Power Prediction of a Distributed Photovoltaic System Using Artificial Intelligence Long Short-Term Memory Method

  • 秦 澤華

Student thesis: Doctoral Thesis

Abstract

To ensure the effective supply of solar energy and its quality researchers are looking for better methods to improve the prediction accuracy of a solar energy harvesting system In this study weather information such as solar radiation and temperature together with the experimental results of a distributed solar power harvesting systems were used to train and test by an artificial intelligent algorithm called the long short-term memory (LSTM) method The LSTM model can assign different weighting coefficients to long-term and short-term memory data which is particularly suitable for time-series data forecasting The proposed AI model is able to provide the coming 1 to 10 minutes short-term forecast of the photovoltaic power system The detail of the method and prediction results as well as potential application of the machine learning algorithm will be discussed
Date of Award2020
Original languageEnglish
SupervisorRu-Min Chao (Supervisor)

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