Weather Based Photovoltaic Energy Generation Prediction Using LSTM Networks

Sahar Arshi; Li Zhang; Becky Strachan

doi:10.1109/IJCNN.2019.8852206

Weather Based Photovoltaic Energy Generation Prediction Using LSTM Networks

Sahar Arshi, Li Zhang, Becky Strachan

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

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	International Joint Conference on Neural Networks (IJCNN)
Publisher	IEEE
ISBN (Electronic)	978-1-7281-1985-4
ISBN (Print)	978-1-7281-1986-1
DOIs	https://doi.org/10.1109/IJCNN.2019.8852206
Publication status	Published - 30 Sept 2019

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

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10.1109/IJCNN.2019.8852206

Cite this

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title = "Weather Based Photovoltaic Energy Generation 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.",

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

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doi = "10.1109/IJCNN.2019.8852206",

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booktitle = "2019 International Joint Conference on Neural Networks (IJCNN)",

publisher = "IEEE",

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T1 - Weather Based Photovoltaic Energy Generation Prediction Using LSTM Networks

AU - Arshi, Sahar

AU - Zhang, Li

AU - Strachan, Becky

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.

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DO - 10.1109/IJCNN.2019.8852206

M3 - Conference contribution

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

PB - IEEE

CY - International Joint Conference on Neural Networks (IJCNN)

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