Wind power prediction based on Elman neural network model optimized by improved genetic algorithm

Sun, Zihan; Liu, Yanlong; Xu, Mingyu; Guan, Wei

doi:10.1109/icbaie52039.2021.9389978

Cited by 9 publications

(5 citation statements)

References 3 publications

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“…Table 1. Synthesis of AI prediction methods [7], [8], [11], [13], [14], [18], [20], [25]- [31], [55]…”

Section: Resultsmentioning

confidence: 99%

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Review of current artificial intelligence methods and metaheuristic algorithms for wind power prediction

Haidi

Jaadi²

2023

IJEECS

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<p>Due to the insufficient fossil resources and the increasing environmental challenges, the world is heading for a more use-oriented to renewable energy sources, specifically to wind energy. A number of predictive techniques are available for the efficient use of wind energy. This article, which is a review of methods of artificial intelligence (AI) and meta-heuristic algorithms for wind energy prediction, fits into this context. There are two distinct categories: the first consists of traditional methods that are commonly used in this context, like different types of artificial neural networks (ANN), support vector machines (SVM) and fuzzy logic; the second is a combined approach which mixes the classic artificial intelligence methods and the meta-heuristic algorithms for the optimization of the forecast output. Then, a summary and comparison between the methodologies are established, and the advantages and limits of each technique are defined. The combination of the classic artificial intelligence and metaheuristic algorithms has a greater performance than the utilization of classic methods only. Nevertheless, using hybrid metaheuristic algorithms with classic artificial intelligence prediction methods can provide a higher precision.</p>

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“…Table 1. Synthesis of AI prediction methods [7], [8], [11], [13], [14], [18], [20], [25]- [31], [55]…”

Section: Resultsmentioning

confidence: 99%

“…Results show that the technique used is more accurate in comparison to the standard LSTM. Sun et al [14], an advanced genetic algorithm is presented for optimizing the Elman neural network model used for predicting wind energy. The suggested approach is validated through the comparison of the obtained results to the real values.…”

Section: Introductionmentioning

confidence: 99%

Review of current artificial intelligence methods and metaheuristic algorithms for wind power prediction

Haidi

Jaadi²

2023

IJEECS

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“…The Elman neural network was first proposed by Elman in 1990 for speech processing, which is a typical local regression network. Elman network can be regarded as a recurrent neural network with local memory unit and local feedback connection [27–30]. The main structure is feed‐forward connection, including input layer, hidden layer, and output layer, whose connection weight can be learned and modified.…”

Section: Multi Parameter Identification Based On Elman Neural Networkmentioning

confidence: 99%

Parameter identification of key components of thyristor converter valve based on wavelet packet key feature extraction and Elman neural network

Pang

Zhang

Wang

et al. 2022

IET Generation Trans & Dist

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Online health monitoring of thyristor converter valve is very important for the stable operation of high voltage direct current (HVDC) system. However, in practical projects, only offline annual maintenance can be realized. This paper proposes a method for parameter identification of thyristor valve based on wavelet packet key feature extraction and Elman neural network. By preprocessing the measurable characteristic parameters of thyristor valve in practical engineering, such as wavelet packet time-frequency domain feature extraction, variation coefficient optimization and so on, the characteristic vector that can characterize the health state of thyristor valve is obtained and sent to Elman neural network for training, which realizes the rapid online identification of multiple electrical parameters of thyristor modules in converter valve. Simulation and comparative experiments show that the key feature extraction method proposed in this paper has significant advantages in identification accuracy, and can better identify the damping capacitance and leakage resistance of all thyristor modules. Among them, the deterioration of the damping capacitance is more easily identified, the identification accuracy is the highest and the relative identification error is less than 5%. The parameter identification method proposed in this paper has the advantages of fast identification speed, strong model generalization ability and wide application. This provides a new idea for the online condition monitoring of thyristor valves.

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“…Reference [8] uses combined modal decomposition and a bidirectional long short-term memory neural network optimized by Bayes for prediction. Literature [9] improved the genetic algorithm, and used the improved genetic algorithm to optimize the topology structure and weight parameters of Elman neural network to improve the prediction effect of the network. Based on the traditional LSTM network, literature [10] adds a new gating mechanism to improve the context information modeling capability of LSTM network and improve the accuracy of wind power prediction.…”

Section: Introductionmentioning

confidence: 99%

Ultra-short term prediction of wind power based on multi-resolution closed-loop method

Zhang,

Liu

2024

J. Phys.: Conf. Ser.

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In order to improve the accuracy and stability of wind power prediction, a multi-resolution closed-loop wind power ultra-short term prediction method is proposed. Using historical data with different resolutions, the differential data is generated by the pre-predictor with low, medium and high resolutions, and then the differential data is combined with the post-prediction data into the post-predictor with three resolutions for training. Since the differential data is obtained from the prediction data with different resolutions, the post-prediction data containing the differential data contains inconsistent information between different resolutions, so that the post-predictor receives the inconsistency between different resolutions and minimizes the inconsistency after several closed-loop iterations, and finally outputs wind power prediction with different resolutions. At the end of the paper, simulation experiments are carried out using the data of the 2022 KDD Cup competition, and comparison is made with some mainstream wind power prediction models. It is found that the proposed model has high accuracy and stability.

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Wind power prediction based on Elman neural network model optimized by improved genetic algorithm

Cited by 9 publications

References 3 publications

Review of current artificial intelligence methods and metaheuristic algorithms for wind power prediction

Review of current artificial intelligence methods and metaheuristic algorithms for wind power prediction

Parameter identification of key components of thyristor converter valve based on wavelet packet key feature extraction and Elman neural network

Ultra-short term prediction of wind power based on multi-resolution closed-loop method

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