EXECUTION OF SYNTHETIC BAYESIAN MODEL AVERAGE FOR SOLAR ENERGY FORECASTING
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Date
2022
Authors
Abedinia, Oveis
Bagheri, Mehdi
Journal Title
Journal ISSN
Volume Title
Publisher
IET Renewable Power Generation
Abstract
Accurate photovoltaic (PV) forecasting is quite crucial in planning and in the regular oper ation of power system. Stochastic habit along with the high risks in PV signal uncertainty
and a probabilistic forecasting model is required to address the numerical weather pre diction (NWP) underdispersion. In this study, a new synthetic prediction process based
on Bayesian model averaging (BMA) and Ensemble Learning is developed. The pro posed model is initiated by the improved self-organizing map (ISOM) clustering K-fold
cross-validation for the training process. To provide desirable learning model for different
input samples, three learners including long short-term memory (LSTM) network, gen eral regression neural network (GRNN), and non-linear auto-regressive eXogenous NN
(NARXNN) are employed. The proposed BMA approach is combined with the output
of the learners to obtain accurate and desirable outcomes. Different models are precisely
compared with the obtained numerical results over real-world engineering test site, that is,
Arta-Solar case study. The numerical analysis and recorded results validate the performance
and superiority of the proposed model.
Description
Keywords
Type of access: Open Access, solar energy forecasting
Citation
Abedinia, O., & Bagheri, M. (2022). Execution of synthetic Bayesian model average for solar energy forecasting. IET Renewable Power Generation, 16(6), 1134–1147. https://doi.org/10.1049/rpg2.12389