Prediction Using LSTM Networks

Arshi, Sahar, Zhang, Li and Strachan, Becky (2019) Prediction Using LSTM Networks. In: 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, Piscataway, pp. 1-8. ISBN 9781728119854

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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. LongShort 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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