A novel decomposition-ensemble prediction model for ultra-short-term wind speed

Energy Conversion and Management, Год журнала: 2021, Номер 248, С. 114775 - 114775

Опубликована: Окт. 1, 2021

Язык: Английский

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Multi-step short-term wind speed prediction based on integrated multi-model fusion

Applied Energy, Год журнала: 2021, Номер 298, С. 117248 - 117248

Опубликована: Июнь 18, 2021

Язык: Английский

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A comprehensive wind speed prediction system based on Monte Carlo and artificial intelligence algorithms

Applied Energy, Год журнала: 2021, Номер 305, С. 117815 - 117815

Опубликована: Сен. 15, 2021

Язык: Английский

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A combination forecasting model of wind speed based on decomposition

Energy Reports, Год журнала: 2021, Номер 7, С. 1217 - 1233

Опубликована: Фев. 21, 2021

Due to the intermittent, fluctuating and random characteristics of wind system, output power will become unstable with change wind, which brings severe challenges safe stable operation system. An effective way solve this problem is accurately forecast speed. This paper presents a novel speed combination forecasting model based on decomposition. The innovation as follows. (a) In view speed, variational mode decomposition algorithm introduced decompose historical data obtain series components different frequencies. (b) Echo state network good ability selected each component. (c) To that performance echo greatly affected by parameters reservoir, an improved whale optimization proposed optimize these parameters. optimized improves effect. (d) final results are obtained adding values (e) developed verified using two actual collected sets ultra-short-term short-term Compared some state-of-the-art models, comparison result curve between value error distribution, histogram indicators, related statistical Taylor diagram show has higher prediction accuracy able reflect laws correctly.

Язык: Английский

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Prediction of wastewater treatment quality using LSTM neural network

Environmental Technology & Innovation, Год журнала: 2021, Номер 23, С. 101632 - 101632

Опубликована: Май 20, 2021

Язык: Английский

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Approach for Short-Term Traffic Flow Prediction Based on Empirical Mode Decomposition and Combination Model Fusion

IEEE Transactions on Intelligent Transportation Systems, Год журнала: 2020, Номер 22(9), С. 5566 - 5576

Опубликована: Май 8, 2020

Accurate prediction of the traffic state can help to address issue congestion, providing guiding advices for people's travel and regulation. In this paper, we propose a novel short-term flow approach based on empirical mode decomposition combination model fusion. First, explore amplitude-frequency characteristics series, use decompose several components with different frequency. Second, results self-similarity analysis each component, improved extreme learning machine, seasonal auto regressive integrated moving average are selected predict components. Meanwhile, an fruit fly optimization algorithm is proposed optimize weight coefficient model. Third, multiplied by their respective get final results. We evaluate our doing thorough experiment real data set. Moreover, experimental show that has superior performance than state-of-the-art methods or models in prediction.

Язык: Английский

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Short-term wind speed interval prediction based on artificial intelligence methods and error probability distribution

Energy Conversion and Management, Год журнала: 2020, Номер 224, С. 113346 - 113346

Опубликована: Авг. 20, 2020

Язык: Английский

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Hybridization of hybrid structures for time series forecasting: a review

Artificial Intelligence Review, Год журнала: 2022, Номер 56(2), С. 1201 - 1261

Опубликована: Май 16, 2022

Язык: Английский

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Double-layer staged training echo-state networks for wind speed prediction using variational mode decomposition

Applied Energy, Год журнала: 2021, Номер 301, С. 117461 - 117461

Опубликована: Авг. 6, 2021

Due to the strong randomness of wind speed, power generation is difficult integrate into grid. It very important predict speed reliably and accurately so that energy can be utilized effectively. In this study, obtain accurate prediction results, a combined VMD-D-ESN model based on variational mode decomposition (VMD), double-layer staged training echo state network (D-ESN) genetic algorithm (GA) optimization proposed. First, preprocesses original data with VMD then uses D-ESN each decomposed subsequence. Lastly, final value obtained by combining all predicted subsequences. model's structure, first layer selects length set, second has ability correct error in layer. practical application case using six different collection sites, ten models are established compare performance proposed model. Compared other traditional models, results show combines structure achieves high accuracy stability available datasets. Additionally, also shows use strongly improves

Язык: Английский

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Natural phase space reconstruction-based broad learning system for short-term wind speed prediction: Case studies of an offshore wind farm

Energy, Год журнала: 2022, Номер 262, С. 125342 - 125342

Опубликована: Сен. 19, 2022

Язык: Английский

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