A new hybrid model for wind speed forecasting combining long short-term memory neural network, decomposition methods and grey wolf optimizer

Altan, Aytaç; Karasu, Seçkin; Zio, Enrico

doi:10.1016/j.asoc.2020.106996

Cited by 472 publications

(121 citation statements)

References 82 publications

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“…Research studies have long focused on the energy consumption prediction of urban rail transit. Prevailing prediction methods include multivariate linear regression method [3], artificial neural network method [4][5][6], support vector machine [7,8], genetic algorithm [9], grey theory method [10,11], and time series method [12].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] A Method for Short‐Term Prediction of the Metro Station’s Individual Energy Consumption Item Based on G‐ACO‐BP Model

Sha¹,

Qian²

2021

Computational Intelligence and Neuroscience

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This paper proposes a new method to make short-term predictions for the three kinds of primary energy consumption of power, lighting, and ventilated air conditioning in the metro station. First, the paper extracts the five main factors influencing metro station energy consumption through the kernel principal component analysis (KPCA). Second, improved genetic-ant colony optimization (G-ACO) was fused into the BP neural network to train and optimize the connection weights and thresholds between each BP neural network layer. The paper then builds a G-ACO-BP neural model to make short-term predictions about different energy consumption in the metro station to predict the energy consumed by power, lighting, and ventilated air conditioning. The experimental results showed that the G-ACO-BP neural model could give a more accurate and effective prediction for the main energy consumption in a metro station.

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Section: Introductionmentioning

confidence: 99%

[Retracted] A Method for Short‐Term Prediction of the Metro Station’s Individual Energy Consumption Item Based on G‐ACO‐BP Model

Sha¹,

Qian²

2021

Computational Intelligence and Neuroscience

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“…Due to an incomplete knowledge of COVID-19 during the early stages, scientists used existing models to forecast the pandemic and made inaccurate predictions. Fortunately, based on historical data, people can still make relatively accurate predictions by applying some kinds of model-free methods [ 2 , 3 ]. However, to generate policy-relevant insights into the nonpharmaceutical interventions, people still need to understand the physical principles of the pandemic.…”

Section: Introductionmentioning

confidence: 99%

A stochastic SEIHR model for COVID-19 data fluctuations

et al. 2021

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Although deterministic compartmental models are useful for predicting the general trend of a disease’s spread, they are unable to describe the random daily fluctuations in the number of new infections and hospitalizations, which is crucial in determining the necessary healthcare capacity for a specified level of risk. In this paper, we propose a stochastic SEIHR (sSEIHR) model to describe such random fluctuations and provide sufficient conditions for stochastic stability of the disease-free equilibrium, based on the basic reproduction number that we estimated. Our extensive numerical results demonstrate strong threshold behavior near the estimated basic reproduction number, suggesting that the necessary conditions for stochastic stability are close to the sufficient conditions derived. Furthermore, we found that increasing the noise level slightly reduces the final proportion of infected individuals. In addition, we analyze COVID-19 data from various regions worldwide and demonstrate that by changing only a few parameter values, our sSEIHR model can accurately describe both the general trend and the random fluctuations in the number of daily new cases in each region, allowing governments and hospitals to make more accurate caseload predictions using fewer compartments and parameters than other comparable stochastic compartmental models.

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“…Besides traditional AI/ML models, deep learning and extreme learning machines are also commonly applied in wind speed forecasting. Notable architectures include Kernel Extreme Learning Machine (KELM) [13,14], Long Short-Term Memory (LSTM) [15,16], Echo State Network [17], Deep Belief Network (DBN) [18,19], and Convolutional Neural Network (CNN) [20,21].…”

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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A new hybrid model for wind speed forecasting combining long short-term memory neural network, decomposition methods and grey wolf optimizer

Cited by 472 publications

References 82 publications

[Retracted] A Method for Short‐Term Prediction of the Metro Station’s Individual Energy Consumption Item Based on G‐ACO‐BP Model

[Retracted] A Method for Short‐Term Prediction of the Metro Station’s Individual Energy Consumption Item Based on G‐ACO‐BP Model

A stochastic SEIHR model for COVID-19 data fluctuations

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

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