Details

Autor(en) / Beteiligte

• Firdaus, Aji Akbar
• Yunardi, Riky Tri
• Agustin, Eva Inaiyah
• Putri, Tesa Eranti
• Anggriawan, Dimas Okky

Titel

Short-term photovoltaics power forecasting using Jordan recurrent neural network in Surabaya

Ist Teil von

• Telkomnika, 2020-04, Vol.18 (2), p.1089-1094

Ort / Verlag

Yogyakarta: Ahmad Dahlan University

Erscheinungsjahr

2020

Link zum Volltext

Quelle

EZB Free E-Journals

Beschreibungen/Notizen

• The power generated by PV is highly dependent on temperature and solar radiation. [...]accurate forecasting of short-term PV power is important for system reliability and large-scale PV development to overcome the power generated by intermittent PV. (ProQuest: ... denotes formulae omitted.) 1.INTRODUCTION In renewable energy, PV gets a lot of attention to replace fossil-fueled plants [1, 2]. Because PV uses solar energy to generate electrical energy. [...]accurate and fast PV power forecasting methods are needed so that intermittent PV power can be overcome. [...]that the JRNN can be said to be more accurate because JRNN has a low error rate. Besides that, the time needed for the JRNN to produce these predictions is longer than ANN. 4.CONCLUSION PV power predictions have been successfully carried out with the JRNN method, which gives low MSE and MAPE values. because the lower the MSE and MAPE, the predictions generated can be close to the actual data.