Methods and systems for power-supply management in smart urban based on internet of things
The embodiments of the present disclosure provide methods and systems for power-supply management in a smart urban based on Internet of things (IoT), performed by an urban power-supply management platform. The method includes obtaining a first power-generation data by a sensor network platform; wherein the first power-generation data is determined by summarizing second power-generation data, and the second power-generation data is residential self-power-generation data; determining a power-supply prediction based on first power-generation data and environmental data; and sending the power-supply prediction to the power-supply company.
1. A method of power-supply management in a smart urban based on Internet of things (IoT), performed by an urban power-supply management platform of a system for power-supply management in the smart urban based on IoT, wherein the system is a part of a processing device or implemented by the processing device; and the system further comprises a sensor network platform and an object platform, wherein
the urban power-supply management platform realize overall planning and coordination of connection and cooperation between various functional platforms; and the urban power-supply management platform gathers information of an operating system of the IoT, and provides perceptual management and control management functions for the operation system of the IoT;
the sensor network platform connects the urban power-supply management platform and the object platform, and plays functions of perceptual information sensing communication and control information sensing communication; and the sensor network platform is configured as a sensor network management server;
the object platform is a functional platform for performing perceptual information generation and control information; the sensor network platform communicates with the object platform, wherein the object platform is configured as a collection terminal and configured to obtain second power-generation data, and the collection terminal includes an intelligent meter or an energy router; wherein the smart meter is configured to collect self-power-generation data from each residential user, and the energy router is configured to collect self-power-generation data from multiple residential users in one or more communities;
the method comprising:
obtaining first power-generation data by the urban power-supply management platform communicating with the sensor network platform, wherein the first power-generation data is determined by summarizing the second power-generation data through network communication between the sensor network platform and the collection terminal, and the second power-generation data is residential self-power-generation data; wherein the second power-generation data is obtained from the collection terminal by the sensor network platform; wherein the first power-generation data is determined based on a process including:
sending, by the urban power-supply management platform, division of power generation modes and distribution information of power generation modes to the sensor network platform; determining, by the sensor network platform, the collection terminal according to the division of power generation modes; sub-dividing, by the urban power-supply management platform, area information according to dividing areas of the sensor network platform, sending a sub-division result to the sensor network platform, and determining, by the sensor network platform, the first power-generation data according to the sub-division result;
determining, by a first prediction model, a power-supply prediction based on the first power-generation data and environmental data; wherein
the first power-generation data includes at least one of first distribution information and second distribution information of the residential self-power-generation data, wherein the first distribution information includes distribution information according to power-generation mode, and the second distribution information includes distribution information according to time; and
the environmental data includes weather forecast data and traffic flow data; wherein the environmental data is obtained from the storage device or through invoking weather API by the urban power-supply management platform; and
transmitting, by the urban power-supply management platform, the power-supply prediction to a power-supply company through the sensor network platform; and
adjusting, by the power-supply company, power supply of different areas based on the power-supply prediction.
2. The method of claim 1 , wherein the obtaining the first power-generation data by the sensor network platform comprises:
obtaining, by the sensor network platform, the second power-generation data from the collection terminal in a staggered manner;
determining, by the sensor network platform, the first power-generation data based on the summary processing of the second power-generation data; and
obtaining, by the sensor network platform, the first power-generation data in the staggered manner; wherein the staggered manner refers to not carrying out different steps or operations at the same time; and the staggered manner include obtaining or uploading the power-generation data of different communities at different time periods.
3. The method of claim 1 , wherein the traffic flow data is used to predict a first power demand; wherein the predicting the first power demand comprises:
dividing an area where the power-supply terminal is located based on a main road of road traffic;
obtaining a traffic flow of each divided area as an environmental feature; and
determining, by a power consumption prediction model, the first power demand in the each divided area based on the environmental feature.
4. A system for power-supply management in a smart urban based on Internet of things (IoT), wherein the system comprises an urban power-supply management platform, a sensor network platform, and an object platform, wherein the system is a part of a processing device or implemented by the processing device; wherein
the urban power-supply management platform realize overall planning and coordination of connection and cooperation between various functional platforms; and the urban power-supply management platform gathers information of an operating system of the IoT, and provides perceptual management and control management functions for the operation system of the IoT;
the sensor network platform connects the urban power-supply management platform and the object platform, and plays functions of perceptual information sensing communication and control information sensing communication; and the sensor network platform is configured as a sensor network management server;
the object platform is a functional platform for performing perceptual information generation and control information; the sensor network platform communicates with the object platform, wherein the object platform is configured as a collection terminal and configured to obtain second power-generation data, and the collection terminal includes an intelligent meter or an energy router; wherein the smart meter is configured to collect self-power-generation data from each residential user, and the energy router is configured to collect self-power-generation data from multiple residential users in one or more communities;
wherein the urban power-supply management platform is configured to perform the following operations including:
obtaining first power-generation data by the urban power-supply management platform communicating with the sensor network platform, wherein the first power-generation data is determined by summarizing the second power-generation data through network communication between the sensor network platform and the collection terminal, and the second power-generation data is residential self-power-generation data; wherein the second power-generation data is obtained from the collection terminal by the sensor network platform; wherein the first power-generation data is determined based on a process including:
sending, by the urban power-supply management platform, division of power generation modes and distribution information of power generation modes to the sensor network platform; determining, by the sensor network platform, the collection terminal according to the division of power generation modes; sub-dividing, by the urban power-supply management platform, area information according to dividing areas of the sensor network platform, sending a sub-division result to the sensor network platform, and determining, by the sensor network platform, the first power-generation data according to the sub-division result;
determining, by a first prediction model, a power-supply prediction based on the first power-generation data and environmental data; wherein
the first power-generation data includes at least one of first distribution information and second distribution information of the residential self-power-generation data, wherein the first distribution information includes distribution information according to power-generation mode, and the second distribution information includes distribution information according to time; and
the environmental data includes weather forecast data and traffic flow data; wherein the environmental data is obtained from the storage device or through invoking weather API by the urban power-supply management platform; and
transmitting, by the urban power-supply management platform, the power-supply prediction to a power-supply company through the sensor network platform; and
adjusting, by the power-supply company, power supply of different areas based on the power-supply prediction.
5. The system of claim 4 , wherein the urban power-supply management platform is further configured to perform the following operations including:
obtaining, by the sensor network platform, the second power-generation data from the collection terminal in a staggered manner;
determining, by the sensor network platform, the first power-generation data based on the summary processing of the second power-generation data; and
obtaining, by the sensor network platform, the first power-generation data in the staggered manner; wherein the staggered manner refers to not carrying out different steps or operations at the same time; and the staggered manner include obtaining or uploading the power-generation data of different communities at different time periods.
6. The system of claim 4 , wherein the urban power-supply management platform is further configured to perform the following operations including:
dividing an area where the power-supply terminal is located based on a main road of road traffic;
obtaining a traffic flow of each divided area as an environmental feature; and
determining, by a power consumption prediction model, the first power demand in the each divided area based on the environmental feature.
7. A computer-readable storage medium storing computer instructions, wherein when reading the computer instructions in the storage medium, a computer executes the method of power-supply management in a smart urban based on the Internet of things (IoT) according to claim 1 .
8. The method of claim 1 , wherein the first prediction model is a machine learning model; wherein the first prediction model is obtained by a training process including:
training an initial first prediction model using a plurality of training samples with labels; wherein the training samples include historical first power-generation data, historical environmental data in a month, a quarter, and a year, the labels of the training samples are historical power supply data, and the labels of the training samples are obtained by manual marking;
updating parameters of the initial first prediction model;
ending training when the initial first prediction model satisfies a preset condition; and
obtaining the first prediction model.
9. The method of claim 3 , wherein the power consumption prediction model is obtained based on training, the training of the power consumption prediction model is performed by the urban power-supply management platform; wherein the training of the power consumption prediction model includes:
obtaining at least one training sample with a label and an initial power consumption prediction model; wherein the at least one training sample includes historical first power-generation data of different divided areas and historical environmental features corresponding to the different divided areas, and the label is historical first power demand for the different divided areas;
the initial power consumption prediction model is a power consumption prediction model without adjusting model parameters or not meeting a training requirement; and a count of the at least one training sample is determined according to accuracy requirements of the power consumption prediction model, actual application scenarios, and other factors;
inputting the at least one training sample into the initial power consumption prediction model;
iteratively updating parameters of the initial power consumption prediction model until meeting a preset condition; wherein the preset condition is a loss function less than a threshold, convergence of the loss function, or a training cycle reaching a threshold; and
obtaining the power consumption prediction model.
10. The system of claim 4 , wherein the first prediction model is a machine learning model; wherein the first prediction model is obtained by a training process including:
training an initial first prediction model using a plurality of training samples with labels; wherein the training samples include historical first power-generation data, historical environmental data in a month, a quarter, and a year, the labels of the training samples are historical power supply data, and the labels of the training samples are obtained by manual marking;
updating parameters of the initial first prediction model;
ending training when the initial first prediction model satisfies a preset condition; and
obtaining the first prediction model.
11. The system of claim 6 , wherein the power consumption prediction model is obtained based on training, the training of the power consumption prediction model is performed by the urban power-supply management platform; wherein the training of the power consumption prediction model includes:
obtaining at least one training sample with a label and an initial power consumption prediction model; wherein the at least one training sample includes historical first power-generation data of different divided areas and historical environmental features corresponding to the different divided areas, and the label is historical first power demand for the different divided areas;
the initial power consumption prediction model is a power consumption prediction model without adjusting model parameters or not meeting a training requirement; and a count of the at least one training sample is determined according to accuracy requirements of the power consumption prediction model, actual application scenarios, and other factors;
inputting the at least one training sample into the initial power consumption prediction model;
iteratively updating parameters of the initial power consumption prediction model until meeting a preset condition; wherein the preset condition is a loss function less than a threshold, convergence of the loss function, or a training cycle reaching a threshold; and
obtaining the power consumption prediction model.