Solar equipment remote monitoring system based on Internet of Things

Abstract

The invention discloses a solar equipment remote monitoring system based on the Internet of Things, and relates to the technical field of solar equipment monitoring, and the system comprises a data collection module which is used for collecting the operation data, historical weather data and solar radiation intensity data of solar equipment; according to the invention, operation data, historical weather data and solar radiation intensity data of the solar equipment are acquired by using the data acquisition module, the acquired data are deeply analyzed by the data analysis module, and a relation model among the historical weather data, the solar radiation intensity change trend and the energy output of the solar equipment is established; the prediction module performs high-precision prediction on energy output of the solar equipment in a period of time in the future by using the established relation model, the energy allocation module makes energy allocation and use planning in advance according to a prediction result, and through a reasonable energy allocation scheme, the utilization efficiency of energy is improved, and stable supply of energy is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of solar equipment monitoring, and in particular to a solar equipment remote monitoring system based on the Internet of Things. Background Art

[0002] As the global demand for clean energy continues to grow, solar energy, as a renewable energy source, is being used more and more widely. Solar energy equipment refers to equipment that uses solar energy for power generation, heating and other applications. These equipment have the advantages of being environmentally friendly, renewable, and resource-rich, and are an important development direction in the world's energy field today. In the field of solar energy equipment, remote monitoring systems can realize real-time collection, transmission and analysis of key information such as the equipment's operating status, power generation efficiency, and environmental parameters. In the remote monitoring system of solar energy equipment, the Internet of Things technology provides a stable and reliable network connection and data transmission channel. By deploying various sensors and remote monitoring terminals, the Internet of Things technology can collect real-time operating data of solar energy equipment, such as light intensity, panel temperature, power generation, etc., and transmit this data to the monitoring center or cloud server via wireless or wired means. The emergence of the remote monitoring system of solar energy equipment based on the Internet of Things is the inevitable result of the rapid development of the Internet of Things technology and the continuous maturity of solar energy utilization technology.

[0003] Currently, traditional IoT-based remote monitoring systems for solar energy equipment still have certain drawbacks. The energy output of solar energy equipment is greatly affected by weather conditions. For example, on sunny days, the intensity of solar radiation is high, and the equipment generates a large amount of power. However, on cloudy, rainy, or foggy days, the intensity of solar radiation is low, and the equipment generates a significant decrease in power. Traditional solar energy equipment monitoring systems can only monitor the operating status of the equipment in real time and cannot accurately predict future energy output. This poses a great challenge to energy management departments and users in terms of energy allocation and usage planning. Therefore, it is necessary to propose a remote monitoring system for solar energy equipment based on the Internet of Things to solve the problems in the existing technology. Summary of the Invention

[0004] The purpose of this invention is to make up for the shortcomings of the existing technology and provide a remote monitoring system for solar equipment based on the Internet of Things. It can achieve high-precision prediction of the energy output of solar equipment in the future through in-depth analysis of historical weather data, changing trends in solar radiation intensity and the characteristics of solar equipment itself. It can accurately grasp the power generation potential of solar equipment under different weather conditions and provide strong support for advance energy allocation and usage planning.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a solar energy equipment remote monitoring system based on the Internet of Things, the system comprising:

[0006] Data acquisition module, used to collect operating data of solar equipment, historical weather data and solar radiation intensity data;

[0007] The data analysis module is used to conduct in-depth analysis of the collected data and establish a relationship model between historical weather data, solar radiation intensity change trends and solar equipment energy output;

[0008] The prediction module is used to make high-precision predictions of the energy output of solar equipment in the future by using the relationship model established by the data analysis module;

[0009] Remote monitoring module, used to achieve remote monitoring of solar equipment through Internet of Things technology;

[0010] The energy allocation module is used to make energy allocation and usage plans in advance based on the prediction results of the prediction module.

[0011] Furthermore, the specific execution steps of the data acquisition module include:

[0012] Identify the solar equipment operating data that needs to be collected, including the voltage, current, power and operating temperature parameters of the solar equipment;

[0013] Determine the scope of historical weather data collection, including temperature, humidity, wind speed, rainfall, and air pressure information, and determine the need for collecting solar radiation intensity data;

[0014] For the collection of solar equipment operating data, select voltage sensors, current sensors, power sensors, and temperature sensors that are compatible with the equipment, and install the voltage sensors, current sensors, power sensors, and temperature sensors on the solar equipment, including the output end of the solar panel and the input and output ends of the inverter;

[0015] To collect historical weather data, professional meteorological monitoring equipment, including temperature sensors, humidity sensors, wind speed sensors, rainfall sensors, and air pressure sensors, are installed near the solar equipment installation site. The temperature sensors, humidity sensors, and air pressure sensors are installed in open areas near the solar equipment, while the wind speed sensors and rainfall sensors are installed at higher locations to measure wind speed and rainfall.

[0016] For the collection of solar radiation intensity data, high-precision solar radiation sensors are installed near the solar panels.

[0017] Furthermore, the specific execution steps of the data acquisition module also include:

[0018] According to the working characteristics and data requirements of solar equipment, determine the frequency of collecting solar equipment operation data, collect voltage, current and power data once per second, and collect operating temperature data once per minute;

[0019] For the collection of historical weather data, temperature, humidity, wind speed, rainfall and air pressure data are collected every hour;

[0020] The frequency of collecting solar radiation intensity data is determined based on the energy output changes of solar energy equipment and predicted demand, and solar radiation intensity data is collected once every minute;

[0021] The data acquisition module transmits the collected data to the data analysis module for processing, and the data acquisition module establishes a data storage mechanism to store the collected data in the local memory;

[0022] Real-time monitoring and verification of collected data to remove outliers and noise, and improve data accuracy and reliability;

[0023] The collected data is regularly verified and maintained to check the integrity and accuracy of the data. The data collection module establishes a data backup mechanism to regularly back up the collected data to local storage and cloud servers.

[0024] Furthermore, the specific execution steps of the data analysis module include:

[0025] Obtaining solar equipment operating data, historical weather data, and solar radiation intensity data from the data acquisition module;

[0026] Fill in a small number of missing values ​​and use box plots to detect outliers in the data;

[0027] The historical weather data and solar radiation intensity data are sorted according to time series.

[0028] Furthermore, the specific execution steps of the data analysis module also include:

[0029] Based on the data collated above, the correlation between various factors in historical weather data and solar radiation intensity is analyzed. Variables X and Y are defined to represent historical weather factors and solar radiation intensity, respectively. n is the number of samples. i and Y i are the i-th observation values ​​of variables X and Y respectively, then the calculation formula of the correlation coefficient is: in and are the means of variables X and Y respectively;

[0030] For weather factors with a high correlation with solar radiation intensity, regression analysis is performed, defining solar radiation intensity as the dependent variable Y, weather factors as the independent variable X, and the regression equation as Y = β0 + β1X, where β0 is the intercept and β1 is the slope. The parameters and are estimated by the least squares method to minimize the residual sum of squares. in To predict the value, we take the partial derivatives of SSE with respect to β0 and β1 and set them to zero, and we get the equation group: Solve the system of equations to get estimates of the parameters β0 and β1.

[0031] Furthermore, the specific execution steps of the data analysis module also include:

[0032] Matching solar radiation intensity data with solar equipment operating data;

[0033] Extracting energy output-related features from the operating data of solar equipment, including voltage, current, and power, while also considering the inherent characteristics of the solar equipment, including conversion efficiency, installation angle, and panel material;

[0034] Analyze the correlation between solar radiation intensity and the energy output characteristics of solar energy equipment. Define solar radiation intensity as X and solar energy output characteristics as Y. Use the same method as the correlation analysis between historical weather data and solar radiation intensity to calculate the correlation coefficient.

[0035] Based on the results of the correlation analysis, a mathematical model between solar radiation intensity and solar equipment energy output was established. The solar equipment energy output was defined as the dependent variable Y, and the solar radiation intensity was defined as the independent variable Y. The linear regression equation was Y = α0 + α1X, where α0 was the intercept and α1 was the slope. The parameter estimation method was the same as the linear regression of the above historical weather factors and solar radiation intensity.

[0036] Furthermore, the specific execution steps of the data analysis module also include:

[0037] The relationship model between historical weather data and solar radiation intensity and the relationship model between solar radiation intensity and solar energy equipment energy output are integrated, and the relationship model between historical weather factors and solar radiation intensity is defined as S(X1, X2, ..., X m ), where X1, X2, …, X m is the historical weather factor, the relationship model between solar radiation intensity and solar energy output is E(S), then the comprehensive relationship model is E(S(X1,X2,…,X m ));

[0038] The established relationship model was evaluated using the root mean square error, mean absolute error, and coefficient of determination evaluation indicators;

[0039] Based on the results of model evaluation, the relational model is optimized.

[0040] Furthermore, the specific execution steps of the prediction module include:

[0041] Obtain the established relationship model between historical weather data, solar radiation intensity change trends and solar equipment energy output from the data analysis module;

[0042] Collect current weather data and solar radiation intensity data, and pre-process the current weather data and solar radiation intensity data;

[0043] The pre-processed data is format converted and features extracted to obtain the current solar radiation intensity data and comprehensive weather factor vector;

[0044] The currently collected solar radiation intensity data and the comprehensive weather factor vector are substituted into the relational model to calculate the predicted energy output value.

[0045] Furthermore, when the remote monitoring module remotely monitors the solar equipment, the user can view the operating status and prediction results of the solar equipment anytime and anywhere through mobile phones and computer terminal devices, and can remotely control the equipment at the same time, install sensor nodes on the solar equipment, install gateway devices, and establish a secure communication mechanism. The sensor nodes regularly collect the operating data and environmental parameters of the solar equipment, and send the data to the gateway device. The gateway device receives the data, performs preliminary processing and packaging, and then sends it to the remote monitoring module.

[0046] Furthermore, the energy allocation module obtains the energy output prediction results of the solar equipment in the future period from the prediction module, collects current energy demand data, analyzes historical energy usage data, and formulates a preliminary energy allocation plan based on the energy output prediction results and energy demand data of the solar equipment, taking into account the power generation, energy demand, backup energy, and grid interaction factors of the solar equipment, and optimizes the preliminary plan. According to the formulated energy allocation plan, the solar equipment, backup energy and grid are coordinated and controlled, and the implementation of the plan is monitored and adjusted in real time.

[0047] Compared with the existing technology, the solar equipment remote monitoring system based on the Internet of Things has the following beneficial effects:

[0048] The present invention utilizes a data acquisition module to collect operating data, historical weather data, and solar radiation intensity data of solar equipment. The data analysis module performs in-depth analysis on the collected data to establish a relationship model between historical weather data, solar radiation intensity change trends, and the energy output of solar equipment. The prediction module utilizes the established relationship model to make a high-precision prediction of the energy output of solar equipment in the future. The energy allocation module makes energy allocation and usage plans in advance based on the prediction results. Through a reasonable energy allocation plan, the energy utilization efficiency is improved and a stable energy supply is guaranteed.

[0049] Other advantages, objects and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be learned from the practice of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.

[0051] Figure 1 This is a structural diagram of a solar equipment remote monitoring system based on the Internet of Things. DETAILED DESCRIPTION

[0052] The following is a clear and complete description of the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0053] Example 1

[0054] A solar energy equipment remote monitoring system based on the Internet of Things, the system comprises: a data acquisition module, a data analysis module, a prediction module, a remote monitoring module and an energy allocation module.

[0055] The data acquisition module is used to collect the operating data of solar equipment, historical weather data and solar radiation intensity data. Among them, the operating data of solar equipment includes parameters such as equipment voltage, current, power, operating temperature, etc. These data can be collected in real time through sensors installed on the equipment. Historical weather data covers information such as temperature, humidity, wind speed, rainfall, air pressure, etc., which can be obtained from the database of the meteorological department or collected through meteorological monitoring equipment installed in specific locations. Solar radiation intensity data is collected in real time through high-precision solar radiation sensors. These sensors can accurately measure the solar radiation intensity in different time periods and weather conditions.

[0056] The data analysis module is used to conduct in-depth analysis of the collected data and establish a relationship model between historical weather data, the trend of changes in solar radiation intensity and the energy output of solar equipment. First, the historical weather data is preprocessed to remove outliers and noise. Then, data analysis algorithms such as regression analysis and time series analysis are used to analyze the relationship between historical weather data and solar radiation intensity. Then, combined with the inherent characteristics of solar equipment, such as conversion efficiency, installation angle, panel material, etc., the relationship between solar radiation intensity and the energy output of solar equipment is analyzed. Finally, through comprehensive analysis, a relationship model between historical weather data, the trend of changes in solar radiation intensity and the energy output of solar equipment is established.

[0057] The prediction module uses the relational model established by the data analysis module to provide high-precision predictions of the solar energy output of the solar system over the next period of time. The prediction module can be configured with different prediction timeframes based on user needs, such as the next hour, the next 24 hours, or the next week. To perform the prediction, the module first obtains current weather data and solar radiation intensity data, then inputs this data into the relational model. The model then calculates the predicted energy output of the solar system over the next period of time. The prediction results can be displayed to the user in various forms, such as charts, reports, and SMS notifications. Users can access the solar energy system's power generation potential forecast anytime and anywhere through mobile phones, computers, and other terminal devices, facilitating advance energy allocation and usage planning. For example, if the solar energy system's power generation is predicted to be low over the next period of time, users can activate backup energy sources such as diesel generators and batteries in advance to ensure a stable energy supply. If the solar energy system's power generation is predicted to be high over the next period of time, users can store the excess energy or transmit it to the power grid to improve energy utilization efficiency.

[0058] The remote monitoring module is used to realize remote monitoring of solar equipment through Internet of Things technology. The remote monitoring module consists of sensor nodes, gateways and monitoring centers. The sensor nodes are installed on the solar equipment and are responsible for collecting the equipment's operating data and environmental data, and sending these data to the gateway via wireless transmission. The gateway is responsible for receiving the data sent by the sensor nodes and forwarding these data to the monitoring center. The monitoring center is responsible for processing, analyzing and storing the received data, and providing remote monitoring services to users. Users can log in to the remote monitoring system through mobile phones, computers and other terminal devices to view the operating status and prediction results of the solar equipment. At the same time, users can also remotely control the equipment, such as adjusting the equipment's working mode, setting alarm thresholds, etc. For example, when the user finds that the solar equipment has a fault, the maintenance personnel can be notified in time through the remote monitoring system to avoid the fault from escalating. When the user needs to adjust the working mode of the solar equipment, instructions can be sent through the remote monitoring system to realize remote control of the equipment.

[0059] The energy allocation module is used to make energy allocation and usage plans in advance according to the prediction results of the prediction module. The energy allocation module first analyzes the prediction results to determine the power generation and electricity consumption demand of the solar energy equipment in the future period, and then formulates a reasonable energy allocation plan based on the difference between the power generation and electricity consumption. For example, when it is predicted that the power generation of the solar energy equipment in the future period will be greater than the electricity consumption demand, the excess electricity can be stored or transmitted to the power grid. When it is predicted that the power generation of the solar energy equipment in the future period will be less than the electricity consumption demand, the backup energy can be started or electricity can be purchased from the power grid.

[0060] Example 2

[0061] This embodiment further describes in detail a remote monitoring system for solar energy equipment based on the Internet of Things on the basis of the first embodiment.

[0062] First, the data acquisition module clearly defines the solar equipment operating data that needs to be collected, including the voltage, current, power and operating temperature parameters of the solar equipment. These data can directly reflect the real-time working status and performance of the solar equipment, and determine the collection scope of historical weather data, covering temperature, humidity, wind speed, rainfall and air pressure information. These data are crucial for analyzing the relationship between the energy output of solar equipment and weather conditions, and determine the collection requirements of solar radiation intensity data. Solar radiation intensity is a key factor affecting the power generation of solar equipment and requires high-precision sensors for real-time collection.

[0063] Then, for the collection of solar equipment operation data, select voltage sensors, current sensors, power sensors, and temperature sensors that are compatible with the equipment, ensuring that the sensor accuracy and stability can meet the data collection requirements. Install the voltage sensors, current sensors, power sensors, and temperature sensors on the solar equipment, including the output end of the solar panel and the input and output ends of the inverter, ensuring that the installation location of the sensors will not affect the normal operation of the equipment and can accurately collect the required data. For the collection of historical weather data, obtain data interfaces from the meteorological department and install professional meteorological monitoring equipment near the installation site of the solar equipment, including temperature sensors, humidity sensors, wind speed sensors, rainfall sensors, and air pressure sensors. Select sensors with high reliability and accuracy to ensure that the collected historical weather data is true and reliable. Install the temperature sensor, humidity sensor, and air pressure sensor in an open area near the solar equipment to avoid obstruction and interference. Install the wind speed sensor and rainfall sensor at a higher position to measure wind speed and rainfall. For the collection of solar radiation intensity data, install a high-precision solar radiation sensor near the solar panel. This sensor should be able to accurately measure solar radiation intensity in different time periods and weather conditions and have good stability and durability.

[0064] Then, based on the working characteristics and data requirements of the solar equipment, the frequency of collecting solar equipment operation data is determined. Voltage, current and power data are collected once a second, and operating temperature data is collected once a minute. For the collection of historical weather data, temperature, humidity, wind speed, rainfall and air pressure data are collected once an hour. The frequency of collecting solar radiation intensity data is determined according to the changes in the energy output of the solar equipment and the predicted needs. Solar radiation intensity data is collected once a minute. The data acquisition module uses advanced data transmission technology to transmit the collected data to the data analysis module for processing. Wireless transmission methods such as wireless sensor network technology, Bluetooth technology and ZigBee technology can also be used. Wired transmission methods such as Ethernet and RS485 can also be selected. The data acquisition module establishes a data storage mechanism to store the collected data in local storage such as SD card, hard disk, etc., or upload the data to a cloud server for storage. When storing data, it should be ensured that the data format is standardized and easy to read and analyze. The data acquisition module is equipped with data quality monitoring equipment such as data verifier and data filter to monitor and verify the collected data in real time, remove outliers and noise, and improve the accuracy and reliability of the data.

[0065] Regularly verify and maintain the collected data to check the integrity and accuracy of the data. If any anomalies or errors are found in the data, they should be repaired and adjusted in a timely manner. At the same time, regularly maintain and calibrate the sensors to ensure stable and reliable performance of the sensors. The data acquisition module establishes a data backup mechanism to regularly back up the collected data to local storage and cloud servers to ensure data security and recoverability. If data is lost or damaged, a data recovery plan should be formulated in a timely manner. The data can be restored through backup data or re-collected for supplementation. When recovering data, the integrity and accuracy of the data should be ensured to avoid affecting the data analysis and prediction results.

[0066] The data acquisition module can accurately and reliably collect operating data of solar equipment, historical weather data, and solar radiation intensity data, providing strong data support for subsequent data analysis, prediction, and remote monitoring.

[0067] The data analysis module then obtains the operating data of the solar equipment, historical weather data, and solar radiation intensity data from the data acquisition module to ensure the integrity and accuracy of the data, conducts a preliminary check on missing values ​​and outliers, fills in a small number of missing values, uses box plots to detect outliers in the data, and organizes the historical weather data and solar radiation intensity data in time series to ensure that the time sequence of the data is correct.

[0068] Then, based on the data collated above, the Pearson correlation coefficient was used to analyze the correlation between various factors in the historical weather data (temperature, humidity, wind speed, rainfall, air pressure, etc.) and the solar radiation intensity. The variables X and Y were defined to represent the historical weather factors and solar radiation intensity respectively, n is the number of samples, X i and y i are the i-th observation values ​​of variables X and Y respectively, then the calculation formula of the correlation coefficient is: in and are the means of variables X and Y respectively. For weather factors with a high correlation with solar radiation intensity, regression analysis is performed. Solar radiation intensity is defined as the dependent variable Y, weather factors as the independent variable X, and the regression equation is Y = β0 + β1X, where β0 is the intercept and β1 is the slope. The parameters and are estimated by the least squares method to minimize the residual sum of squares. in To predict the value, we take the partial derivatives of SSE with respect to β0 and β1 and set them to zero, and we get the equation group: Solve the system of equations to get estimates of the parameters β0 and β1.

[0069] Then, the solar radiation intensity data is matched with the operating data of the solar energy equipment to ensure that the data corresponds to the same time period and the same equipment. Features related to energy output, including voltage, current, and power, are extracted from the operating data of the solar energy equipment. At the same time, the inherent characteristics of the solar energy equipment, including conversion efficiency, installation angle, and panel material, are considered to analyze the correlation between solar radiation intensity and the energy output characteristics of the solar energy equipment. Scatter plots and correlation coefficients can be used to determine the relationship between solar radiation intensity and energy output. The solar radiation intensity is defined as X and the energy output characteristics of the solar energy equipment are defined as Y. The correlation coefficient is calculated using the same method as the correlation analysis of the historical weather data and solar radiation intensity mentioned above. Based on the results of the correlation analysis, a mathematical model between solar radiation intensity and solar energy output is established, and a prediction model is established. Taking linear regression as an example, the energy output of the solar energy equipment is defined as the dependent variable Y, and the solar radiation intensity is defined as the independent variable X. The linear regression equation is Y=α0+α1X, where α0 is the intercept and α1 is the slope. The parameter estimation method is the same as the linear regression of the historical weather factors and solar radiation intensity mentioned above.

[0070] Then the relationship model between historical weather data and solar radiation intensity and the relationship model between solar radiation intensity and solar energy equipment energy output are integrated, and the relationship model between historical weather factors and solar radiation intensity is defined as S(X1,X2,...,X m ), where X1, X2, …, X m is the historical weather factor, the relationship model between solar radiation intensity and solar energy output is E(S), then the comprehensive relationship model is E(S(X1,X2,…,X m )), using root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R 2 ) is used as the evaluation index to evaluate the established relationship model. The root mean square error (RMSE) calculation formula is: where Y i is the actual value, is the predicted value, n is the number of samples, and the mean absolute error (MAE) is calculated as: Coefficient of determination (R 2 ) is calculated as: in is the mean of the actual values. Based on the results of model evaluation, the relational model can be optimized. The performance of the model can be improved by adjusting the model parameters, selecting different algorithms, increasing the amount of data, and other methods.

[0071] The prediction module obtains the established historical weather data, the relationship model between the trend of solar radiation intensity changes and the energy output of solar equipment from the data analysis module, collects the current weather data and solar radiation intensity data, and can obtain them in real time through the data acquisition module.

[0072] Preprocess the current weather data and solar radiation intensity data, including outlier removal and standardization. Outliers can be detected by comparing with historical data or using statistical methods, and then appropriately corrected or deleted. Standardization transforms the data into a standard normal distribution with a mean of 0 and a variance of 1, improving model performance and stability.

[0073] The preprocessed data is formatted and features extracted, and the processed input data is input into the selected prediction model to perform energy output prediction. During the prediction process, parallel computing or distributed computing technology can be used to improve the efficiency and speed of the prediction. In particular, for large-scale solar equipment systems or scenarios that require real-time prediction, parallel computing can significantly shorten the prediction time.

[0074] When the remote monitoring module remotely monitors solar equipment, users can view the operating status and prediction results of solar equipment anytime and anywhere through mobile phones and computer terminal devices, and can remotely control the equipment. Sensor nodes and gateway devices are installed on the solar equipment to establish a secure communication mechanism. The sensor nodes regularly collect the operating data and environmental parameters of the solar equipment and send the data to the gateway device. The gateway device receives the data for preliminary processing and packaging and then sends it to the remote monitoring module. The remote monitoring module can realize remote monitoring of solar equipment and provide users with real-time and accurate equipment operating status and environmental parameter information, which is convenient for users to manage and maintain equipment. At the same time, the remote monitoring module can also improve the operating efficiency and reliability of solar equipment, reduce equipment maintenance costs and failure rates, and provide strong support for the widespread application of solar equipment.

[0075] Finally, the energy allocation module obtains the energy output forecast results of the solar equipment in the future from the prediction module, collects the current energy demand data, analyzes the historical energy usage data, and formulates a preliminary energy allocation plan based on the energy output forecast results and energy demand data of the solar equipment, taking into account the power generation, energy demand, backup energy, and grid interaction factors of the solar equipment, and optimizes the preliminary plan. According to the formulated energy allocation plan, the solar equipment, backup energy and grid are coordinated and controlled, and the implementation of the plan is monitored and adjusted in real time. The energy allocation module can formulate a reasonable energy allocation plan based on the energy output forecast results and energy demand data of the solar equipment, and monitor and adjust the implementation of the plan in real time to improve energy utilization efficiency and reduce energy costs, and ensure a stable supply of energy.

[0076] In the above embodiment, the data acquisition module is used to collect the operating data, historical weather data and solar radiation intensity data of the solar equipment. The data analysis module performs in-depth analysis on the collected data, establishes a relationship model between the historical weather data, the trend of changes in solar radiation intensity and the energy output of the solar equipment, and makes a high-precision prediction of the energy output of the solar equipment in the future.

[0077] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and scope of the same elements of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

Claims

1. A solar energy equipment remote monitoring system based on the Internet of Things, characterized in that: The system includes: Data collection module, used to collect operating data of solar equipment, historical weather data and solar radiation intensity data; The data analysis module is used to conduct in-depth analysis of the collected data and establish a relationship model between historical weather data, solar radiation intensity change trends and solar energy equipment energy output; A prediction module is used to make a high-precision prediction of the energy output of solar energy equipment in the future by using the relationship model established by the data analysis module; Remote monitoring module, used to realize remote monitoring of solar equipment through Internet of Things technology; The energy allocation module is used to make energy allocation and usage plans in advance according to the prediction results of the prediction module.

2. According to the Internet of Things-based solar energy equipment remote monitoring system of claim 1, it is characterized in that: The specific execution steps of the data acquisition module include: Specify the solar equipment operating data that needs to be collected, including the voltage, current, power and operating temperature parameters of the solar equipment; Determine the scope of historical weather data collection, including temperature, humidity, wind speed, rainfall and air pressure information, and determine the need for collecting solar radiation intensity data; For the collection of solar equipment operation data, select voltage sensors, current sensors, power sensors and temperature sensors that are compatible with the equipment, and install the voltage sensors, current sensors, power sensors and temperature sensors on the solar equipment, including the output end of the solar panel, the input end and the output end of the inverter; For the collection of historical weather data, professional meteorological monitoring equipment including temperature sensors, humidity sensors, wind speed sensors, rainfall sensors and air pressure sensors are installed near the installation site of the solar energy equipment. The temperature sensors, humidity sensors and air pressure sensors are installed in open areas near the solar energy equipment, and the wind speed sensors and rainfall sensors are installed at higher locations to measure wind speed and rainfall; For the collection of solar radiation intensity data, high-precision solar radiation sensors are installed near the solar panels.

3. According to the Internet of Things-based solar energy equipment remote monitoring system of claim 2, it is characterized in that: The specific execution steps of the data acquisition module also include: According to the working characteristics and data requirements of solar energy equipment, determine the frequency of collecting solar energy equipment operation data, collect voltage, current and power data once a second, and collect working temperature data once a minute; For the collection of historical weather data, temperature, humidity, wind speed, rainfall and air pressure data are collected every hour; The frequency of collecting solar radiation intensity data is determined according to the energy output changes of solar energy equipment and predicted demand, and solar radiation intensity data is collected once every minute; The data acquisition module transmits the collected data to the data analysis module for processing, and the data acquisition module establishes a data storage mechanism to store the collected data in the local memory; Monitor and verify the collected data in real time, remove outliers and noise, and improve the accuracy and reliability of the data; The collected data is verified and maintained regularly to check the integrity and accuracy of the data. The data collection module establishes a data backup mechanism to regularly back up the collected data to local storage and cloud servers.

4. According to the Internet of Things-based solar energy equipment remote monitoring system of claim 1, it is characterized in that: The specific execution steps of the data analysis module include: Acquire the operation data of solar energy equipment, historical weather data and solar radiation intensity data from the data acquisition module; Fill in a small number of missing values ​​and use box plots to detect outliers in the data; The historical weather data and solar radiation intensity data are sorted according to time series.

5. The solar energy equipment remote monitoring system based on the Internet of Things according to claim 4 is characterized in that: The specific execution steps of the data analysis module also include: Based on the above data, the correlation between various factors in the historical weather data and the solar radiation intensity is analyzed. The variables X and Y are defined to represent the historical weather factors and solar radiation intensity respectively. n is the number of samples. i and Y i are the i-th observation values ​​of variables X and Y respectively, then the calculation formula of the correlation coefficient is: in and are the means of variables X and Y respectively; For weather factors with a high correlation with solar radiation intensity, regression analysis is performed, defining solar radiation intensity as the dependent variable Y, weather factors as the independent variable X, and the regression equation as Y = β0 + β1X, where β0 is the intercept and β1 is the slope. The parameters and are estimated by the least squares method to minimize the residual sum of squares. in To predict the value, we take the partial derivatives of SSE with respect to β0 and β1 and set them equal to zero, and we get the following equations: Solving the system of equations yields estimates of the parameters β0 and β1.

6. The solar energy equipment remote monitoring system based on the Internet of Things according to claim 5 is characterized in that: The specific execution steps of the data analysis module also include: Matching solar radiation intensity data with solar equipment operating data; Extract features related to energy output from the operating data of solar equipment, including voltage, current and power, while considering the characteristics of the solar equipment itself, including conversion efficiency, installation angle and panel material; Analyze the correlation between solar radiation intensity and solar energy equipment energy output characteristics, define solar radiation intensity as X, solar energy equipment energy output characteristics as Y, and use the same method as the above historical weather data and solar radiation intensity correlation analysis to calculate the correlation coefficient; According to the results of correlation analysis, a mathematical model between solar radiation intensity and solar energy equipment energy output is established, and the solar energy equipment energy output is defined as the dependent variable Y, and the solar radiation intensity is defined as the independent variable Y. The linear regression equation is Y=α0+α1X, where α0 is the intercept and α1 is the slope. The parameter estimation method is the same as the linear regression of the above historical weather factors and solar radiation intensity.

7. A solar energy equipment remote monitoring system based on the Internet of Things according to claim 6, characterized in that: The specific execution steps of the data analysis module also include: The relationship model between historical weather data and solar radiation intensity and the relationship model between solar radiation intensity and solar energy equipment energy output are integrated, and the relationship model between historical weather factors and solar radiation intensity is defined as S(X1,X2,…,X m ), where X1, X2, …, X m is the historical weather factor, the relationship model between solar radiation intensity and solar energy output is E(S), then the comprehensive relationship model is E(S(X1,X2,…,X m )); The established relationship model was evaluated using the evaluation indicators of root mean square error, mean absolute error, and coefficient of determination; According to the results of model evaluation, the relationship model is optimized.

8. The solar energy equipment remote monitoring system based on the Internet of Things according to claim 1 is characterized in that: The specific execution steps of the prediction module include: Obtain the established relationship model between historical weather data, solar radiation intensity change trend and solar energy equipment energy output from the data analysis module; Collect current weather data and solar radiation intensity data, and pre-process the current weather data and solar radiation intensity data; The preprocessed data is format converted and features extracted to obtain the current solar radiation intensity data and comprehensive weather factor vector; Substitute the currently collected solar radiation intensity data and the comprehensive weather factor vector into the relationship model to calculate the predicted energy output value.

9. The solar energy equipment remote monitoring system based on the Internet of Things according to claim 1 is characterized in that: When the remote monitoring module remotely monitors the solar equipment, the user can view the operating status and prediction results of the solar equipment anytime and anywhere through mobile phones and computer terminal devices, and can remotely control the equipment at the same time, install sensor nodes on the solar equipment, install gateway devices, and establish a secure communication mechanism. The sensor nodes regularly collect the operating data and environmental parameters of the solar equipment, and send the data to the gateway device. The gateway device receives the data, performs preliminary processing and packaging, and then sends it to the remote monitoring module.

10. The solar energy equipment remote monitoring system based on the Internet of Things according to claim 1, characterized in that: The energy allocation module obtains the energy output prediction results of the solar energy equipment in the future from the prediction module, collects the current energy demand data, analyzes the historical energy usage data, and formulates a preliminary energy allocation plan based on the energy output prediction results and energy demand data of the solar energy equipment, taking into account the power generation, energy demand, backup energy, and grid interaction factors of the solar energy equipment, and optimizes the preliminary plan. According to the formulated energy allocation plan, the solar energy equipment, backup energy and grid are coordinated and controlled, and the implementation of the plan is monitored and adjusted in real time.

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