POWER GENERATION PREDICTION SYSTEM AND A METHOD THEREOF
First Claim
1. A power generation prediction system, applicable to an area where a plurality of devices are installed for power generation with a power generation conversion curve, and the power generation prediction system comprising:
- a first computing device configured to receive first input data, and use a first neural network to generate amount prediction data according to the first input data, wherein the first input data comprises meteorological data and used to determine the amount prediction data, and the amount prediction data is used to determine power generation prediction data;
a second computing device connected to the first computing device and configured to receive the amount prediction data, and use a second neural network to perform an approximation function to calculate the power generation prediction data according to the amount prediction data; and
a third computing device connected to the second computing device, and configured to receive second input data, and use a third neural network to generate a power generation module parameter prediction data according to the second input data, wherein the second input data is used to determine the power generation module parameter prediction data, and the power generation module parameter prediction data is used to correct the power generation prediction data;
wherein the second computing device configured to receives the power generation module parameter prediction data, and uses the second neural network to perform the approximation function to calculate the power generation prediction data according to the amount prediction data and the power generation module parameter prediction data;
wherein when the power generation conversion curve is affected by deterioration or reinstallation of at least one of the plurality of devices in the area, the second neural network is fine-tuned and trained again to update the approximation function thereof.
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Abstract
A power generation prediction system using a first neural network and a second neural network is provided, and the first neural network is connected to the second neural network. The first neural network receives first input data, and generates the amount prediction data according to the first input data. The first input data is used to determine amount prediction data, and the amount prediction data is used to determine power generation prediction data. The second neural network receives the amount prediction data, and calculates the power generation prediction data according to the amount prediction data. When a device in a selected area is deteriorated or reinstalled, the second neural network is fine-tuned and trained again.
21 Citations
12 Claims
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1. A power generation prediction system, applicable to an area where a plurality of devices are installed for power generation with a power generation conversion curve, and the power generation prediction system comprising:
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a first computing device configured to receive first input data, and use a first neural network to generate amount prediction data according to the first input data, wherein the first input data comprises meteorological data and used to determine the amount prediction data, and the amount prediction data is used to determine power generation prediction data; a second computing device connected to the first computing device and configured to receive the amount prediction data, and use a second neural network to perform an approximation function to calculate the power generation prediction data according to the amount prediction data; and a third computing device connected to the second computing device, and configured to receive second input data, and use a third neural network to generate a power generation module parameter prediction data according to the second input data, wherein the second input data is used to determine the power generation module parameter prediction data, and the power generation module parameter prediction data is used to correct the power generation prediction data; wherein the second computing device configured to receives the power generation module parameter prediction data, and uses the second neural network to perform the approximation function to calculate the power generation prediction data according to the amount prediction data and the power generation module parameter prediction data; wherein when the power generation conversion curve is affected by deterioration or reinstallation of at least one of the plurality of devices in the area, the second neural network is fine-tuned and trained again to update the approximation function thereof. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A power generation prediction method, applicable to an area where a plurality of devices are installed for power generation with a power generation conversion curve, and the power generation prediction method comprising:
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processing first input data by using a first neural network to generate amount prediction data, wherein the first input data comprises meteorological data, the first input data is used to determine the amount prediction data, and the amount prediction data is used to determine power generation prediction data; processing the amount prediction data by using a second neural network to perform an approximation function to calculate the power generation prediction data, wherein the second neural network is connected to the first neural network; and processing second input data by using a third neural network to generate a power generation module parameter prediction data, wherein the second input data is used to determine the power generation module parameter prediction data, and the power generation module parameter prediction data is used to correct the power generation prediction data; wherein the amount prediction data and the power generation module parameter prediction data are processed by the second neural network according to the approximation function to calculate the power generation prediction data; wherein when the power generation conversion curve is affected by deterioration or reinstallation of at least one of the plurality of devices in the area, the second neural network is fine-tuned and trained again to update the approximation function thereof. - View Dependent Claims (8, 9, 10, 11, 12)
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Specification