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Enhanced Deep Belief Network Based on Ensemble Learning and Tree-Structured of Parzen Estimators: An Optimal Photovoltaic Power Forecasting Method

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submitted on 2023-08-31, 10:07 and posted on 2023-08-31, 10:55 authored by Mohamed Massaoudi, Haitham Abu-Rub, Shady S. Refaat, Mohamed Trabelsi, Ines Chihi, Fakhreddine S. Oueslati

The random fluctuation and non-uniformity of Photovoltaic (PV) power generation greatly affect the power grids’ stability and operation. This paper addresses the high volatility of PV power by proposing a precise and reliable ensemble learning model for short-term PV power generation forecasting. The proposed forecasting tool incorporates a base model and meta-model layers. The first-layer base learner combines extreme learning machines, extremely randomized trees, k-nearest neighbor, and mondrian forest models. The meta-model layer exploits deep belief network to generate the final outputs. The hyper-parameters of the proposed stacking ensemble are carefully tuned using the tree-structured of parzen estimators algorithm to achieve top-notch predictive performance. The proposed model is thoroughly assessed through an empirical study using a real data set from Australia. The simulation results confirm the performance superiority of the proposed model over the existing forecasting models with the lowest average root mean square error and mean absolute percentage error of 3.88kW and 2.30%, respectively.

Other Information

Published in: IEEE Access
License: https://creativecommons.org/licenses/by/4.0/
See article on publisher's website: https://dx.doi.org/10.1109/access.2021.3125895

Funding

Open Access funding provided by the Qatar National Library.

History

Language

  • English

Publisher

IEEE

Publication Year

  • 2021

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License.

Institution affiliated with

  • Texas A&M University at Qatar