Open AccessSwarm Decomposition Technique Based Hybrid Model for Very Short-Term Solar PV Power Generation Forecast
Author(s) -
Emrah Dokur
Publication year - 2020
Publication title -
elektronika ir elektrotechnika
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.224
H-Index - 26
eISSN - 2029-5731
pISSN - 1392-1215
DOI - 10.5755/j01.eie.26.3.25898
Subject(s) - photovoltaic system , solar irradiance , solar power , grid connected photovoltaic power system , solar resource , electricity generation , power (physics) , meteorology , environmental science , computer science , engineering , maximum power point tracking , electrical engineering , inverter , physics , quantum mechanics
Accurate predictions of solar photovoltaic (PV) power generation at different time horizons are essential for reliable operation of energy management systems. The output power of a PV power plant is dependent on non-linear and intermittent environmental factors, such as solar irradiance, wind speed, relative humidity, etc. Intermittency and randomness of solar PV power effect precision of estimation. To address the challenge, this paper presents a Swarm Decomposition Technique (SWD) based hybrid model as a novel approach for very short-term (15 min) solar PV power generation forecast. The original contribution of the study is to investigate use of SWD for solar data forecast. The solar PV power generation data with hourly resolution obtained from the field (grid connected, 857.08 kWp Akgul Solar PV Power Plant in Turkey) are used to develop and validate the forecast model. Specifically, the analysis showed that the hybrid model with SWD technique provides highly accurate predictions in cloudy periods.