Wave power forecasting using an effective decomposition-based convolutional Bi-directional model with equilibrium Nelder-Mead optimiser

Neshat, Mehdi; Nezhad, Meysam Majidi; Sergiienko, Nataliia Y.; Mirjalili, Seyedali; Piras, Giuseppe; Garcia, Davide Astiaso

doi:10.1016/j.energy.2022.124623

Cited by 25 publications

(4 citation statements)

References 49 publications

(66 reference statements)

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“…(c) In a comparison of the MVMD-CLES model to the published VMD-CNNBiLS-TM [42], it was shown that the present system optimized the VMD decomposition using a multi-objective strategy whale optimization algorithm, combining the CNN, LSTM, and BiLSTM integrated networks, obtaining better metrics than the former in most data sets. The EEMD(Modify)-LSTM prediction network was constructed in combination with the constructive error correction network proposed in the literature [43].…”

Section: Seasonmentioning

confidence: 91%

A Photovoltaic Power Prediction Approach Based on Data Decomposition and Stacked Deep Learning Model

et al. 2023

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Correctly anticipating PV electricity production may lessen stochastic fluctuations and incentivize energy consumption. To address the intermittent and unpredictable nature of photovoltaic power generation, this article presents an ensemble learning model (MVMD-CLES) based on the whale optimization algorithm (WOA), variational mode decomposition (VMD), convolutional neural network (CNN), long and short-term memory (LSTM), and extreme learning machine (ELM) stacking. Given the variances in the spatiotemporal distribution of photovoltaic data and meteorological features, a multi-branch character extraction iterative mixture learning model is proposed: we apply the MWOA algorithm to find the optimal decomposition times and VMD penalty factor, and then divide the PV power sequences into sub-modes with different frequencies using a two-layer algorithmic structure to reconstruct the obtained power components. The primary learner is CNN–BiLSTM, which is utilized to understand the temporal and spatial correlation of PV power from information about the weather and the output of photovoltaic cells, and the LSTM learns the periodicity and proximity correlation of the power data and obtains the corresponding component predictions. The second level is the secondary learner—the output of the first layer is learned again using the ELM to attenuate noise and achieve short-term prediction. In different case studies, regardless of weather changes, the proposed method is provided with the best group of consistency and constancy, with an average RMSE improvement of 12.08–39.14% over a single-step forecast compared to other models, the average forecast RMSE increased by 5.71–9.47% for the first two steps.

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Section: Seasonmentioning

confidence: 91%

A Photovoltaic Power Prediction Approach Based on Data Decomposition and Stacked Deep Learning Model

et al. 2023

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“…If the path is correct, it can enclose the optimal solution, leading to rapid convergence. However, a drawback of this method arises when there are too many optimal solutions within the problem area, thus making it challenging to find the proper optimal solution and susceptibility to local optima [ 17 ]. The NM method first evaluates each point by substituting them into the evaluation function ff, thereby ranking them accordingly to find the best point Plow, the second-best point Psec hi, the worst point Phigh, and the centroid Pcent among them.…”

Section: Methodologies and System Descriptionmentioning

confidence: 99%

Application of Independent Component Analysis and Nelder–Mead Particle Swarm Optimization Algorithm in Non-Contact Blood Pressure Estimation

Su,

Lin,

Zhuang

et al. 2024

Sensors

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In recent years, hypertension has become one of the leading causes of illness and death worldwide. Changes in lifestyle among the population have led to an increasing prevalence of hypertension. This study proposes a non-contact blood pressure estimation method that allows patients to conveniently monitor their blood pressure values. By utilizing a webcam to track facial features and the region of interest (ROI) for obtaining forehead images, independent component analysis (ICA) is employed to eliminate artifact signals. Subsequently, physiological parameters are calculated using the principle of optical wave reflection. The Nelder–Mead (NM) simplex method is combined with the particle swarm optimization (PSO) algorithm to optimize the empirical parameters, thus enhancing computational efficiency and accurately determining the optimal solution for blood pressure estimation. The influences of light intensity and camera distance on the experimental results are also discussed. Furthermore, the measurement time is only 10 s. The superior accuracy and efficiency of the proposed methodology are demonstrated by comparing them with those in other published literature.

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“…For the 1,440 min sequence length, the average R2 of TVF-EMD-ENN was 0.952, higher than those of WT-ENN (0.910) and CEEMD-ENN (0.929). A hybrid prediction model was developed for wind and wave power prediction, which was based on adaptive decomposition (Nelder-Mead variational mode decomposition) and a convolutional neural network with bi-directional long shortterm memory (Neshat et al, 2022). Meng et al (2022) In summary, signal decomposition algorithms enhance the model's ability to handle non-stationary signals.…”

Section: Signal Decomposition Hybrid Modelmentioning

confidence: 99%

A review of artificial intelligence in marine science

Song

Pang²,

Hou³

et al. 2023

Front. Earth Sci.

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Utilization and exploitation of marine resources by humans have contributed to the growth of marine research. As technology progresses, artificial intelligence (AI) approaches are progressively being applied to maritime research, complementing traditional marine forecasting models and observation techniques to some degree. This article takes the artificial intelligence algorithmic model as its starting point, references several application trials, and methodically elaborates on the emerging research trend of mixing machine learning and physical modeling concepts. This article discusses the evolution of methodologies for the building of ocean observations, the application of artificial intelligence to remote sensing satellites, smart sensors, and intelligent underwater robots, and the construction of ocean big data. We also cover the method of identifying internal waves (IW), heatwaves, El Niño-Southern Oscillation (ENSO), and sea ice using artificial intelligence algorithms. In addition, we analyze the applications of artificial intelligence models in the prediction of ocean components, including physics-driven numerical models, model-driven statistical models, traditional machine learning models, data-driven deep learning models, and physical models combined with artificial intelligence models. This review shows the growth routes of the application of artificial intelligence in ocean observation, ocean phenomena identification, and ocean elements forecasting, with examples and forecasts of their future development trends from several angles and points of view, by categorizing the various uses of artificial intelligence in the ocean sector.

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Wave power forecasting using an effective decomposition-based convolutional Bi-directional model with equilibrium Nelder-Mead optimiser

Cited by 25 publications

References 49 publications

A Photovoltaic Power Prediction Approach Based on Data Decomposition and Stacked Deep Learning Model

A Photovoltaic Power Prediction Approach Based on Data Decomposition and Stacked Deep Learning Model

Application of Independent Component Analysis and Nelder–Mead Particle Swarm Optimization Algorithm in Non-Contact Blood Pressure Estimation

A review of artificial intelligence in marine science

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