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Title: | Remaining useful life prediction of Solar Panels in the Microgrid using combination of CNN and LSTM approaches | Authors: | Thai Hoang Tam | Keywords: | PV system;Blocking diode;Bypass diode;Lifetime consumption;CNN;LSTM;SSO | Issue Date: | 2017 | Abstract: | The thesis shows some short-term prediction models using a combination of Convolutional Neural Network (CNN) and long short-term memory (LSTM) network with the SSO technique for the remaining useful life (RUL) or lifetime consumption (LC) of a PV system in a microgrid. This thesis also covers fundamental knowledge of a PV system to understand which factors, and components can affect the lifespan of a PV system. The main goal of the thesis is to try different machine-learning methods for the short-term (one day ahead) prediction model and suggest the best model for each type of dataset |
URI(1): | https://epub.vgu.edu.vn/handle/dlibvgu/1724 | Rights: | Attribution-NonCommercial 4.0 International |
Appears in Collections: | Computer Science (CS) |
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Remaining Useful Life Prediction of Solar Panelsin the Microgrid using Combination of CNN and LSTM Approaches.pdf | 1.19 MB | Adobe PDF |
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