Application of hybrid model based on empirical mode decomposition, novel recurrent neural networks and the ARIMA to wind speed prediction

Energy Conversion and Management, Год журнала: 2021, Номер 233, С. 113917 - 113917

Опубликована: Фев. 25, 2021

Язык: Английский

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Machine Learning and Deep Learning in Energy Systems: A Review

Sustainability, Год журнала: 2022, Номер 14(8), С. 4832 - 4832

Опубликована: Апрель 18, 2022

With population increases and a vital need for energy, energy systems play an important decisive role in all of the sectors society. To accelerate process improve methods responding to this increase demand, use models algorithms based on artificial intelligence has become common mandatory. In present study, comprehensive detailed study been conducted applications Machine Learning (ML) Deep (DL), which are newest most practical Artificial Intelligence (AI) systems. It should be noted that due development DL algorithms, usually more accurate less error, these ability model solve complex problems field. article, we have tried examine very powerful problem solving but received attention other studies, such as RNN, ANFIS, RBN, DBN, WNN, so on. This research uses knowledge discovery databases understand ML systems’ current status future. Subsequently, critical areas gaps identified. addition, covers efficient used field; optimization, forecasting, fault detection, investigated. Attempts also made cover their evaluation metrics, including not only important, newer ones attention.

Язык: Английский

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Integrated framework of extreme learning machine (ELM) based on improved atom search optimization for short-term wind speed prediction

Energy Conversion and Management, Год журнала: 2021, Номер 252, С. 115102 - 115102

Опубликована: Дек. 14, 2021

Язык: Английский

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Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects

Sustainability, Год журнала: 2023, Номер 15(9), С. 7087 - 7087

Опубликована: Апрель 23, 2023

This article presents a review of current advances and prospects in the field forecasting renewable energy generation using machine learning (ML) deep (DL) techniques. With increasing penetration sources (RES) into electricity grid, accurate their becomes crucial for efficient grid operation management. Traditional methods have limitations, thus ML DL algorithms gained popularity due to ability learn complex relationships from data provide predictions. paper reviews different approaches models that been used discusses strengths limitations. It also highlights challenges future research directions field, such as dealing with uncertainty variability generation, availability, model interpretability. Finally, this emphasizes importance developing robust enable integration RES facilitate transition towards sustainable future.

Язык: Английский

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Wind speed prediction using a hybrid model of EEMD and LSTM considering seasonal features

Energy Reports, Год журнала: 2022, Номер 8, С. 8965 - 8980

Опубликована: Июль 14, 2022

As a clean and renewable energy source, wind power is of great significance for addressing global shortages environmental pollution. However, the uncertainty speed hinders direct use power, resulting in high proportion abandoned wind. Therefore, accurate prediction improving utilization rate energy. In this study, hybrid model proposed based on seasonal autoregressive integrated moving average (SARIMA), ensemble empirical mode decomposition (EEMD), long short-term memory (LSTM) methods. First, original data were resampled to obtain within time scales 15, 30, 60 min. The SARIMA was used extract linear features nonlinear residual sequences series at different scales, EEMD decompose sequence intrinsic functions (IMFs) sub-residual sequences. For IMFs obtained after decomposition, LSTM method training, predicted IMFs, sequence, series, final speed. To verify superiority large farm as case study. Finally, compared with other models, verifying that experimental has higher accuracy.

Язык: Английский

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Multi-step short-term wind speed forecasting based on multi-stage decomposition coupled with stacking-ensemble learning approach

International Journal of Electrical Power & Energy Systems, Год журнала: 2022, Номер 143, С. 108504 - 108504

Опубликована: Авг. 2, 2022

Язык: Английский

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Machine-learning predictions of solubility and residual trapping indexes of carbon dioxide from global geological storage sites

Expert Systems with Applications, Год журнала: 2023, Номер 222, С. 119796 - 119796

Опубликована: Март 8, 2023

Язык: Английский

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A novel hybrid model based on GA-VMD, sample entropy reconstruction and BiLSTM for wind speed prediction

Measurement, Год журнала: 2023, Номер 222, С. 113643 - 113643

Опубликована: Окт. 14, 2023

Язык: Английский

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MMKE: Multi-trial vector-based monkey king evolution algorithm and its applications for engineering optimization problems

PLoS ONE, Год журнала: 2023, Номер 18(1), С. e0280006 - e0280006

Опубликована: Янв. 3, 2023

Monkey king evolution (MKE) is a population-based differential evolutionary algorithm in which the single strategy and control parameter affect convergence balance between exploration exploitation. Since strategies have considerable impact on performance of algorithms, collaborating multiple can significantly enhance abilities algorithms. This our motivation to propose multi-trial vector-based monkey named MMKE. It introduces novel best-history trial vector producer (BTVP) random (RTVP) that effectively collaborate with canonical MKE (MKE-TVP) using approach tackle various real-world optimization problems diverse challenges. expected proposed MMKE improve global search capability, strike exploitation, prevent original from converging prematurely during process. The was assessed CEC 2018 test functions, results were compared eight metaheuristic As result experiments, it demonstrated capable producing competitive superior terms accuracy rate comparison comparative Additionally, Friedman used examine gained experimental statistically, proving Furthermore, four engineering design optimal power flow (OPF) problem for IEEE 30-bus system are optimized demonstrate MMKE's real applicability. showed handle difficulties associated able solve multi-objective OPF better solutions than

Язык: Английский

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A novel DWTimesNet-based short-term multi-step wind power forecasting model using feature selection and auto-tuning methods

Energy Conversion and Management, Год журнала: 2024, Номер 301, С. 118045 - 118045

Опубликована: Янв. 5, 2024

Язык: Английский

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