A Hybrid Solar Irradiance Forecasting Using Full Wavelet Packet Decomposition and Bi-Directional Long Short-Term Memory (BiLSTM)

Singla, Pradeep; Duhan, Manoj; Saroha, Sumit

doi:10.1007/s13369-022-06655-2

Cited by 23 publications

(9 citation statements)

References 58 publications

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“…In the data processing stage, Recurrent Neural Networks (RNN) may face the challenge of gradient explosion due to the large amount of data and the long duration of the sequence [22,23]. In 1997, Horchreiter and Schmidhuber proposed a special type of Recurrent Neural Network…”

Section: Gru Modelmentioning

confidence: 99%

Short-term prediction of PM2.5 concentration by hybrid neural network based on sequence decomposition

Wu,

Zhu,

Wen

2024

PLoS ONE

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Accurate forecasting of PM2.5 concentrations serves as a critical tool for mitigating air pollution. This study introduces a novel hybrid prediction model, termed MIC-CEEMDAN-CNN-BiGRU, for short-term forecasting of PM2.5 concentrations using a 24-hour historical data window. Utilizing the Maximal Information Coefficient (MIC) for feature selection, the model integrates Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Convolutional Neural Network (CNN), and Bidirectional Recurrent Gated Neural Network (BiGRU) to optimize predictive accuracy. We used 2016 PM2.5 monitoring data from Beijing, China as the empirical basis of this study and compared the model with several deep learning frameworks. RNN, LSTM, GRU, and other hybrid models based on GRU, respectively. The experimental results show that the prediction results of the hybrid model proposed in this question are more accurate than those of other models, and the R2 of the hybrid model proposed in this paper improves the R2 by nearly 5 percentage points compared with that of the single model; reduces the MAE by nearly 5 percentage points; and reduces the RMSE by nearly 11 percentage points. The results show that the hybrid prediction model proposed in this study is more accurate than other models in predicting PM2.5.

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Section: Gru Modelmentioning

confidence: 99%

Short-term prediction of PM2.5 concentration by hybrid neural network based on sequence decomposition

Wu,

Zhu,

Wen

2024

PLoS ONE

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“…Solar irradiance forecasting using a bi-directional LSTM model along with wavelet decomposition is proposed in [9]. The process involves forecasting for every decomposed frequency feature and the final point forecast produced as a result of reconstruction using the wavelet decomposition technique.…”

Section: B Literature Review: Cnn-lstm and Lstm Bi-directional Modelsmentioning

confidence: 99%

“…This means that given a vector of inputs to the model it is able to understand the relationship between the past and future samples moving from both the past to future direction and from the future to past direction. Such models have been implemented in the studies [9], [17] and in this study the following architecture with the bi-directional LSTM layer is shown in Fig. 2.…”

Section: B Bi-directional Lstm Modelmentioning

confidence: 99%

Probabilistic LSTM-Autoencoder Based Hour-Ahead Solar Power Forecasting Model for Intra-Day Electricity Market Participation: A Polish Case Study

et al. 2022

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This article presents the selection of an appropriate deep learning Long Short-Term Memory (LSTM) based probabilistic hour-ahead forecasting model for a grid connected industrial solar PV power plant located in Poland. It has a 317 kW peak power capacity and is connected with a metallurgical plant producing steel for car parts. The purpose of the study is to present a model that could be used by the plant to participate in the Polish intra-day electricity market. Four different LSTM models were investigated which include the vanilla model, the stacked LSTM model, the Bi-directional LSTM model and the LSTM-Autoencoder model. Out of the investigated models it was observed that the LSTM-Autoencoder model was the best performing one in terms of reliability. The average Root Mean Squared Error (RMSE) and the average Mean Absolute Error (MAE) for the Autoencoder model over 100 runs were 15.59 kW and 8.36 kW which represent 4.9% and 2.6% of the peak power respectively. Moreover, it was observed that it has the shortest width for the 95% confidence interval of only 0.5% for both the RMSE and the MAE. In terms of accuracy the best performing model was the LSTM bi-directional model with the average RMSE and MAE values of 12.87 kW and 6.91 kW which represent 4% and 2.1% of the peak power. The 95% confidence intervals width for both the RMSE and MAE over the 100 runs were 0.8% and 0.5% respectively.INDEX TERMS Solar power forecasting, LSTM, deep learning, electricity market and grid connected solar PV.

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“…A day-ahead hybrid solar irradiance forecasting method based on bi-directional Long Short-Term Memory (Bi-LSTM) and full wavelet decomposition was proposed in (Singla et al, 2022c) comparing the results with naïve baseline predictor, LSTM and Gated Recurrent Unit (GRU). Moreover, an ensemble method for dayahead forecast of solar irradiance using wavelet decomposition and Bi-LSTM was proposed in (Singla et al, 2022d).…”

Section: Introductionmentioning

confidence: 99%

Embedding the weather prediction errors (WPE) into the photovoltaic (PV) forecasting method using deep learning

Bâra,

Oprea

2024

Journal of Forecasting

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The creation of features makes the difference in improving the photovoltaic forecast (PVF) for on‐grid, hybrid and off‐grid PV systems. The importance of the PVF is tremendous, and it can be essential in optimizing the home appliances to maximize the Renewable Energy Sources (RES) usage or to create performant bids for the electricity market. Several use cases are considered from the connectivity point of view. Therefore, in this paper, we propose a Weather Prediction Error (WPE)‐based method that uses a Stacking Regressor (SR) for various PV systems that coexist in the emerging Energy Communities (EC) landscape. The novelty of the research we conduct consists in proposing several features and determining the coefficients to adjust the PVF based on WPE. The forecast results of four types of PV systems from size and connectivity point of view are investigated. Compared with individual Machine Learning (ML) models, R2 increases with more than 3% with the SR and with more than 6% after applying the adjustment coefficients. Nevertheless, the major improvement is recorded for the off‐grid inverter and for the large industrial PV power plant, demonstrating that the proposed model is suitable for these types of systems. The other metrics improved as well, especially Mean Average Error (MAE) that decreases between 10% and 23%. A significant decrease is in the case of the industrial on‐grid PV, from 130 kW to 123 kW using the SR and to 107 kW after adjustments. This represents around 18% from the initial MAE. The ratio of daily deviations is also improved using the SR. For all the PV systems, the ratio stabilizes in a shorter interval, from daily values between 0.78 and 1.33 obtained with the ML models to values between 0.83 and 1.24 obtained with the SR. After the final adjustments, the interval becomes shorter, having daily values between 0.90 and 1.10.

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A Hybrid Solar Irradiance Forecasting Using Full Wavelet Packet Decomposition and Bi-Directional Long Short-Term Memory (BiLSTM)

Cited by 23 publications

References 58 publications

Short-term prediction of PM2.5 concentration by hybrid neural network based on sequence decomposition

Short-term prediction of PM2.5 concentration by hybrid neural network based on sequence decomposition

Probabilistic LSTM-Autoencoder Based Hour-Ahead Solar Power Forecasting Model for Intra-Day Electricity Market Participation: A Polish Case Study

Embedding the weather prediction errors (WPE) into the photovoltaic (PV) forecasting method using deep learning

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