TY - GEN
T1 - Prediction Using LSTM Networks
AU - Arshi, Sahar
AU - Zhang, Li
AU - Strachan, Rebecca
PY - 2019/9/30
Y1 - 2019/9/30
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
UR - https://www.ijcnn.org/assets/docs/ijcnn2019-program-Jul07-largefont.pdf
U2 - 10.1109/IJCNN.2019.8852206
DO - 10.1109/IJCNN.2019.8852206
M3 - Conference contribution
AN - SCOPUS:85073225761
SN - 9781728119854
T3 - Proceedings of the International Joint Conference on Neural Networks
SP - 1
EP - 8
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 -
