Open AccessEffective prediction model for Hungarian small‐scale solar power output
Author(s) -
Abedinia Oveis,
Raisz David,
Amjady Nima
Publication year - 2017
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
eISSN - 1752-1424
pISSN - 1752-1416
DOI - 10.1049/iet-rpg.2017.0165
Subject(s) - artificial neural network , feature selection , photovoltaic system , computer science , multilayer perceptron , solar energy , artificial intelligence , selection (genetic algorithm) , power (physics) , electric power system , machine learning , engineering , physics , quantum mechanics , electrical engineering
Owing to critical role of photovoltaic (PV) power in oncoming energy market, an accurate PV power forecasting model is demanded. In this paper, an effective solar power prediction model composed of variational mode decomposition, information‐theoretic feature selection, and forecasting engine with high learning capability is proposed. The feature selection method is based on information‐theoretic criteria and an optimisation algorithm. The forecasting engine is multilayer perceptron neural network equipped with modified Levenberg–Marquardt learning algorithm. An evolutionary algorithm is also incorporated into the training mechanism of the forecasting engine to enhance its learning capability. Effectiveness of the proposed PV prediction model is illustrated on a Hungarian solar power plant.