Interval forecasting of solar PV power generation

Solar photovoltaic power generation is more and more popular in recent year. However, when solar PV power stations connect to the grid, it is important to predict the solar PV power generation for the stability of the whole system. Based on such situation, this dissertation provides and validates a...

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Main Author: Zhao, Yunan
Other Authors: Xu Yan
Format: Thesis-Master by Coursework
Language:English
Published: Nanyang Technological University 2020
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Online Access:https://hdl.handle.net/10356/144163
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1441632023-07-04T17:00:22Z Interval forecasting of solar PV power generation Zhao, Yunan Xu Yan School of Electrical and Electronic Engineering xuyan@ntu.edu.sg Engineering::Electrical and electronic engineering::Electric power::Production, transmission and distribution Solar photovoltaic power generation is more and more popular in recent year. However, when solar PV power stations connect to the grid, it is important to predict the solar PV power generation for the stability of the whole system. Based on such situation, this dissertation provides and validates a method by using a neural network algorithm which is an LSTM model to make prediction, and proposes some observation results from the simulation case. Briefly, in this case study, it is shown that different time steps can lead to different accuracy of the results, so it is important to choose suitable time steps to forecast. In addition, the results of this prediction model are also related to the correlation of input features, and it is necessary to select the input features when training the model in order to make it more effective. Master of Science (Power Engineering) 2020-10-19T05:48:03Z 2020-10-19T05:48:03Z 2020 Thesis-Master by Coursework https://hdl.handle.net/10356/144163 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering::Electric power::Production, transmission and distribution
spellingShingle Engineering::Electrical and electronic engineering::Electric power::Production, transmission and distribution
Zhao, Yunan
Interval forecasting of solar PV power generation
description Solar photovoltaic power generation is more and more popular in recent year. However, when solar PV power stations connect to the grid, it is important to predict the solar PV power generation for the stability of the whole system. Based on such situation, this dissertation provides and validates a method by using a neural network algorithm which is an LSTM model to make prediction, and proposes some observation results from the simulation case. Briefly, in this case study, it is shown that different time steps can lead to different accuracy of the results, so it is important to choose suitable time steps to forecast. In addition, the results of this prediction model are also related to the correlation of input features, and it is necessary to select the input features when training the model in order to make it more effective.
author2 Xu Yan
author_facet Xu Yan
Zhao, Yunan
format Thesis-Master by Coursework
author Zhao, Yunan
author_sort Zhao, Yunan
title Interval forecasting of solar PV power generation
title_short Interval forecasting of solar PV power generation
title_full Interval forecasting of solar PV power generation
title_fullStr Interval forecasting of solar PV power generation
title_full_unstemmed Interval forecasting of solar PV power generation
title_sort interval forecasting of solar pv power generation
publisher Nanyang Technological University
publishDate 2020
url https://hdl.handle.net/10356/144163
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