Wind/solar power forecasting using improved LSTM neural networks

Wind/solar power forecasting using improved LSTM neural networks

Nowadays, new energy become more and more important not only for industry but also for our citizens. How to forecast the wind and solar power correctly is also necessary for power plant. In this dissertation, four kinds of forecasting system based respectively on NARX model, BP neural network model,...

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Main Author: Liu, Shixian
Other Authors: Ponnuthurai N. Suganthan
Format: Theses and Dissertations
Language:English
Published: 2019
Subjects:
DRNTU::Engineering::Electrical and electronic engineering
Online Access:http://hdl.handle.net/10356/78572
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-785722023-07-04T16:23:05Z Wind/solar power forecasting using improved LSTM neural networks Liu, Shixian Ponnuthurai N. Suganthan School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering Nowadays, new energy become more and more important not only for industry but also for our citizens. How to forecast the wind and solar power correctly is also necessary for power plant. In this dissertation, four kinds of forecasting system based respectively on NARX model, BP neural network model, RNN model, and LSTM neural network model are described and the performance of these model are compared. It is shown that the forecast result of LSTM model is much better than NARX model and other models. With a very small MSE, the LSTM model is really suitable for wind power and solar power forecasting. Master of Science (Computer Control and Automation) 2019-06-24T02:34:09Z 2019-06-24T02:34:09Z 2019 Thesis http://hdl.handle.net/10356/78572 en 58 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering
spellingShingle DRNTU::Engineering::Electrical and electronic engineering
Liu, Shixian
Wind/solar power forecasting using improved LSTM neural networks
description Nowadays, new energy become more and more important not only for industry but also for our citizens. How to forecast the wind and solar power correctly is also necessary for power plant. In this dissertation, four kinds of forecasting system based respectively on NARX model, BP neural network model, RNN model, and LSTM neural network model are described and the performance of these model are compared. It is shown that the forecast result of LSTM model is much better than NARX model and other models. With a very small MSE, the LSTM model is really suitable for wind power and solar power forecasting.
author2 Ponnuthurai N. Suganthan
author_facet Ponnuthurai N. Suganthan
Liu, Shixian
format Theses and Dissertations
author Liu, Shixian
author_sort Liu, Shixian
title Wind/solar power forecasting using improved LSTM neural networks
title_short Wind/solar power forecasting using improved LSTM neural networks
title_full Wind/solar power forecasting using improved LSTM neural networks
title_fullStr Wind/solar power forecasting using improved LSTM neural networks
title_full_unstemmed Wind/solar power forecasting using improved LSTM neural networks
title_sort wind/solar power forecasting using improved lstm neural networks
publishDate 2019
url http://hdl.handle.net/10356/78572
_version_ 1772828603535327232

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