CN115933507B - Intelligent regional power consumption energy saving method and system - Google Patents

Abstract

The present invention provides an intelligent regional electricity energy-saving method and system, the system is used to manage the electricity consumption of users, buildings and public areas in the area, and save electricity, the system includes a real-time data monitoring and acquisition module, a temperature sensor, a microcontroller module, a display module, a relay, a wireless communication module, a cloud computing early warning analysis module, an information collector module, a blockchain information management module, and an alarm module. The method and system provided by the present invention are provided with a cloud computing early warning analysis module having an abnormal detection model, and a temperature sensor that detects the external temperature, monitors the external air temperature in real time, and constructs different abnormal detection conditions that affect the construction of different external temperatures according to the influence of different external temperatures on the power supply and heating, and then performs electric energy saving early warning judgment under different electric energy saving standard requirements, and issues an alarm when the energy saving requirements are exceeded, so as to effectively save electric energy.

Description

An intelligent regional electricity energy-saving method and system

Technical Field

The present invention belongs to the technical field of electric energy monitoring and management, and specifically relates to an intelligent regional electricity energy-saving method and system.

Background technique

The power industry has the characteristics of high investment, high consumption, and high emissions, and has become the largest source of carbon emissions in the global energy field. In the past decade, my country's power generation has continued to grow, and electricity has become an indispensable part of people's lives. While the high power consumption ensures efficient and convenient social production and life, it also produces a series of related problems.

There are more and more types of electrical equipment and the structure of lines is becoming more and more complex. The resulting power quality problems have had varying degrees of impact on multiple parties.

To this end, power quality monitoring systems have been built in many places to collect power quality data. However, most of the current research focuses on disturbance detection. Relatively speaking, there are not many real analyses and predictions of power quality indicators. Therefore, it is of great engineering significance to provide reasonable early warning for steady-state power quality indicators.

In order to respond to energy conservation and emission reduction and effectively improve the quality of electricity management in the power industry, in addition to quickly and accurately obtaining the power parameters of each area, it is also necessary to develop a system and method for power energy conservation management and alarm processing based on the power consumption of the monitored area and the external ambient temperature.

Summary of the invention

In view of the above-mentioned defects, the present invention provides an electric energy saving early warning method and system for performing electric energy saving management and alarm processing according to the power consumption of the monitored and managed area in combination with the external ambient temperature.

The present invention provides the following technical solution: an intelligent regional electricity energy-saving method, comprising the following steps:

1) The microcontroller module controls the data real-time monitoring and acquisition module and the temperature sensor to start. The data real-time monitoring and acquisition module collects the power parameters of users, buildings and public areas in the area, and the temperature sensor collects the outside temperature data;

2) The micro-control module transmits data to the display module for real-time display of power consumption, and sends it to the relay and wireless communication module. The relay is connected to the socket to control the on and off of the live wire, and the control signal received from the micro-control module is combined to realize the on and off of the power supply inside the socket;

3) The wireless communication module transmits the power parameter data to the cloud computing early warning analysis module, which performs data analysis and builds an abnormal detection model based on the received external temperature to calculate the actual power consumption Y _s,n and predicted consumption within a monitoring cycle t. Is the difference less than the threshold? If it is less than the threshold It is the power usage status in normal energy-saving mode. If it exceeds the threshold If the power usage is not in the normal energy-saving mode, the cloud computing early warning analysis module sends an alarm command to the alarm module;

4) The alarm module issues an alarm notification;

5) The blockchain information management module receives the abnormal data analyzed by the cloud computing early warning analysis module, the power usage data of users, buildings and public areas in the area collected by the real-time data monitoring and acquisition module and the temperature sensor, as well as the external ambient temperature, for subsequent historical query and analysis summary when necessary.

Furthermore, the anomaly detection model construction method is as follows:

S1: Take 24 hours in a day as a detection cycle, the cycle is represented by s, that is, t = 0, 1, 2, ..., 23, s ∈ t, each cycle has n periods, and use the consumption of the previous i days within the range of time t for automatic regression, i = 1, 2, ..., p, and the regression of each day forms an order; construct the anomaly detection model Y _s,n for the sth cycle and the nth cycle:

Where, Y is the data point in the consumption time series; p is the order in the autoregression; XT1, XT2 and XT3 are the required power supply brought by the external temperature; α is the anomaly detection calculation coefficient, β is the external temperature influence calculation coefficient; ε _s is the value of white noise;

S2: Construct different anomaly detection conditions according to different external temperature influences;

S3: construct a training data set X = {x ₁ ,x ₂ ,…,x _n } formed under different anomaly detection conditions, construct a detection function m(x _j ; μ, δ) for each element x _j that conforms to the unique variable Gaussian distribution, j = 1, 2,…, 3, and perform detection iterative calculations on the subsequent detected data;

The calculated m(x _j ;μ,δ) and the threshold Compare.

Furthermore, in step S2, different external temperature influences construct different abnormality detection conditions as follows:

The T is the external temperature detected by the temperature sensor;

The XT1 abnormal detection condition represents the external temperature impact situation when the outside temperature is 20 degrees Celsius and no power supply, insulation and heating are required; the XT2 abnormal detection condition represents the external temperature impact situation when the outside temperature is 16 degrees Celsius and power supply, insulation and heating are required; the XT3 abnormal detection condition represents the external temperature impact situation when the outside temperature is 5 degrees Celsius and power supply, insulation and heating are urgently required.

Furthermore, in the step S3, the detection function m(x _j ; μ, δ) is as follows:

in,

The present invention also provides an intelligent regional electricity energy-saving system using the above method, wherein the regional electricity energy-saving system is used to manage the electricity consumption of users, buildings and public areas in the area and save electricity. The system includes a real-time data monitoring and acquisition module, a temperature sensor, a micro-control module, a display module, a relay, a wireless communication module, a cloud computing early warning analysis module, an information collector module, a blockchain information management module, and an alarm module; the real-time data monitoring and acquisition module is used to monitor the power usage data of users, buildings and public areas in the area in real time; the temperature sensor is used to monitor the external environment temperature data in real time; the micro-control module is used to control whether the real-time data monitoring and acquisition module and the temperature sensor are turned on; the wireless communication module is used to transmit the data integrated and collected by the micro-control module to the cloud computing early warning analysis module; the cloud computing early warning analysis module is used to analyze the power data and perform abnormal detection according to the external environment temperature. If the abnormal threshold is exceeded, an instruction is issued to the alarm module, and the alarm module issues an alarm notification; otherwise, data analysis and abnormal detection are continued; the information collector module is used to control the cloud computing early warning analysis module to receive the data of the wireless communication module in stages; the blockchain information management module is used to manage the collected data.

Furthermore, the cloud computing early warning analysis module adopts the Hadoop cloud computing method, introduces a data storage platform and performs data processing.

Furthermore, the cloud computing early warning analysis module includes an early warning classification module, an electric energy trend prediction module, a data query module and a statistical report module.

Furthermore, the alarm module notifies the user of the warning in the form of a text message notification.

Furthermore, the blockchain information management module includes a user management module, a building management module, and an area management module;

The user management module is used to collect and manage the user's personal information such as identity information, electricity usage information, etc.;

The building management module is used to classify and measure the power data of several buildings in the monitored area;

The public area management module is used to classify and measure the public area power data in the monitored area by item.

The beneficial effects of the present invention are:

1. The system and method provided by the present invention have the ability to monitor various indicators of power quality in real time, can conveniently and quickly query relevant data of events, automatically modify information types, and set appropriate threshold values according to differences in different regions to automatically capture disturbances of different power quality indicators; the system can predict the development trend of the distribution network through sensitivity analysis and statistics, and issue maintenance and governance control commands to monitoring stations at all levels.

2. The system and method provided by the present invention mainly include functions such as real-time data monitoring, statistical reports, graded warning, trend prediction, historical data query, etc. Based on this, the system can realize the monitoring and early warning of the power quality trend of the smart distribution network, perform data analysis and evaluation conclusion display on all connected voltage quality monitoring devices, and can add and delete corresponding lines.

3. The system and method provided by the present invention are provided with a cloud computing early warning analysis module that has built an abnormality detection model, and a temperature sensor that detects the external temperature to monitor the external air temperature in real time. Different abnormality detection conditions that affect different external temperatures are constructed according to the influence of different external temperatures on the power supply and heating supply. Then, under different electric energy saving standards, electric energy saving early warning judgments are made, and an alarm is issued when the energy saving requirements are exceeded, thereby effectively saving electric energy.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will be described in more detail below based on embodiments and with reference to the accompanying drawings, wherein:

FIG1 is a flow chart of a method for regional electricity energy saving provided by Embodiment 1 of the present invention;

FIG2 is a schematic diagram of the overall structure of a regional electricity energy-saving system provided by Embodiment 2 of the present invention;

3 is a schematic diagram of the structure of a cloud computing early warning analysis module in a regional electricity energy-saving system provided by Embodiment 3 of the present invention;

Figure 4 is a schematic diagram of the structure of the blockchain information management module in the regional electricity energy-saving system provided in Example 4 of the present invention.

Detailed ways

The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

Example 1

As shown in FIG1 , it is a flow chart of an intelligent regional electricity energy saving method provided in this embodiment, and the method includes the following steps:

1) The microcontroller module controls the data real-time monitoring and acquisition module and the temperature sensor to start. The data real-time monitoring and acquisition module collects the power parameters of users, buildings and public areas in the area, and the temperature sensor collects the outside temperature data;

2) The micro-control module transmits data to the display module for real-time display of power consumption, and sends it to the relay and wireless communication module. The relay is connected to the socket to control the on and off of the live wire, and the control signal received from the micro-control module is combined to realize the on and off of the power supply inside the socket;

3) The wireless communication module transmits the power parameter data to the cloud computing early warning analysis module, which performs data analysis and builds an abnormal detection model based on the received external temperature to calculate the actual power consumption Y _s,n and predicted consumption within a monitoring cycle t. Is the difference less than the threshold? If it is less than the threshold It is the power usage status in normal energy-saving mode. If it exceeds the threshold If the power usage is not in the normal energy-saving mode, the cloud computing early warning analysis module sends an alarm command to the alarm module;

4) The alarm module issues an alarm notification;

5) The blockchain information management module receives the abnormal data analyzed by the cloud computing early warning analysis module, the power usage data of users, buildings and public areas in the area collected by the real-time data monitoring and acquisition module and the temperature sensor, as well as the external ambient temperature, for subsequent historical query and analysis summary when necessary.

The anomaly detection model is constructed as follows:

S1: Take 24 hours in a day as a detection cycle, the cycle is represented by s, that is, t = 0, 1, 2, ..., 23, s ∈ t, each cycle has n periods, and use the consumption of the previous i days within the range of time t for automatic regression, i = 1, 2, ..., p, and the regression of each day forms an order; construct the anomaly detection model Y _s,n of the sth cycle and the nth period:

Where, Y is the data point in the consumption time series; p is the order in the autoregression; XT1, XT2 and XT3 are the required power supply brought by the external temperature; α is the anomaly detection calculation coefficient, β is the external temperature influence calculation coefficient; ε _s is the value of white noise;

S2: Construct different anomaly detection conditions according to different external temperature influences;

S3: construct a training data set X = {x ₁ ,x ₂ ,…,x _n } formed under different anomaly detection conditions, construct a detection function m(x _j ; μ, δ) for each element x _j that conforms to the unique variable Gaussian distribution, j = 1, 2,…, 3, and perform detection iterative calculations on the subsequent detected data;

The calculated m(x _j ;μ,δ) and the threshold Compare.

In step S2, different external temperature influences construct different anomaly detection conditions as follows:

T is the external temperature detected by the temperature sensor;

The XT1 abnormal detection condition represents the external temperature impact situation when the outside temperature is 20 degrees Celsius and no power supply, insulation and heating are required; the XT2 abnormal detection condition represents the external temperature impact situation when the outside temperature is 16 degrees Celsius and power supply, insulation and heating are required; the XT3 abnormal detection condition represents the external temperature impact situation when the outside temperature is 5 degrees Celsius and power supply, insulation and heating are urgently required.

In step S3, the detection function m(x _j ; μ, δ) is as follows:

in,

The system provided by the present invention constructs an abnormality detection model and uses a temperature sensor to detect the external temperature to monitor the external air temperature in real time. According to the influence of different external temperatures on the power supply and heating, different abnormality detection conditions that affect the construction of different external temperatures are constructed. Then, under different electric energy saving standard requirements, electric energy saving early warning judgment is carried out, and an alarm is issued when the energy saving requirements are exceeded, thereby effectively saving electric energy.

Example 2

As shown in FIG2 , an intelligent regional power saving system provided by the present embodiment is provided. The power saving warning system is used to manage the power consumption of users, buildings and public areas in the region and save power. The system includes a data real-time monitoring and acquisition module, a temperature sensor, a micro-control module, a display module, a relay, a wireless communication module, a cloud computing early warning analysis module, an information collector module, a blockchain information management module and an alarm module; the data real-time monitoring and acquisition module is used to monitor the power usage data of users, buildings and public areas in the region in real time; the temperature sensor is used to monitor the external environment temperature data in real time; the micro-control module is used to control whether the data real-time monitoring and acquisition module and the temperature sensor are turned on; the wireless communication module is used to transmit the data collected by the micro-control module to the cloud computing early warning analysis module; the cloud computing early warning analysis module is used to analyze the power data and perform abnormal detection according to the external environment temperature. If the abnormal threshold is exceeded, an instruction is issued to the alarm module, and the alarm module issues an alarm notification; otherwise, data analysis and abnormal detection are continued; the information collector module is used to control the cloud computing early warning analysis module to receive the data of the wireless communication module in stages; the blockchain information management module is used to manage the collected data.

The data real-time monitoring module of the present application includes several current transformers: current transformer 1, current transformer 2, and current transformer n. The microcontroller module uses STM32F103C8T6 as the core controller, the display module uses an LCD data display circuit, and the wireless communication module can be a Wi-Fi wireless data transmission circuit or a 4G mobile data transmission circuit, a 5G mobile data transmission circuit, etc.

The STM32F103C8T6 single-chip microcomputer has strong computing performance and excellent interrupt system response. As a 32-bit embedded ARM processor, it has low cost and low power consumption, and the number of pins is also suitable for the electric energy saving early warning system provided by the present invention.

The current transformer adopts PZEM-004T AC multifunctional communication module and communicates with the main controller via TTL serial port communication. It contains current transformer circuit and A/D conversion and data measurement and calculation functions, which can obtain four electrical parameters: voltage, current, power and electricity, and can maintain data in case of power failure.

The display module selects LCD12864 as the LCD screen to display power usage information and electricity consumption information. Together with the buttons, it builds a good human-computer interaction interface and can present the dynamic information of the circuit system more intuitively.

Example 2

On the basis of Example 1, the cloud computing early warning analysis module adopts the Hadoop cloud computing method, introduces the data storage platform and performs data processing.

Example 3

Based on Example 1, the alarm module notifies the user of an early warning in the form of a text message notification.

As shown in FIG2 , an intelligent regional power saving system provided by the present embodiment is used to manage the power consumption of users, buildings and public areas in the region and save power. The system includes a data real-time monitoring and acquisition module, a temperature sensor, a micro-control module, a display module, a relay, a wireless communication module, a cloud computing early warning analysis module, an information collector module, a blockchain information management module and an alarm module; the data real-time monitoring and acquisition module is used to monitor the power consumption data of users, buildings and public areas in the region in real time; the temperature sensor is used to monitor the external environment temperature data in real time; the micro-control module is used to control whether the data real-time monitoring and acquisition module and the temperature sensor are turned on; the wireless communication module is used to transmit the data collected by the micro-control module to the cloud computing early warning analysis module; the cloud computing early warning analysis module is used to analyze the power data and perform abnormal detection according to the external environment temperature. If the abnormal threshold is exceeded, an instruction is issued to the alarm module, and the alarm module issues an alarm notification; otherwise, data analysis and abnormal detection are continued; the information collector module is used to control the cloud computing early warning analysis module to receive the data of the wireless communication module in stages; the blockchain information management module is used to manage the collected data.

As shown in FIG3 , the cloud computing early warning analysis module includes an early warning classification module, an electric energy trend prediction module, a data query module and a statistical report module.

Example 4

As shown in FIG2-4, an intelligent regional power saving system is provided in the present embodiment. The regional power saving system is used to manage the power consumption of users, buildings and public areas in the region and save power. The system includes a real-time data monitoring and acquisition module, a temperature sensor, a micro-control module, a display module, a relay, a wireless communication module, a cloud computing early warning analysis module, an information collector module, a blockchain information management module, and an alarm module; the real-time data monitoring and acquisition module is used to monitor the power usage data of users, buildings and public areas in the region in real time; the temperature sensor is used to monitor the external environment temperature data in real time; the micro-control module is used to control whether the real-time data monitoring and acquisition module and the temperature sensor are turned on; the wireless communication module is used to transmit the data collected by the micro-control module to the cloud computing early warning analysis module; the cloud computing early warning analysis module is used to analyze the power data and perform abnormal detection according to the external environment temperature. If the abnormal threshold is exceeded, an instruction is issued to the alarm module, and the alarm module issues an alarm notification; otherwise, data analysis and abnormal detection are continued; the information collector module is used to control the cloud computing early warning analysis module to receive data from the wireless communication module in stages; the blockchain information management module is used to manage the collected data.

As shown in FIG3 , the cloud computing early warning analysis module includes an early warning classification module, an electric energy trend prediction module, a data query module and a statistical report module.

As shown in Figure 4, the blockchain information management module includes a user management module, a building management module, and a regional management module;

User management module, used to collect and manage user's personal information such as identity information, electricity usage information, etc.;

The building management module is used to classify and measure the power data of several buildings in the monitored area;

The common area management module is used to classify and measure the common area power data in the monitored area.

Based on the cloud computing early warning analysis module, the electricity consumption of buildings and regions is compared and analyzed, and output in the form of reports, curves, graphics, etc. The system can classify and summarize the total electricity consumption of each user, each building, and each region, and calculate the data results required by the user through algorithms, such as calculating the electricity consumption of a certain type of equipment, a certain user, and a certain region in different time periods such as days, weeks, and months. Through real-time online monitoring, early warning prompts are issued for abnormal situations in the power consumption process, and the sources of waste and hidden dangers in the power consumption process are analyzed. They are uploaded to the monitoring management center through the network. According to the preset conditions in the smart contract, early warnings are issued for objects that exceed the standard of electricity consumption, so as to achieve accurate rectification, optimize the power consumption plan, and improve the utilization rate of electricity.

Example 5

Although the present invention has been described with reference to preferred embodiments, various modifications may be made thereto and parts thereof may be replaced by equivalents without departing from the scope of the present invention. In particular, the various technical features mentioned in the various embodiments may be combined in any manner as long as there is no structural conflict. The present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims (8)

1. An intelligent regional power consumption energy-saving method is characterized by comprising the following steps:

the micro-control module controls the data real-time monitoring and collecting module and the temperature sensor to be started, the data real-time monitoring and collecting module collects electric energy parameters of users, buildings and public areas in the area, and the temperature sensor collects outside air temperature data;

The micro control module transmits data to the display module for displaying the electricity consumption condition of the electric energy in real time, and sends the data to the relay and the wireless communication module, the relay is connected into the power strip to control the on-off of the live wire, and the control signal of the micro control module is received to realize the on-off of the power supply in the jack;

The wireless communication module transmits the electric energy parameter data to the cloud computing early warning analysis module, the cloud computing early warning analysis module performs data analysis, an anomaly detection model is built through the received external temperature, and the actual consumption of the electric energy in the range of one monitoring cycle t is calculated And predicting consumptionWhether the difference is smaller than a threshold valueIf less than the threshold valueThe power use state is the normal energy-saving mode, if the threshold value is exceededIf the power consumption state is not the power consumption state in the normal energy-saving mode, the cloud computing early-warning analysis module sends an alarm instruction to the alarm module;

the method for constructing the abnormality detection model comprises the following steps:

S1: taking 24 hours of a day as a detection cycle, the cycle being represented by s, i.e. t=0, 1,2, …,23, s e t, each cycle having n periods therein, and performing an auto-regression using the consumption of the previous i days in the t time range, i=1, 2, …, p, the regression per day forming a first order; constructing an anomaly detection model for the s-th cycle and the n-th cycle ；

；

Wherein Y is a data point in the elapsed time sequence; p is the order in autoregressive; XT1, XT2, and XT3 are the required power supplies due to external temperature; alpha is an anomaly detection calculation coefficient, and beta is an external temperature influence calculation coefficient; is the value of white noise;

s2: constructing different abnormal detection conditions according to different external temperature influences;

s3: constructing training data sets formed under different abnormality detection conditions Constructing each element conforming to a unique variable gaussian distributionIs a function of the detection of (2)Detecting and iterating the data detected subsequently;

Calculated and obtained Comparing with a threshold value;

The alarm module sends out an alarm notice;

The block chain information management module receives abnormal data obtained by analysis of the cloud computing early warning analysis module, electric energy use data of users, buildings and public areas in the areas where the data are acquired by the data real-time monitoring acquisition module and the temperature sensor, and external environment temperature, and is used for carrying out historical query, analysis and summarization when needed later.

2. The intelligent regional power-saving method according to claim 1, wherein in the step S2, different external temperature influences construct different abnormality detection conditions as follows:

；

；

the T is the external temperature detected by the temperature sensor;

The XT1 abnormal detection condition represents the external temperature influence condition that power supply, heat preservation and heat supply are not needed under the condition that the external temperature is 20 ℃; the XT2 abnormal detection condition represents the external temperature influence condition of power supply, heat preservation and heat supply under the condition that the external temperature is 16 ℃; the abnormal XT3 detection condition represents the external temperature influence condition of power supply, heat preservation and heat supply under the condition that the external temperature is 5 ℃.

3. The intelligent regional power-saving method according to claim 1, wherein in the step S3, the detection function isThe following are provided:

；

Wherein: ，。

4. An intelligent regional electricity-saving system adopting the method according to any one of claims 1-3, wherein the intelligent regional electricity-saving system is used for managing electricity consumption conditions of users, buildings and public areas in the region and saving electricity, and is characterized by comprising a data real-time monitoring and acquisition module, a temperature sensor, a micro control module, a display module, a relay, a wireless communication module, a cloud computing early warning analysis module, an information acquisition module, a blockchain information management module and an alarm module; the data real-time monitoring and collecting module is used for monitoring the electric energy use data of users, buildings and public areas in the area in real time; the temperature sensor is used for monitoring external environment temperature data in real time; the micro control module is used for controlling whether the data real-time monitoring acquisition module and the temperature sensor are started; the wireless communication module is used for transmitting the data integrated and collected by the micro control module to the cloud computing early warning analysis module; the cloud computing early warning analysis module is used for analyzing the electric energy use data, detecting abnormality according to the external environment temperature, and sending an instruction to the alarm module if the abnormality threshold value is exceeded, wherein the alarm module sends an alarm notification; otherwise, continuing to analyze the data and detect the abnormality; the information collector module is used for controlling the cloud computing early warning analysis module to receive the data of the wireless communication module in a staged manner; and the block chain information management module is used for managing the acquired data.

5. The intelligent regional power consumption energy saving system according to claim 4, wherein the cloud computing early warning analysis module adopts a Hadoop cloud computing method, introduces a data storage platform and performs data processing.

6. The intelligent regional power-saving system of claim 4, wherein the cloud computing early-warning analysis module comprises an early-warning classification module, an electric energy trend prediction module, a data query module and a statistics report module.

7. The intelligent regional power-saving system according to claim 4, wherein the alarm module is configured to notify the user of the early warning in the form of a short message notification.

8. The intelligent district power saving system of claim 4 wherein the blockchain information management module comprises a user management module, a building management module, a public district management module;

the user management module is used for collecting and managing personal information such as identity information, electricity utilization information and the like of a user;

The building management module is used for classifying and metering the electric energy data of a plurality of buildings in the monitored area;

the public area management module is used for classifying and metering public area electric energy data in the monitored area.