Meteorological Data Forecast using RNN

Balluff, Stefan; Bendfeld, Jörg; Krauter, Stefan

doi:10.4018/ijghpc.2017010106

Cited by 7 publications

(2 citation statements)

References 14 publications

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“…Additionally, it can help when an individual prediction model method produces large prediction errors at specific points. In [15], PV power was predicted with relatively high accuracy by recurrent neural network (RNN) model training on historical time series data. However, the gradient disappearance and gradient explosion limit the prediction time range.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Prediction of PV Power Based on Combined Modal Decomposition and NARX-LSTM-LightGBM

et al. 2023

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Recently, solar energy has been gaining attention as one of the best promising renewable energy sources. Accurate PV power prediction models can solve the impact on the power system due to the non-linearity and randomness of PV power generation and play a crucial role in the operation and scheduling of power plants. This paper proposes a novel machine learning network framework to predict short-term PV power in a time-series manner. The combination of nonlinear auto-regressive neural networks with exogenous input (NARX), long short term memory (LSTM) neural network, and light gradient boosting machine (LightGBM) prediction model (NARX-LSTM-LightGBM) was constructed based on the combined modal decomposition. Specifically, this paper uses a dataset that includes ambient temperature, irradiance, inverter temperature, module temperature, etc. Firstly, the feature variables with high correlation effects on PV power were selected by Pearson correlation analysis. Furthermore, the PV power is decomposed into a new feature matrix by (EMD), (EEMD) and (CEEMDAN), i.e., the combination decomposition (CD), which deeply explores the intrinsic connection of PV power historical series information and reduces the non-smoothness of PV power. Finally, preliminary PV power prediction values and error correction vector are obtained by NARX prediction. Both are embedded into the NARX-LSTM-LightGBM model pair for PV power prediction, and then the error inverse method is used for weighted optimization to improve the accuracy of the PV power prediction. The experiments were conducted with the measured data from Andre Agassi College, USA, and the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of the model under different weather conditions were lower than 1.665 kw, 0.892 kw and 0.211, respectively, which are better than the prediction results of other models and proved the effectiveness of the model.

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Section: Introductionmentioning

confidence: 99%

Short-Term Prediction of PV Power Based on Combined Modal Decomposition and NARX-LSTM-LightGBM

et al. 2023

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“…For point forecasting, the authors in [27] succeeded to predict the PV power with high accuracy using a Recurrent Neural Network (RNN) technique where a historical database is used in the training process. However, it has been concluded that the vanishing gradient limits the forecasting time horizon of the RNN due to the gradient explosion and disappearance [5].…”

Section: Introductionmentioning

confidence: 99%

An Effective Hybrid NARX-LSTM Model for Point and Interval PV Power Forecasting

et al. 2021

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This paper proposes an effective Photovoltaic (PV) Power Forecasting (PVPF) technique based on hierarchical learning combining Nonlinear Auto-Regressive Neural Networks with exogenous input (NARXNN) with Long Short-Term Memory (LSTM) model. First, the NARXNN model acquires the data to generate a residual error vector. Then, the stacked LSTM model, optimized by Tabu search algorithm, uses the residual error correction associated with the original data to produce a point and interval PVPF. The performance of the proposed PVPF technique was investigated using two real datasets with different scales and locations. The comparative analysis of the NARX-LSTM with twelve existing benchmarks confirms its superiority in terms of accuracy measures. In summary, the proposed NARX-LSTM technique has the following major achievements: 1) Improves the prediction performance of the original LSTM and NARXNN models; 2) Evaluates the uncertainties associated with point forecasts with high accuracy; 3) Provides a high generalization capability for PV systems with different scales. Numerical results of the comparison of the proposed NARX-LSTM method with two real-world PV systems in Australia and USA demonstrate its improved prediction accuracy, outperforming the benchmark approaches with an overall normalized Rooted Mean Squared Error (nRMSE) of 1.98% and 1.33% respectively. INDEX TERMS Long Short-Term Memory (LSTM), photovoltaic power forecasting, Nonlinear Auto-Regressive Neural Networks with exogenous input (NARXNN), Tabu Search Algorithm (TSA).

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