dc.contributor.author | Trabelsi, Mohamed | |
dc.contributor.author | Massaoudi, Mohamed | |
dc.contributor.author | Chihi, Ines | |
dc.contributor.author | Sidhom, Lilia | |
dc.contributor.author | Refaat, Shady S. | |
dc.contributor.author | Huang, Tingwen | |
dc.contributor.author | Oueslati, Fakhreddine S. | |
dc.contributor.editor | Kucukdemiral, Ibrahim Beklan | |
dc.contributor.editor | Eren, Yavuz | |
dc.date.accessioned | 2022-12-08T12:30:01Z | |
dc.date.available | 2022-12-08T12:30:01Z | |
dc.date.issued | 2022-12 | |
dc.identifier.citation | Trabelsi , M , Massaoudi , M , Chihi , I , Sidhom , L , Refaat , S S , Huang , T , Oueslati , F S , Kucukdemiral , I B (ed.) & Eren , Y (ed.) 2022 , ' An Effective Hybrid Symbolic Regression–Deep Multilayer Perceptron Technique for PV Power Forecasting ' , Energies , vol. 15 , no. 23 , 9008 . https://doi.org/10.3390/en15239008 | |
dc.identifier.issn | 1996-1073 | |
dc.identifier.other | Jisc: 768943 | |
dc.identifier.other | publisher-id: energies-15-09008 | |
dc.identifier.other | ORCID: /0000-0001-9392-6141/work/124446658 | |
dc.identifier.uri | http://hdl.handle.net/2299/25941 | |
dc.description | © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | |
dc.description.abstract | The integration of Photovoltaic (PV) systems requires the implementation of potential PV power forecasting techniques to deal with the high intermittency of weather parameters. In the PV power prediction process, Genetic Programming (GP) based on the Symbolic Regression (SR) model has a widespread deployment since it provides an effective solution for nonlinear problems. However, during the training process, SR models might miss optimal solutions due to the large search space for the leaf generations. This paper proposes a novel hybrid model that combines SR and Deep Multi-Layer Perceptron (MLP) for one-month-ahead PV power forecasting. A case study analysis using a real Australian weather dataset was conducted, where the employed input features were the solar irradiation and the historical PV power data. The main contribution of the proposed hybrid SR-MLP algorithm are as follows: (1) The training speed was significantly improved by eliminating unimportant inputs during the feature selection process performed by the Extreme Boosting and Elastic Net techniques; (2) The hyperparameters were preserved throughout the training and testing phases; (3) The proposed hybrid model made use of a reduced number of layers and neurons while guaranteeing a high forecasting accuracy; (4) The number of iterations due to the use of SR was reduced. The presented simulation results demonstrate the higher forecasting accuracy (reductions of more than 20% for Root Mean Square Error (RMSE) and 30 % for Mean Absolute Error (MAE) in addition to an improvement in the R2 evaluation metric) and robustness (preventing the SR from converging to local minima with the help of the ANN branch) of the proposed SR-MLP model as compared to individual SR and MLP models. | en |
dc.format.extent | 14 | |
dc.format.extent | 18813511 | |
dc.language.iso | eng | |
dc.relation.ispartof | Energies | |
dc.subject | Article | |
dc.subject | hybrid model | |
dc.subject | genetic algorithm | |
dc.subject | PV power forecasting | |
dc.subject | symbolic regression | |
dc.subject | deep multi-layer perceptron | |
dc.subject | MLP | |
dc.subject | Control and Optimization | |
dc.subject | Energy (miscellaneous) | |
dc.subject | Engineering (miscellaneous) | |
dc.subject | Energy Engineering and Power Technology | |
dc.subject | Electrical and Electronic Engineering | |
dc.subject | Building and Construction | |
dc.subject | Fuel Technology | |
dc.subject | Renewable Energy, Sustainability and the Environment | |
dc.title | An Effective Hybrid Symbolic Regression–Deep Multilayer Perceptron Technique for PV Power Forecasting | en |
dc.contributor.institution | Communications and Intelligent Systems | |
dc.contributor.institution | Centre for Engineering Research | |
dc.contributor.institution | Department of Engineering and Technology | |
dc.contributor.institution | School of Physics, Engineering & Computer Science | |
dc.contributor.institution | School of Computer Science | |
dc.description.status | Peer reviewed | |
dc.identifier.url | http://www.scopus.com/inward/record.url?scp=85143796812&partnerID=8YFLogxK | |
rioxxterms.versionofrecord | 10.3390/en15239008 | |
rioxxterms.type | Journal Article/Review | |
herts.preservation.rarelyaccessed | true | |