A Novel Ensemble Algorithm for Solar Power Forecasting Based on Kernel Density Estimation

Mohamed Lotfi, Mohammad Javadi, Gerardo J. Osório, Cláudio Monteiro and João P. S. Catalão
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Mohamed Lotfi: Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
Mohammad Javadi: INESC TEC, 4200-465 Porto, Portugal
Gerardo J. Osório: C-MAST, University of Beira Interior, 6201-001 Covilha, Portugal
Cláudio Monteiro: Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
João P. S. Catalão: Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal

Energies, 2020, vol. 13, issue 1, 1-19

Abstract: A novel ensemble algorithm based on kernel density estimation (KDE) is proposed to forecast distributed generation (DG) from renewable energy sources (RES). The proposed method relies solely on publicly available historical input variables (e.g., meteorological forecasts) and the corresponding local output (e.g., recorded power generation). Given a new case (with forecasted meteorological variables), the resulting power generation is forecasted. This is performed by calculating a KDE-based similarity index to determine a set of most similar cases from the historical dataset. Then, the outputs of the most similar cases are used to calculate an ensemble prediction. The method is tested using historical weather forecasts and recorded generation of a PV installation in Portugal. Despite only being given averaged data as input, the algorithm is shown to be capable of predicting uncertainties associated with high frequency weather variations, outperforming deterministic predictions based on solar irradiance forecasts. Moreover, the algorithm is shown to outperform a neural network (NN) in most test cases while being exceptionally faster (32 times). Given that the proposed model only relies on public locally-metered data, it is a convenient tool for DG owners/operators to effectively forecast their expected generation without depending on private/proprietary data or divulging their own.

Keywords: forecasting; ensemble methods; kernel density estimation; smart grids; distributed generation; solar PV (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (17)

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