A Novel Deep Learning Approach for Wind Power Forecasting Based on WD-LSTM Model

Liu, Bingchun; Zhao, Shijie; Yu, Xiaogang; Zhang, Lei; Wang, Qingshan

doi:10.3390/en13184964

Cited by 47 publications

(16 citation statements)

References 41 publications

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“…In the work [45], a hybrid LSTM neural network along with the Wavelet Decomposition (WD-LSTM) was used for wind power forecasting. The proposed model was compared to the BMA-EL, the MRMLE-AMS and the SVR-IDA.…”

Section: Comparative Results Of Reviewed Workmentioning

confidence: 99%

“…Reference ARMA [17][18][19][20][21][22][23] ARIMA [24,25] Grey Method [26][27][28] ANN [29][30][31][32][33] SVM [34][35][36][37][38][39] Hybrid [40][41][42][43][44][45][46][47]…”

Section: Approachmentioning

confidence: 99%

“…In the work [45], a hybrid Long Short-Term Memory (LSTM) neural network along with the Wavelet Decomposition (WD-LSTM) was used for wind power forecasting in China. The time series of the macroeconomic indicators as well as the related power generation indicators were decomposed with the WD into low-frequency and high-frequency components.…”

Section: Hybrid Approachmentioning

confidence: 99%

See 2 more Smart Citations

Review of Deterministic and Probabilistic Wind Power Forecasting: Models, Methods, and Future Research

Bazionis

Georgilakis

2021

Electricity

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The need to turn to more environmentally friendly sources of energy has led energy systems to focus on renewable sources of energy. Wind power has been a widely used source of green energy. However, the wind’s stochastic and unpredictable behavior has created several challenges to the operation and stability of energy systems. Forecasting models have been developed and excessively used in recent decades in order to deal with these challenges. Deterministic forecasting models have been the main focus of researchers and are still being developed in order to improve their accuracy. Furthermore, in recent years, in order to observe and study the uncertainty of forecasts, probabilistic forecasting models have been developed in order to give a wider view of the possible prediction outcomes. Advanced probabilistic and deterministic forecasting models could be used in order to facilitate the energy systems operation and energy markets management. This paper introduces an overview of state-of-the-art wind power deterministic and probabilistic models, developing a comparative evaluation between the different models reviewed, identifying their advantages and disadvantages, classifying and analyzing current and future research directions in this area.

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Section: Comparative Results Of Reviewed Workmentioning

confidence: 99%

“…Reference ARMA [17][18][19][20][21][22][23] ARIMA [24,25] Grey Method [26][27][28] ANN [29][30][31][32][33] SVM [34][35][36][37][38][39] Hybrid [40][41][42][43][44][45][46][47]…”

Section: Approachmentioning

confidence: 99%

Section: Hybrid Approachmentioning

confidence: 99%

See 1 more Smart Citation

Review of Deterministic and Probabilistic Wind Power Forecasting: Models, Methods, and Future Research

Bazionis

Georgilakis

2021

Electricity

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show abstract

“…In general, the baseline model is easy to implement forecasting modeling and naive of problems-specific details. For example, the persistence model can quickly, simply and repeatable calculate the corresponding expected output based on the current input, so as to effectively measure the reliability and effectiveness of the forecasting model currently established [150][151][152][153][154][155][156][157]. Multiple vector regression analysis (Multi-SVR) is a common kind of time-series forecasting model, which can use statistical methods to determine the quantitative relationship of the interdependence between multiple variables [158][159][160].…”

Section: The Short-term Wind Power Forecasting Based On the Hidden-layers Topology Analysismentioning

confidence: 99%

The Hidden-Layers Topology Analysis of Deep Learning Models in Survey for Forecasting and Generation of theWind Power and Photovoltaic Energy

Xu¹,

Shao²,

Deng³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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As wind and photovoltaic energy become more prevalent, the optimization of power systems is becoming increasingly crucial. The current state of research in renewable generation and power forecasting technology, such as wind and photovoltaic power (PV), is described in this paper, with a focus on the ensemble sequential LSTMs approach with optimized hidden-layers topology for short-term multivariable wind power forecasting. The methods for forecasting wind power and PV production. The physical model, statistical learning method, and machine learning approaches based on historical data are all evaluated for the forecasting of wind power and PV production. Moreover, the experiments demonstrated that cloud map identification has a significant impact on PV generation. With a focus on the impact of photovoltaic and wind power generation systems on power grid operation and its causes, this paper summarizes the classification of wind power and PV generation systems, as well as the benefits and drawbacks of PV systems and wind power forecasting methods based on various typologies and analysis methods.

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“…Zhu et al proposed a multivariate method for ultra-short-term wind power forecasting based on long short-term memory (LSTM) to forecast the ultra-short-term wind power [19]. As the algorithm has its distinct advantages and disadvantages, some works about utilizing hybrid deep learning algorithms were also discussed in [20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Modeling of Ultra-Short Term Offshore Wind Power Prediction Based on Condition-Assessment of Wind Turbines

Huang

Liu

et al. 2021

Energies

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More accurate wind power prediction (WPP) is of great significance for the operation of electrical power systems, as offshore wind power penetration increases continuously. As the offshore wind turbines (OWT) are a key system in converting offshore wind power into electrical power, maintaining their condition plays a pivotal role in WPP. However, it is seldom considered in traditional WPP. This paper proposes an ultra-short term offshore WPP methodology based on the condition assessment (CA) of OWTs. Firstly, a modified fuzzy comprehensive evaluation (MFCE) based CA of the OWT is presented with a new defined deterioration of indicators calculated by the relative errors. Long short-term memory (LSTM) neural network is introduced to deal with the complicated interactions between the various monitoring data of an OWT and the dynamic marine environment. Then, with the classifications of the health conditions of the OWT, the historical operation data is classified accordingly. An OWT-condition based WPP with a backpropagation (BP) neural network is developed to deal with the non-linear mapping relations between the numerical weather prediction (NWP) information, health conditions of OWT, and the output power. The results of the case study show the influences of the OWT health conditions to its output power and verifies the effectiveness and higher accuracy of the proposed method.

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A Novel Deep Learning Approach for Wind Power Forecasting Based on WD-LSTM Model

Cited by 47 publications

References 41 publications

Review of Deterministic and Probabilistic Wind Power Forecasting: Models, Methods, and Future Research

Review of Deterministic and Probabilistic Wind Power Forecasting: Models, Methods, and Future Research

The Hidden-Layers Topology Analysis of Deep Learning Models in Survey for Forecasting and Generation of theWind Power and Photovoltaic Energy

Modeling of Ultra-Short Term Offshore Wind Power Prediction Based on Condition-Assessment of Wind Turbines

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