Prediction Using LSTM Networks

Sahar Arshi; Li Zhang; Rebecca Strachan

doi:10.1109/IJCNN.2019.8852206

Prediction Using LSTM Networks

Sahar Arshi, Li Zhang, Rebecca Strachan

School of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

13 Citations (Scopus)

89 Downloads (Pure)

Abstract

Photovoltaic (PV) systems use the sunlight and convert it to electrical power. It is predicted that by 2023, 371,000 PV installations will be embedded in power networks in the UK. This may increase the risk of voltage rise which has adverse impacts on the power network. The balance maintenance is important for high security of the physical electrical systems and the operation economy. Therefore, the prediction of the output of PV systems is of great importance. The output of a PV system highly depends on local environmental conditions. These include sun radiation, temperature, and humidity. In this research, the importance of various weather factors are studied. The weather attributes are subsequently employed for the prediction of the solar panel power generation from a time-series database. Long-Short Term Memory networks are employed for obtaining the dependencies between various elements of the weather conditions and the PV energy metrics. Evaluation results indicate the efficiency of the deep networks for energy generation prediction.

Original language	English
Title of host publication	2019 International Joint Conference on Neural Networks (IJCNN)
Place of Publication	Piscataway
Pages	1-8
Number of pages	8
ISBN (Electronic)	9781728119861
DOIs	https://doi.org/10.1109/IJCNN.2019.8852206
Publication status	Published - 30 Sept 2019
Event	2019 International Joint Conference on Neural Networks, IJCNN 2019 - Budapest, Hungary Duration: 14 Jul 2019 → 19 Jul 2019

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks

Conference

Conference	2019 International Joint Conference on Neural Networks, IJCNN 2019
Country/Territory	Hungary
City	Budapest
Period	14/07/19 → 19/07/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Photovoltaic systems
Solar panels
Long Short Term Memory
Energy Forecasting

Access to Document

10.1109/IJCNN.2019.8852206

Arshi et al - Weather based photovoltaic energy generation prediction AAMAccepted author manuscript, 564 KB

Cite this

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title = "Prediction Using LSTM Networks",

abstract = "Photovoltaic (PV) systems use the sunlight and convert it to electrical power. It is predicted that by 2023, 371,000 PV installations will be embedded in power networks in the UK. This may increase the risk of voltage rise which has adverse impacts on the power network. The balance maintenance is important for high security of the physical electrical systems and the operation economy. Therefore, the prediction of the output of PV systems is of great importance. The output of a PV system highly depends on local environmental conditions. These include sun radiation, temperature, and humidity. In this research, the importance of various weather factors are studied. The weather attributes are subsequently employed for the prediction of the solar panel power generation from a time-series database. Long-Short Term Memory networks are employed for obtaining the dependencies between various elements of the weather conditions and the PV energy metrics. Evaluation results indicate the efficiency of the deep networks for energy generation prediction.",

keywords = "Photovoltaic systems, Solar panels, Long Short Term Memory, Energy Forecasting",

author = "Sahar Arshi and Li Zhang and Rebecca Strachan",

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AU - Arshi, Sahar

AU - Zhang, Li

AU - Strachan, Rebecca

PY - 2019/9/30

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N2 - Photovoltaic (PV) systems use the sunlight and convert it to electrical power. It is predicted that by 2023, 371,000 PV installations will be embedded in power networks in the UK. This may increase the risk of voltage rise which has adverse impacts on the power network. The balance maintenance is important for high security of the physical electrical systems and the operation economy. Therefore, the prediction of the output of PV systems is of great importance. The output of a PV system highly depends on local environmental conditions. These include sun radiation, temperature, and humidity. In this research, the importance of various weather factors are studied. The weather attributes are subsequently employed for the prediction of the solar panel power generation from a time-series database. Long-Short Term Memory networks are employed for obtaining the dependencies between various elements of the weather conditions and the PV energy metrics. Evaluation results indicate the efficiency of the deep networks for energy generation prediction.

AB - Photovoltaic (PV) systems use the sunlight and convert it to electrical power. It is predicted that by 2023, 371,000 PV installations will be embedded in power networks in the UK. This may increase the risk of voltage rise which has adverse impacts on the power network. The balance maintenance is important for high security of the physical electrical systems and the operation economy. Therefore, the prediction of the output of PV systems is of great importance. The output of a PV system highly depends on local environmental conditions. These include sun radiation, temperature, and humidity. In this research, the importance of various weather factors are studied. The weather attributes are subsequently employed for the prediction of the solar panel power generation from a time-series database. Long-Short Term Memory networks are employed for obtaining the dependencies between various elements of the weather conditions and the PV energy metrics. Evaluation results indicate the efficiency of the deep networks for energy generation prediction.

KW - Photovoltaic systems

KW - Solar panels

KW - Long Short Term Memory

KW - Energy Forecasting

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UR - https://www.scopus.com/pages/publications/85073225761

U2 - 10.1109/IJCNN.2019.8852206

DO - 10.1109/IJCNN.2019.8852206

M3 - Conference contribution

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SN - 9781728119854

T3 - Proceedings of the International Joint Conference on Neural Networks

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BT - 2019 International Joint Conference on Neural Networks (IJCNN)

CY - Piscataway

T2 - 2019 International Joint Conference on Neural Networks, IJCNN 2019

Y2 - 14 July 2019 through 19 July 2019

ER -

Prediction Using LSTM Networks

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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Cite this