Residential electricity consumption prediction method

Abstract

The invention discloses a residential electricity consumption prediction method, which belongs to the technical field of electricity consumption prediction, and comprises the following steps: S1, obtaining electricity consumption information of a mobile communication base station; s2, according to the electricity consumption information of the mobile communication base station, the number of people using the mobile equipment is estimated, and the flow and change conditions of the number of residents in the coverage area of the mobile communication base station are known; and S3, according to the pre-estimated number of the personnel using the mobile equipment and the personnel flow and change condition in the mobile communication base station area, predicting the change condition of the residential electricity consumption influenced by the personnel flow in the coverage area. According to the method, the resident electricity consumption can be accurately predicted in real time according to the flow and change conditions of people in the coverage area of the base station.

Description

Technical Field

[0001] The invention relates to the technical field of electricity consumption prediction, and in particular to a method for predicting residential electricity consumption. Background Art

[0002] In order to better understand the changing trend of electricity consumption, and thus better adjust electricity consumption and avoid power shortages, it is necessary to predict residential electricity consumption. Traditionally, the flow of people is roughly estimated based on holidays to estimate the possible impact on residential electricity consumption. However, it can only estimate large-scale population flows such as the Spring Festival, and cannot accurately estimate the specific flow volume, and cannot accurately predict the actual impact on residential electricity consumption.

[0003] The patent document with publication number CN 113139672 A discloses a method for predicting residential electricity consumption, including the following steps: determining historical data on residential electricity consumption and total population, and obtaining the predicted population in the target year; predicting the per capita residential electricity consumption in the target year according to the econometric model; and determining and outputting the residential electricity consumption in the target year. The invention can comprehensively and quantitatively analyze the impact on economic and social development and residential electricity consumption, and use the econometric model to obtain the per capita residential electricity consumption results in the target year; at the same time, the total urban / rural population in previous years is analyzed and predicted, and the urban / rural population prediction results in the target year are obtained, and finally the residential electricity consumption prediction results in the target year are obtained. Predicting residential electricity consumption is conducive to reasonable and effective power planning and promotes the safe, economical and reliable operation of the power system. However, the invention predicts the annual residential electricity consumption through the historical data of the total population, and can only obtain the overall prediction data, and cannot accurately predict the residential electricity consumption in a small area.

[0004] The patent document with publication number CN 113780660 A discloses a method for predicting residential electricity consumption, including obtaining monthly historical electricity consumption and photovoltaic power generation data of a preset area; establishing a time series-based electricity consumption prediction model and a photovoltaic power generation prediction model based on the monthly historical electricity consumption and photovoltaic power generation data; predicting the monthly electricity consumption and photovoltaic power generation of residents in the preset area based on the electricity consumption prediction model and the photovoltaic power generation prediction model; and calculating the actual electricity consumption of residents in the preset area based on the predicted monthly electricity consumption and photovoltaic power generation of residents. The technical solution provided by the invention predicts the actual monthly electricity consumption of residents in the next year by establishing an electricity consumption prediction model and a photovoltaic power generation prediction model, thereby effectively predicting the trend of changes in electric energy load within a certain area, and providing a certain decision-making basis for regional power grid planning and construction. However, the invention still predicts residents' electricity consumption through historical data, which has poor timeliness and low prediction accuracy. Summary of the invention

[0005] In view of this, the present invention aims at the deficiencies of the prior art and provides a method for predicting residential electricity consumption, which makes real-time and accurate predictions of residential electricity consumption according to the flow and changes of people in the base station coverage area.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is: a method for predicting residential electricity consumption, comprising the following steps:

[0007] S1: Obtaining power consumption information of mobile communication base stations;

[0008] S2: Based on the power consumption information of mobile communication base stations, estimate the number of people using mobile devices and understand the flow and changes of the number of residents in the area covered by the mobile communication base stations;

[0009] S3: Based on the estimated number of people using mobile devices and the flow and changes of people in the mobile communication base station area, predict the changes in residential electricity consumption in the coverage area due to the flow of people.

[0010] Furthermore, in S1, the power consumption information of the mobile communication base station including the base station type, power, and operation time is obtained from the base station operator or relevant management department.

[0011] Furthermore, in S2, the method for estimating the number of people using mobile devices based on the power consumption information of the mobile communication base station is:

[0012] Step 1: Filter the electricity consumption information that reflects the number of users

[0013] The power consumption of mobile communication base stations includes the power consumption of base station main equipment, base station air conditioners, base station power supply equipment, antennas and other outdoor equipment. The power consumption of base station main equipment is selected;

[0014] Step 2: Determine the factors affecting the power consumption of the base station main equipment

[0015] This includes base station coverage and signal quality: population density and number of mobile devices within base station coverage;

[0016] User behavior: the calling and Internet surfing behaviors of users in the area;

[0017] Efficiency of base station equipment: the energy efficiency ratio of the equipment, that is, the wireless signal strength or data transmission efficiency generated per unit power consumption;

[0018] Step 3: Use statistical and mathematical methods to establish a mathematical model of power consumption of base station main equipment and user behavior;

[0019] Step 4: Substitute historical data into the model for verification and observe the model's prediction accuracy. If there is a large deviation between the prediction result and the actual data, the model needs to be adjusted and optimized;

[0020] Step 5: Based on the current electricity consumption data and model, predict the number of people using mobile devices in the future.

[0021] Furthermore, in S2, the method for understanding the flow and change of the number of residents in the coverage area of ​​the mobile communication base station is:

[0022] (1) Obtain the number and activity of users within the base station coverage area;

[0023] (2) Using GIS technology, the distribution of mobile communication base stations and population distribution data are superimposed and analyzed to understand the population flow and changes in the base station coverage area.

[0024] Furthermore, the population flow and changes in the base station coverage area are combined with the predicted number of people using mobile devices in the future to predict the changes in residents' electricity consumption in the coverage area due to population flow.

[0025] Furthermore, the statistical and mathematical methods are regression analysis, linear regression model or exponential model.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] The present invention predicts the number of people using mobile devices through the changes in base station power consumption of mobile communication base stations, and by understanding the flow and changes in the number of residents in the area covered by the mobile communication base stations, comprehensively predicts the changes in residents' power consumption in the area due to the flow of people, thereby achieving real-time and accurate prediction of residents' power consumption.

[0028] In addition, the present invention screens out the power consumption of base station main equipment that affects the number of people using mobile devices, and uses statistical and mathematical methods to establish a mathematical model of the power consumption of base station main equipment and user behavior, so as to predict the number of people in the base station coverage area in the future, and combine the population flow and changes in the base station coverage area obtained according to GIS technology to accurately predict the changes in residents' power consumption in the coverage area due to the flow of people. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution of the embodiment of the present invention is clearly and completely described below. Obviously, the described embodiment is a part of the embodiment of the present invention, not all the embodiments. Based on the described embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0030] A method for predicting residential electricity consumption comprises the following steps:

[0031] S1: Obtaining power consumption information of mobile communication base stations;

[0032] S2: Based on the power consumption information of mobile communication base stations, estimate the number of people using mobile devices and understand the flow and changes of the number of residents in the area covered by the mobile communication base stations;

[0033] S3: Based on the estimated number of people using mobile devices and the flow and changes of people in the mobile communication base station area, predict the changes in residential electricity consumption in the coverage area due to the flow of people.

[0034] In S1, the power consumption information of the mobile communication base station including the base station type, power, and operation time is obtained from the base station operator or relevant management department.

[0035] In S2, the method for estimating the number of people using mobile devices based on the power consumption information of the mobile communication base station is:

[0036] Step 1: Filter the electricity consumption information that reflects the number of users

[0037] The power consumption of mobile communication base stations includes the power consumption of base station main equipment, base station air conditioners, base station power supply equipment, antennas and other outdoor equipment. The power consumption of base station main equipment is selected;

[0038] Step 2: Determine the factors affecting the power consumption of the base station main equipment

[0039] This includes base station coverage and signal quality: population density and number of mobile devices within base station coverage;

[0040] User behavior: the calling and Internet surfing behaviors of users in the area;

[0041] Efficiency of base station equipment: the energy efficiency ratio of the equipment, that is, the wireless signal strength or data transmission efficiency generated per unit power consumption;

[0042] Step 3: Using statistical and mathematical methods, establish a mathematical model of power consumption of the base station main equipment and user behavior; the statistical and mathematical methods are regression analysis, linear regression model or exponential model;

[0043] Step 4: Substitute historical data into the model for verification and observe the model's prediction accuracy. If there is a large deviation between the prediction result and the actual data, the model needs to be adjusted and optimized;

[0044] Step 5: Based on the current electricity consumption data and model, predict the number of people using mobile devices in the future.

[0045] In S2, the method for understanding the flow and change of the number of residents in the coverage area of ​​the mobile communication base station is:

[0046] (1) Obtain the number and activity of users within the base station coverage area;

[0047] (2) Using GIS technology, the distribution of mobile communication base stations and population distribution data are superimposed and analyzed. Through GIS visualization, we can intuitively understand the population flow and changes in the base station coverage area.

[0048] The population flow and changes in the base station coverage area are combined with the predicted number of people using mobile devices in the future to predict the changes in residents' electricity consumption in the coverage area due to population flow.

[0049] An embodiment of the present invention provides a method for predicting electricity consumption of residents. The method predicts the number of people using mobile devices through changes in base station electricity consumption of mobile communication base stations, and comprehensively predicts changes in electricity consumption of residents in the area covered by the mobile communication base stations due to the flow of people by understanding the flow and changes in the number of residents in the area, thereby achieving real-time and accurate prediction of electricity consumption of residents.

[0050] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for predicting residential electricity consumption, characterized in that: The following steps are involved: S1: Obtaining power consumption information of mobile communication base stations; S2: Based on the power consumption information of mobile communication base stations, estimate the number of people using mobile devices and understand the flow and changes of the number of residents in the area covered by the mobile communication base stations; S3: Based on the estimated number of people using mobile devices and the flow and changes of people in the mobile communication base station area, predict the changes in residential electricity consumption in the coverage area due to the flow of people.

2. The method for predicting residential electricity consumption according to claim 1, characterized in that: In S1, the power consumption information of the mobile communication base station including the base station type, power, and operation time is obtained from the base station operator or relevant management department.

3. The method for predicting residential electricity consumption according to claim 2, characterized in that: In S2, the method for estimating the number of people using mobile devices based on the power consumption information of the mobile communication base station is: Step 1: Filter the electricity consumption information that reflects the number of users The power consumption of mobile communication base stations includes the power consumption of base station main equipment, base station air conditioners, base station power supply equipment, antennas and other outdoor equipment. The power consumption of base station main equipment is selected; Step 2: Determine the factors affecting the power consumption of the base station main equipment This includes base station coverage and signal quality: population density and number of mobile devices within base station coverage; User behavior: the calling and Internet surfing behaviors of users in the area; Efficiency of base station equipment: the energy efficiency ratio of the equipment, that is, the wireless signal strength or data transmission efficiency generated per unit power consumption; Step 3: Use statistical and mathematical methods to establish a mathematical model of power consumption of base station main equipment and user behavior; Step 4: Substitute historical data into the model for verification and observe the model's prediction accuracy. If there is a large deviation between the prediction result and the actual data, the model needs to be adjusted and optimized; Step 5: Based on the current electricity consumption data and model, predict the number of people using mobile devices in the future.

4. The method for predicting residential electricity consumption according to claim 3 is characterized in that: In S2, the method for understanding the flow and change of the number of residents in the coverage area of ​​the mobile communication base station is: (1) Obtain the number and activity of users within the base station coverage area; (2) Using GIS technology, the distribution of mobile communication base stations and population distribution data are superimposed and analyzed to understand the population flow and changes in the base station coverage area.

5. The method for predicting residential electricity consumption according to claim 4, characterized in that: The population flow and changes in the base station coverage area are combined with the predicted number of people using mobile devices in the future to predict the changes in residents' electricity consumption in the coverage area due to population flow.

6. The method for predicting residential electricity consumption according to claim 3, characterized in that: The statistical and mathematical methods are regression analysis, linear regression model or exponential model.