Municipal application method based on solar clean energy

Abstract

The invention discloses a municipal application method based on clean solar energy. The municipal application method comprises the following steps: S1, acquiring municipal power utilization data information; s2, constructing a municipal power consumption demand prediction model according to the municipal power consumption data information obtained in the S1; s3, accessing the constructed municipal power consumption demand prediction model to a cloud platform; s4, accessing a municipal power utilization terminal to the cloud platform; s5, monitoring power consumption data of the terminal in real time and outputting a power consumption demand prediction result; and S6, implementing solar energy supply dynamic allocation according to the electricity demand prediction result. Compared with the prior art, the municipal application method based on the solar clean energy has the advantages that operation and use are convenient, the self-sufficiency rate of municipal electricity is increased, and carbon energy emission is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent municipal energy management applications, and specifically refers to a municipal application method based on solar clean energy. Background Art

[0002] With the intensification of global climate change and the continuous growth of energy demand, the development of clean energy has become the choice of countries around the world. As a clean and renewable energy source, solar energy has great development potential.

[0003] In the municipal field, the extensive application of solar energy can not only reduce greenhouse gas emissions, but also improve the reliability and flexibility of the urban energy system.

[0004] However, traditional solar application methods have problems such as high cost, low efficiency, and limited application scope, which limit their further development in the municipal field. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to overcome the above technical defects and provide a municipal application method based on solar clean energy that is convenient to operate and use, improves the self-sufficiency rate of municipal electricity, and reduces carbon energy emissions.

[0006] To solve the above technical problem, the technical solution provided by the present invention is: a municipal application method based on solar clean energy, including the following steps:

[0007] S1: Obtain municipal electricity data information;

[0008] S2: Construct a municipal electricity demand prediction model according to the municipal electricity data information obtained in S1;

[0009] S3: Connect the constructed municipal electricity demand prediction model to the cloud platform;

[0010] S4: Connect a municipal electricity terminal to the cloud platform;

[0011] S5: Real-time monitor the terminal electricity consumption data and output the electricity demand prediction result;

[0012] S6: Implement dynamic allocation of solar energy supply according to the electricity demand prediction result.

[0013] Preferably, in S1, it includes deploying smart meters to key nodes of the municipal power grid and collecting parameter information including voltage, current, and power;

[0014] Establish a municipal electricity characteristic database according to the collected parameter information, and construct a historical load curve and meteorological correlation data.

[0015] Preferably, the municipal electricity demand prediction model in S2 includes an input layer, a hidden layer, and an output layer.

[0016] Preferably, the cloud platform in S3 further includes access to the monitoring of the photovoltaic module status.

[0017] Preferably, the municipal power consumption terminal includes a cluster of intelligent street lights.

[0018] Preferably, S4 further includes access to a composite energy storage system connected to the photovoltaic module. The composite energy storage system includes a lithium battery, a vanadium redox flow battery, and a phase change heat storage material for short-term energy storage, medium-term energy storage, and long-term energy storage, respectively.

[0019] Preferably, the monitoring of the photovoltaic module status in S4 further includes energy conversion warning to adjust the area to be cleaned based on the power generation efficiency of the photovoltaic array.

[0020] Preferably, the meteorological correlation data includes light intensity, temperature, and wind speed.

[0021] Preferably, the cloud platform in S3 includes visual data output, and the dynamic allocation of solar energy supply in S6 includes daytime scheduling, peak-valley scheduling, and emergency guarantee.

[0022] The advantages of the present invention compared with the prior art are as follows: In the present invention, information is extracted through intelligent meters deployed at key nodes of the municipal power grid, and combined with key meteorological data, etc., to conveniently construct a database to complete the construction of the municipal power consumption demand prediction model. In the present invention, a composite cloud platform performs multi-party and multi-dimensional data composite display, so as to complete the subsequent dynamic allocation of solar energy supply, and achieve the efficient conversion and precise allocation of solar energy resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 is a schematic flow chart of a municipal application method based on solar clean energy. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] The present invention will be further described in detail below with reference to the accompanying drawings.

[0025] Combined with the attached Figure 1 As shown, a municipal application method based on solar clean energy includes the following steps: S1: Obtain municipal power consumption data information; S2: Construct a municipal power consumption demand prediction model according to the municipal power consumption data information obtained in S1; S3: Connect the constructed municipal power consumption demand prediction model to the cloud platform; S4: Connect a municipal power consumption terminal to the cloud platform; S5: Real-time monitor the terminal power consumption data and output the power consumption demand prediction result; S6: Implement dynamic allocation of solar energy supply according to the power consumption demand prediction result.

[0026] In the specific implementation of the present invention, step S1 includes deploying smart meters to key nodes of the municipal power grid to collect parameter information including voltage, current, and power; establishing a municipal electricity consumption feature database based on the collected parameter information, constructing historical load curves and meteorological correlation data, and in step S2, the municipal electricity demand prediction model includes an input layer, a hidden layer, and an output layer, where the municipal electricity consumption terminals include a cluster of smart streetlights;

[0027] Among them, the meteorological correlation data includes light intensity, temperature, and wind speed;

[0028] Among them, the input layer: accesses multi-dimensional feature data (historical load, meteorological parameters, holiday identification), the hidden layer: sets 3 layers of LSTM units (128 nodes per layer) and 2 layers of self-attention mechanisms, and the output layer: generates predicted values of electricity load for the next 24 hours.

[0029] In step S3, the cloud platform also includes accessing the status monitoring of photovoltaic modules. In step S4, it also includes accessing a composite energy storage system connected to the photovoltaic modules. The composite energy storage system includes a lithium battery, a vanadium redox flow battery, and a phase change heat storage material, which are used for short-term energy storage, medium-term energy storage, and long-term energy storage respectively. The status monitoring of photovoltaic modules also includes conversion warning, and adjusts the area to be cleaned based on the power generation efficiency of the photovoltaic array;

[0030] For convenient use, the cloud platform includes visual data output. In step S6, the dynamic allocation of solar energy supply includes daytime scheduling, peak-valley scheduling, and emergency guarantee.

[0031] During actual use:

[0032] Deploy smart meters to key nodes of the municipal power grid (such as substations, distribution rooms, and key energy-consuming units), collect parameter information such as voltage (V), current (A), and power (kW), and the sampling frequency is ≥1 time / minute. Synchronously integrate meteorological station data (light intensity, temperature, wind speed), construct a municipal electricity consumption feature database containing historical load curves (time resolution of 15 minutes) and meteorological correlation data, and adopt an LSTM-Transformer hybrid neural network architecture, including: the input layer: accesses multi-dimensional feature data (historical load, meteorological parameters, holiday identification), the hidden layer: sets 3 layers of LSTM units (128 nodes per layer) and 2 layers of self-attention mechanisms, and the output layer: generates predicted values of electricity load for the next 24 hours.

[0033] Deploy the prediction model to edge computing nodes and upload it to the cloud platform through a 5G network.

[0034] Synchronously access the status monitoring system of photovoltaic modules to obtain in real time: module temperature, power generation efficiency, and fault location.

[0035] Terminal access: It covers the use of intelligent street lamp clusters (single lamp control granularity), traffic signal lights, public building HVAC systems, etc.

[0036] Thereby, real-time monitoring and predictive output are carried out.

[0037] Among them, in the S1 stage, an innovative spatio-temporal alignment algorithm is adopted to solve the problem of the sampling frequency difference between meteorological data and electricity consumption data. Through two-level processing of interpolation and downsampling, the time resolution of meteorological data is unified to 15 minutes, and model adaptive optimization is achieved through the federated learning framework.

[0038] The content not described in detail in this specification belongs to the prior art well-known to those skilled in the art.

[0039] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification only illustrates the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed.

[0040] The scope of protection claimed by the present invention is defined by the appended claims and their equivalents. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the present invention claimed, but merely represents the selected embodiments of the present invention.

[0041] Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0042] It should be noted that: similar reference numerals and letters denote similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0043] In the present invention, unless otherwise clearly defined and limited, the terms "installation", "connection", "connection", "fixation", etc. should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or integrated; it can be directly connected, or indirectly connected through an intermediate medium, and it can be the internal connection of two components or the interaction relationship between two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0044] The above description of the present invention and its implementation manners is not restrictive. What is shown in the drawings is only one of the implementation manners of the present invention, and the actual structure is not limited thereto. In general, if those of ordinary skill in the art are inspired by it and, without departing from the gist of the present invention, design similar structural manners and embodiments to this technical solution without creative efforts, they shall fall within the protection scope of the present invention.

Claims

1. A municipal application method based on solar clean energy, characterized in that: It includes the following steps: S1: Obtain municipal electricity consumption data information; S2: Build a municipal electricity demand prediction model based on the municipal electricity consumption data information obtained in S1; S3: Connect the built municipal electricity demand prediction model to the cloud platform; S4: Connect a municipal electricity terminal to the cloud platform; S5: Monitor the terminal electricity consumption data in real time and output the electricity demand prediction result; S6: Implement dynamic allocation of solar energy supply according to the electricity demand prediction result.

2. The municipal application method based on solar clean energy according to claim 1, wherein: In S1, it includes deploying smart meters to key nodes of the municipal power grid and collecting parameter information including voltage, current, and power; Establish a municipal electricity characteristic database based on the collected parameter information, and build a historical load curve and meteorological correlation data.

3. A municipal application method based on solar clean energy according to claim 1 or 2, characterized in that: In S2, the municipal electricity demand prediction model includes an input layer, a hidden layer, and an output layer.

4. A municipal application method based on solar clean energy according to claim 1, characterized in that: In S3, the cloud platform also includes access to photovoltaic module status monitoring.

5. A municipal application method based on solar clean energy according to claim 1, characterized in that: The municipal electricity terminal includes a cluster of intelligent street lights.

6. The method for municipal application based on solar clean energy according to claim 1, wherein: In S4, it also includes connecting a composite energy storage system connected to the photovoltaic module. The composite energy storage system includes a lithium battery, a vanadium redox flow battery, and a phase change heat storage material, which are used for short-term energy storage, medium-term energy storage, and long-term energy storage respectively.

7. A municipal application method based on solar clean energy according to claim 4, characterized in that: In S4, the photovoltaic module status monitoring also includes energy conversion warning, and adjusts the area to be cleaned based on the power generation efficiency of the photovoltaic array.

8. A municipal application method based on solar clean energy according to claim 2, characterized in that: The meteorological correlation data includes light intensity, temperature, and wind speed.

9. A municipal application method based on solar clean energy according to claim 7, characterized in that: In S3, the cloud platform includes visual data output. In S6, the dynamic allocation of solar energy supply includes daytime scheduling, peak-valley scheduling, and emergency support.

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