediction almost impossible. Although real-time measurement of the smart meter 10 partially complements this aspect, as long as the behavior pattern of the consumer cannot be accurately predicted due to the characteristics of home appliances with strong on-off properties, the smart metering data 11 only predicts the behavior pattern of the consumer. There is a limit that it is difficult to do. In particular, the real-time energy usage pattern collected through the smart meter 10 may change rapidly depending on whether a consumer is out or active. In order to compensate for this limitation, the present invention may further use consumer data 21 to predict a consumer's behavior pattern.

Consumers can voluntarily input their own data through the consumer analysis application 20. The consumer analysis application 20 may be linked to the smart meter 10. The consumer data 21 acquired through the consumer analysis application 20 may be used together with the smart metering data 11 to predict future behavior patterns of consumers. As a result, the smart metering data 11 acquired through the smart meter 10 serves as the consumer's past data, and the consumer data 21 input and acquired through the consumer analysis application 20 corrects past behavior patterns. It can play a role and increase the likelihood of future prediction

By introducing persona, consumers can enjoy the effect of choosing a rate plan that suits them by choosing a character that suits them. In one embodiment, the persona may be more than the TOU plan, but is not limited thereto, and the persona may be less than or equal to the TOU plan.

Referring to FIG. 2, consumer data 21 directly input by a consumer may include at least one of static data 22 and dynamic data 23. The static data 22 may include data that does not change over time. The static data 22 may include demographic data. For example, the static data 22 may include at least one or more of data that do not change according to the passage of time of the day, such as the consumer's age, occupation, income level, and education level. The dynamic data 23 may include data related to the passage of time. The dynamic data 23 may include at least one or more of life pattern data and energy preference data.

Therefore, the step of obtaining the consumer data is specifically the step of obtaining the static data 22 including at least one or more of the consumer's age, occupation, income level, and education level through the consumer analysis application 20, and the consumer analysis application It may include the step of acquiring dynamic data 23 including at least one or more of consumer life pattern data or energy preference data through (20).

The life pattern data may include information on a consumer's life pattern that can be classified by time zone. The energy preference data may include consumer response information regarding a preference for energy use by time slot, whether or not there is a willingness to reduce or stop electricity use at a specific time slot. In other words, consumers can increase the predictability of electricity use by inputting their behavior patterns in advance, and this can be used for public demand response (DR) services that require short-term prediction.

Using the dynamic data 23, it is possible to more accurately predict the use of electrical energy by time of day. In the past, electricity consumption patterns of consumers have been predicted based on static data such as demographic and geopolitical data, but this has a remarkably low accuracy of prediction results due to the characteristics of consumers who may vary in daily life patterns even for the same person. Has a drawback.

Here, the time zone used for the preference survey and the specific time zone used for the survey intention to reduce or stop electricity use may correspond to the time zones included in the plurality of time-by-time rate plans.

The present invention described above has been described with reference to the embodiments shown in the drawings, but these are only exemplary, and those of ordinary skill in the art will appreciate that various modifications and variations of examples are possible therefrom. However, such modifications should be considered to be within the technical protection scope of the present invention. Therefore, the true technical protection scope of the present invention should be determined by the technical spirit of the appended claims.

10: smart meter 11: smart metering data
20: consumer analytics application 21: consumer data
22: static data 23: dynamic data
30~32: Persona 1~N 40~42: 1st~M Time-specific rate plan

Claims (3)

Acquiring smart metering data based on real-time energy usage measured by the smart meter;
Acquiring consumer data including information on consumers through a consumer analysis application;
Matching with one of at least one or more personas preset using the smart metering data and the consumer data; And
And recommending at least one or more of a plurality of different timing plans based on the persona matched in the matching step. The method of claim 1,
The step of obtaining the consumer data,
Obtaining static data including at least one of a consumer's age, occupation, income level, and education level through the consumer analysis application; And
Acquiring dynamic data including at least one of consumer life pattern data or energy preference data through the consumer analysis application; Including,
The consumer data includes the static data and the dynamic data. The method of claim 1, wherein the energy preference data is
It characterized in that it includes the consumer's response information on whether or not the intention to reduce or stop electricity use in a specific time period corresponding to the plurality of time-by-time plans.

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