Stacking Ensemble Method with the RNN Meta-Learner for Short-Term PV Power Forecasting

Lateko, Andi A. H.; Yang, Hong-Tzer; Huang, Chao-Ming; Aprillia, Happy; Hsu, Che-Yuan; Zhong, Jie-Lun; Phương, Nguyễn Hoàng

doi:10.3390/en14164733

Cited by 25 publications

(17 citation statements)

References 49 publications

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“…Stacking RNN is the ensemble method based on stacked generalization by training the first-level learners and combining them using the second-level learner to obtain the final forecasting results. A more detailed explanation of the stacking RNN ensemble method can be found in [31].…”

Section: The Stacking Rnn Ensemble Methodsmentioning

confidence: 99%

“…It also uses regression-based methods such as linear regression (LR) and support vector regression (SVR) and uses LASSO and Ridge regularization for weighting to combine the forecasting results. A previous study implemented a stacked generalization ensemble method for short-term PV power forecasting using an RNN meta learner [31]. The stacking RNN method is used as a benchmark for the ensemble forecasting method for this study.…”

Section: Introductionmentioning

confidence: 99%

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

2022

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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%.

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Section: The Stacking Rnn Ensemble Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2022

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“…This kind of network uses stacking to stack the layers of the LSTM network. 24 A hierarchical feature representation of the input data can be created through this structure, which can subsequently be used for prediction.…”

Section: Deep Learning Model Based On the Stacked Lstm Networkmentioning

confidence: 99%

“…A multilayer LSTM network can remove some of the constraints of a single-layer LSTM network, which can be viewed as the output of the upper layer and as the input of the next layer. This kind of network uses stacking to stack the layers of the LSTM network . A hierarchical feature representation of the input data can be created through this structure, which can subsequently be used for prediction.…”

Section: Deep Learning Model Based On the Stacked Lstm Networkmentioning

confidence: 99%

Reservoir Production Prediction Model Based on a Stacked LSTM Network and Transfer Learning

et al. 2021

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Gas injection and water injection are common and effective methods to improve oil recovery. To ensure its production effect, it is necessary to simulate the oilfield production process. However, traditional composition simulation runs a large number of calculations and takes a long time. Through the analysis of relevant data, we found that production is affected by many factors and has a strong sequential character. Therefore, this paper proposes a deep learning model for reservoir production prediction based on stacked long short-term memory network (LSTM). It is applied to other well patterns with a short production time and a few samples in the same oilfield block by transfer learning. The model achieves an effective combination with the actual reservoir production process. At the same time, it uses the knowledge learned from the well pattern with sufficient historical data to assist in the establishment of the model of the well pattern with limited data. This can obtain accurate prediction results and save the model training time, thus getting more effective application effects than composition simulation. This paper verifies the effectiveness of the proposed method through the data and multiple different injection combinations of the Tarim oilfield.

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“…The curves for irradiance and PV power output are quite similar, thus this method usually accurately estimates PV power output if the irradiance prediction is accurate. Direct prediction [4][5][6][7][8][9][10] uses the historical PV power output and weather variables (including irradiance) as input variables, and the prediction results for PV power generation are the output. This method requires an accurate weather forecast to produce good forecast results.…”

Section: Introductionmentioning

confidence: 99%

A Parameter Estimation Method for a Photovoltaic Power Generation System Based on a Two-Diode Model

2022

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This study presents a parameter estimation method that uses an enhanced gray wolf optimizer (EGWO) to optimize the parameters for a two-diode photovoltaic (PV) power generation system. The proposed method consists of three stages. The first stage converts seven parameters for the two-diode model into 17 parameters for different environmental conditions, which provides more precise parameter estimation for the PV model. A PV power generation model is then established to represent the nonlinear relationship between inputs and outputs. The second stage involves a parameter sensitivity analysis and uses the overall effect method to remove the parameters that have smaller effect on the output. The final stage uses an enhanced GWO that is associated with measurement data to optimally estimate the parameters that are selected in the second stage. When the parameters are estimated, the predicted value for the PV power output is calculated for specific values of solar irradiation and module temperature. The proposed method is verified on a 200 kWp PV power generation system. To confirm the feasibility of the proposed method, the parameter estimation before and after optimization are compared, and these results are compared with other optimization algorithms, as well as those for a single-diode PV model.

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Stacking Ensemble Method with the RNN Meta-Learner for Short-Term PV Power Forecasting

Cited by 25 publications

References 49 publications

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

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

Reservoir Production Prediction Model Based on a Stacked LSTM Network and Transfer Learning

A Parameter Estimation Method for a Photovoltaic Power Generation System Based on a Two-Diode Model

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