Short-Term PV Power Forecasting Using a Regression-Based Ensemble Method

• Published in: Energies (Basel) Vol. 15; no. 11; p. 4171
• Main Authors: Lateko, Andi A. H., Yang, Hong-Tzer, Huang, Chao-Ming
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2022
• Subjects: Accuracy; Algorithms; Alternative energy sources; Atmospheric aerosols; Bayesian analysis; Benchmarks; Classification; clustering method; Coefficients; Deep learning; Economic forecasting; Electricity; Electricity distribution; Energy sources; ensemble method; Forecasting; Humidity; Industrial areas; linear regression; Mathematical models; Model testing; Neural networks; Optimization; Photovoltaics; PV power forecasting; Random forest; Recurrent neural networks; Regression analysis; Regularization; Renewable energy sources; Solar energy; support vector machine; Support vector machines; Time series
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en15114171

One of the most critical aspects of integrating renewable energy sources into the smart grid is photovoltaic (PV) power generation forecasting. This ensemble forecasting technique combines several forecasting models to increase the forecasting accuracy of the individual models. This study proposes a regression-based ensemble method for day-ahead PV power forecasting. The general framework consists of three steps: model training, creating the optimal set of weights, and testing the model. In step 1, a Random forest (RF) with different parameters is used for a single forecasting method. Five RF models (RF1, RF2, RF3, RF4, and RF5) and a support vector machine (SVM) for classification are established. The hyperparameters for the regression-based method involve learners (linear regression (LR) or support vector regression (SVR)), regularization (least absolute shrinkage and selection operator (LASSO) or Ridge), and a penalty coefficient for regularization (λ). Bayesian optimization is performed to find the optimal value of these three hyperparameters based on the minimum function. The optimal set of weights is obtained in step 2 and each set of weights contains five weight coefficients and a bias. In the final step, the weather forecasting data for the target day is used as input for the five RF models and the average daily weather forecasting data is also used as input for the SVM classification model. The SVM output selects the weather conditions, and the corresponding set of weight coefficients from step 2 is combined with the output from each RF model to obtain the final forecasting results. The stacking recurrent neural network (RNN) is used as a benchmark ensemble method for comparison. Historical PV power data for a PV site in Zhangbin Industrial Area, Taiwan, with a 2000 kWp capacity is used to test the methodology. The results for the single best RF model, the stacking RNN, and the proposed method are compared in terms of the mean relative error (MRE), the mean absolute error (MAE), and the coefficient of determination (R2) to verify the proposed method. The results for the MRE show that the proposed method outperforms the best RF method by 20% and the benchmark method by 2%.