Short term PV power forecasting using empirical mode decomposition based orthogonal extreme learning machine technique

Nayak, Niranjan; Pani, Alok Kumar

doi:10.5958/0976-5506.2018.01771.0

Cited by 6 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The most representative method included the wavelet analysis [20] and the (expended) empirical mode decomposition (EMD, EEMD and CEEMDAN) [21][22][23][24]. However, the wavelet basis function need pre-definition and is non-adaptive in nature.…”

Section: State Of the Artmentioning

confidence: 99%

Short-Term PV Power Prediction Based on Optimized VMD and LSTM

Wang

Liu

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Because of intermittence and fluctuation of photovoltaic (PV) power, it is difficult to enhance prediction accuracy. To sustain high-efficient operation of power system, this paper proposes a hybrid method to predict the short-term PV power. It consists of components separation of PV power, parameters optimization and reconstruction of prediction result. Firstly, the methods based on the identifying of feature frequency and mutual information maximum are proposed to optimize the mode number and penalty factor of VMD, respectively. The optimized VMD (OVMD) is used to decompose the complicated fluctuation components of PV power into single component. Then, the improved PSO (IPSO) based on non-linear inertia weight of anti-sine function is proposed to optimize the number of hidden layer nodes, learning rate and iteration number of LSTM network. The optimized LSTM is used to predict each single component of OVMD decomposition. Thirdly, the prediction result of each single component is reconstructed to obtain the final PV prediction power. The experiment result indicates that the prediction accuracy of the proposed method (OVMD-IPSO-LSTM) outperformances the other typical methods. By the improvement of the traditional method (VMD and PSO) and the parameter optimization of LSTM, this hybrid method makes a contribution to the prediction of short-term PV power.

show abstract

Section: State Of the Artmentioning

confidence: 99%

Short-Term PV Power Prediction Based on Optimized VMD and LSTM

Wang

Liu

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…For instance, the authors employed EEMD to address the volatility and instability of the data, and concluded that the prediction results obtained by separately predicting the subseries after EEMD and then summing them are more accurate than direct prediction [25]. The authors proposed prediction model combining EMD and orthogonal extreme learning machine technique for short-term PV power prediction [26]. However, most of the above models concern the decomposition of PV historical data, but rarely consider the temporal characteristics of meteorological data.…”

Section: Introductionmentioning

confidence: 99%

“…In view of the above problems, people have carried out some research. Aiming at the problem of large fluctuations in PV data, an effective method is to reduce the instability of data through mode decomposition, such as wavelet packet transform (WPT) [24], ensemble empirical mode decomposition (EEMD) [25], empirical mode decomposition (EMD) [26]. For instance, the authors employed EEMD to address the volatility and instability of the data, and concluded that the prediction results obtained by separately predicting the subseries after EEMD and then summing them are more accurate than direct prediction [25].…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Deep Learning Model for Photovoltaic Power Forecasting Based on Feature Extraction and BiLSTM

Lin,

Zhang,

2024

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

In this paper, a hybrid photovoltaic power forecasting model is proposed based on bidirectional long‐short‐term memory network. Firstly, the photovoltaic power and meteorological data are decomposed by ensemble empirical mode decomposition. Secondly, key features in the meteorological subsequences are extracted by kernel principal component analysis method to eliminate the correlation and redundancy in the original meteorological sequence and reduce the input dimension of the model, then the coupling time characteristics between meteorological subsequences and photovoltaic power subsequence are further explored. Thirdly, a bidirectional long‐short‐term memory network optimized by improved particle swarm optimization based on inertia weight of anti‐sine function is proposed, and then the eventual photovoltaic power forecasting results are obtained by superimposing the predicted values of each prediction component. The experiment is carried out with the operation data of the Inner Mongolia power grid, and the results show that the proposed forecasting model exhibits higher prediction accuracy. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

show abstract

“…Given the high complexity and nonlinear characteristics of PV series, single algorithm prediction is still difficult. Signal decomposition techniques such as Wavelet Transform (WT) [20], Ensemble Empirical Mode Decomposition (EEMD) [21], Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) [22], and Variational Modal Decomposition (VMD) [23] are widely used in the field of PV power prediction. In paper [24], EEMD algorithm is used to decompose the weather sequence into components of different frequencies, which improves the prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

RSMD-RF-BGSkip Based PV Generation Prediction Method

Xie,

Lin

2024

IEEE Access

View full text Add to dashboard Cite

With the increasing use of photovoltaic (PV) power generation, power forecasting has become equally important to maintain stable and economic operation of the power system. However, the high frequency component of the PV power time series reduces the accuracy of the model predictions. Therefore, this paper proposes a short-term prediction model for PV power based on Row Secondary Modal Decomposition (RSMD), Random Forest (RF), and BGSkip neural network. Firstly, modal features with different complexity are obtained by RSMD. Secondly, RF is used to select different modal features. Then, to improve the prediction performance of the model, the BGSkip model employs a hybrid neural network to accurately predict the nonlinear part, while the linear part is handled by an autoregressive model. The prediction results of these two parts are integrated through the BGSkip model to output more accurate prediction values. Finally, the historical data of PV power plants in a region of Liaoning is utilized for experimental validation. The experimental results show that 2 R , MAPE E

show abstract

Short term PV power forecasting using empirical mode decomposition based orthogonal extreme learning machine technique

Cited by 6 publications

References 0 publications

Short-Term PV Power Prediction Based on Optimized VMD and LSTM

Short-Term PV Power Prediction Based on Optimized VMD and LSTM

A Novel Hybrid Deep Learning Model for Photovoltaic Power Forecasting Based on Feature Extraction and BiLSTM

RSMD-RF-BGSkip Based PV Generation Prediction Method

Contact Info

Product

Resources

About