Open AccessApplication of parallel Elman neural network to hourly area solar PV plant generation estimation
Author(s) -
Cho MingYuan,
Chang JyhMing,
Huang ChihChun
Publication year - 2020
Publication title -
international transactions on electrical energy systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.428
H-Index - 42
ISSN - 2050-7038
DOI - 10.1002/2050-7038.12470
Subject(s) - photovoltaic system , artificial neural network , weighting , solar power , reliability (semiconductor) , electricity generation , power (physics) , computer science , reliability engineering , electricity , engineering , simulation , meteorology , artificial intelligence , electrical engineering , geography , medicine , physics , quantum mechanics , radiology
Summary Based on existing power generation data, an hourly area solar power estimation model using the parallel Elman neural network with solar radiation and system conversion efficiency is proposed. The accuracy and reliability of the assessment were verified using the information/data of solar photovoltaic power stations in various regions and timescales. Using the established appraisal algorithm involving K‐means evaluation and inverse distance weighting, regional forecasting of solar power generation was achieved. The prediction accuracy was also investigated using the actual details of the photovoltaic power stations. The results of the proposed model can assist the electricity dispatcher to not only precisely monitor the trend of solar power generation in different areas, but also coordinate with traditional power plants to meet the load demand more accurately. The proposed method can benefit power dispatching involving a larger scale of intermittent and unstable solar power electricity in the future.