Short-Term Wind Power Forecasting Based on VMD Decomposition, ConvLSTM Networks and Error Analysis

Sun, Zexian; Zhao, Mingyu

doi:10.1109/access.2020.3011060

Cited by 83 publications

(23 citation statements)

References 27 publications

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“…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%

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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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“…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%

“…The work [33] proposed a Convolutional Long Short-Term Memory (Conv-LSTM) network for short-term WPF. Before the application of the Conv-LSTM, the Variational Mode Decomposition (VMD) was used in order to eliminate any non-stationary features of the raw data.…”

Section: Statistical Approaches Based On Artificial Intelligence Artificial Neural Networkmentioning

confidence: 99%

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

Bazionis

Georgilakis

2021

Electricity

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“…Lee et al [6] proposed to integrate three ensemble learning models, including boosted trees, random forest and generalized random forest, to realize the prediction of wind power. Sun et al [7] used VMD method to process historical power series, and then used LSTM to predict and fuse sub-sequences. Shi et al [8] proposed to use the wavelet decomposition method to process the time series of wind power and then uses SVM to predict wind power in the last day of a week.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Wind Power Prediction Based On Particle Swarm Optimization-Extreme Learning Machine Model Combined With Adaboost Algorithm

Jiang

Cao

et al. 2021

IEEE Access

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As the proportion of wind power in the world's electricity generation increases, improving wind power prediction accuracy is vital for making full use of wind energy and ensuring the safe and stable operation of the power grid. Given the uncertainty and volatility of wind power and the weak generalization ability of the current wind power prediction models, we propose a wind power prediction model that combines Adaboost algorithm with extreme learning machine optimized by particle swarm optimization (PSO-ELM). First, particle swarm optimization is used to optimize the initial thresholds and input weights of the ELM to obtain the PSO-ELM basic prediction model. Then, combined with the Adaboost algorithm, a series of PSO-ELM weak predictors with input weights and thresholds optimized by PSO and containing different hidden layer nodes are composed. Finally, each weak predictor is weighted and fused into a strong prediction model of wind power, and the final prediction results are output. In this paper, the Adaboost-PSO-ELM model is verified by a wind turbine's measured data in Turkey. The prediction indicators are compared with the current wind power prediction methods including optimized neural networks and ensemble learning models. The results show that the Adaboost-PSO-ELM wind power prediction model has higher accuracy and better generalization ability.

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“…Also, the decomposition of wind speed time series can make the input data more stationary. Once the stationary condition is met, the statistical models would generate competitive predictions [43]. Therefore, trained by the preprocessed data, the statistical models can learn the nonlinear behavior of the wind [1].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Wind Speed Forecasting Based on Hybrid MODWT-ARIMA-Markov Model

2021

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Markov chains (MC) are statistical models used to predict very short to short-term wind speed accurately. Such models are generally trained with a single moving window. However, wind speed time series do not possess an equal length of behavior for all horizons. Therefore, a single moving window can provide reasonable estimates but is not an optimal choice. In this study, a forecasting model is proposed that integrates MCs with an adjusting dynamic moving window. The model selects the optimal size of the window based on a similar approach to the leave-one-out method. The traditional model is further optimized by introducing a self-adaptive state categorization algorithm. Instead of synthetically generating time series, the modified model directly predicts one-step ahead wind speed. Initial results indicate that adjusting the moving window MC prediction model improved the forecasting performance of a single moving window approach by 50%. Based on preliminary findings, a novel hybrid model is proposed integrating maximal overlap discrete wavelet transform (MODWT) with auto-regressive integrated moving average (ARIMA) and adjusting moving window MC. It is evident from the literature that MC models are suitable for predicting residual sequences. However, MCs were not considered as a primary forecasting model for the decomposition-based hybrid approach in any wind forecasting studies. The improvement of the novel model is, on average, 55% for single deep learning models and 30% for decomposition-based hybrid models.

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Short-Term Wind Power Forecasting Based on VMD Decomposition, ConvLSTM Networks and Error Analysis

Cited by 83 publications

References 27 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

Short-Term Wind Power Prediction Based On Particle Swarm Optimization-Extreme Learning Machine Model Combined With Adaboost Algorithm

Short-Term Wind Speed Forecasting Based on Hybrid MODWT-ARIMA-Markov Model

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