A Combined Method of Improved Grey BP Neural Network and MEEMD-ARIMA for Day-Ahead Wave Energy Forecast

Wu, Feng; Jing, Rui; Zhang, Xiaoping; Wang, Fei; Bao, Yifan

doi:10.1109/tste.2021.3096554

Cited by 44 publications

(12 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wu et al proposed a wave energy prediction model using an improved BPNN model and an improved ensemble empirical mode decomposition. The wave energy is predicted by this model, and finally, the validity of the prediction model is verified by using the actual measured wind waves of the real ocean as an example [ 7 ]. Wu et al decomposed the original wind speed sequence into a set of intrinsic mode functions using variational mode decomposition and then combined the high-performance multiview prediction and interpretable temporal dynamic insight based on the attentional deep learning model.…”

Section: Introductionmentioning

confidence: 99%

BDS-3 Broadcast Ephemeris Orbit Correction Model Based on Improved PSO Combined with BP Neural Network

Peng

Liu

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

During the operation of navigation satellites, errors in the broadcast ephemeris orbits are caused by the influence of ingress factors and other factors. To address this phenomenon, this paper examines the use of the computational intelligence (CI) methods to implement track correction and to develop an optimized BP neural network model based on an improved particle swarm algorithm. The model improves the inertia weights and learning factor parameters of the particle swarm optimization (PSO) algorithm to improve the global optimization capability and accelerate the convergence speed. The improved PSO (IPSO) algorithm is used to perform a global optimization search for the hyperparameters of the BP neural network, and then the neural network model is trained by broadcast ephemeris Keplerian root number and regression parameters. The model is validated using the broadcast ephemeris data of the BDS-3 MEO and IGSO satellites, and the mean square error correction rate of multiple satellites with different correction models shows that the error correction rate of the IPSO-BPNN model can reach 70.2–84% and the error correction rate can be improved by 14.2–56.8% compared with the PSO-BPNN model. The proposed algorithm provides an important reference for BDS-3 and other global navigation satellite systems for improving the accuracy of satellite orbit determination.

show abstract

Section: Introductionmentioning

confidence: 99%

BDS-3 Broadcast Ephemeris Orbit Correction Model Based on Improved PSO Combined with BP Neural Network

Peng

Liu

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Following that, the authors in [24] have introduced a self-recurrent wavelet NN control approach for WECS and it has local self-feedback loops in a self-recurrent wavelet neural network, which give the memory function and the essential knowledge of historical signal values. The authors in [25] have proposed an upgraded gray BP NN and a modified ensemble empirical mode decomposition auto-regressive integrated moving average for real-time wind speed estimation. However, the aforementioned control algorithms are dependent on the wind measuring sensors, and it takes much time to collect the wind data.…”

Section: Introductionmentioning

confidence: 99%

Reference Model Adaptive Control Scheme on PMVG-Based WECS for MPPT under a Real Wind Speed

2022

View full text Add to dashboard Cite

Over the last few years, improving power extraction from the wind energy conversion system (WECS) under varying wind speeds has become a complex task. The current study presents the optimum maximum power point tracking (MPPT) control approach integrated with neural network (NN)-based rotor speed control and pitch angle control to extract the maximum power from the WECS. So, this study presents a reference model adaptive control (RMAC) for a direct-drive (DD) permanent magnet vernier generator (PMVG)-based WECS under real wind speed conditions. Initially, the RMAC-based rotor speed tracking control is presented with adaptive terms, which tracks a reference model that guarantees the expected exponential decay of rotor speed error trajectory. Then, to reduce the wind speed measurement errors, a recurrent neural network (RNN)-based training model is presented. Moreover, the asymptotic stability of the proposed control method is mathematically proven by Lyapunov theory. In addition, the pitch angle control is presented to efficiently operate the rotor speed within the allowable operating range. Eventually, the proposed control system demonstrates its effectiveness through simulation and experimentation using a prototype of 5 kW DD PMVG-based WECS. After that, the comparative results affirm the superiority of the proposed control method over existing control methods.

show abstract

“…With the development of information technology [7][8][9][10][11], artificial intelligence technology has been gradually applied in various industries [12][13][14][15][16]. Because BP neural network [17][18][19] has strong learning ability and nonlinear fitting ability; it has been empirically applied to the field of investment potential analysis of integrated energy projects. In this paper, RBF-BP neural network is proposed and used to calculate the weight of investment potential evaluation index of integrated energy project.…”

Section: Introductionmentioning

confidence: 99%

Design of Investment Potential Analysis Model of Integrated Energy Project Based on Deep Learning Neural Network

Huang

Fan

et al. 2022

Scientific Programming

View full text Add to dashboard Cite

Under the background of energy conservation and emission reduction and large-scale promotion of electric energy substitution, fully exploring the complementary potential of various energy systems and realizing the optimization of comprehensive energy utilization are the most critical development goals of the current energy system. The key to achieving this goal is the investment potential analysis of integrated energy projects. In order to effectively solve the problems of difficult scientific determination of evaluation index weight and low accuracy of evaluation results in the analysis of investment potential of integrated energy projects, an investment potential analysis model of integrated energy project based on deep learning neural network is designed. The design process of the integrated energy project is summarized. The RBF-BP neural network model is established to obtain the correlation between the factors of the evaluation unit, further analyze and process the training results, and calculate the weight of the evaluation index. The obtained weight is substituted into the TOPSIS comprehensive evaluation model for the investment potential analysis of integrated energy projects. According to the investment potential analysis results, the investment potential analysis value of energy performance contracting (EPC) mode is 0.9122, which is the best operation mode. The results show that the analysis results reflect the investment potential of integrated energy projects more objectively and scientifically.

show abstract

A Combined Method of Improved Grey BP Neural Network and MEEMD-ARIMA for Day-Ahead Wave Energy Forecast

Cited by 44 publications

References 26 publications

BDS-3 Broadcast Ephemeris Orbit Correction Model Based on Improved PSO Combined with BP Neural Network

BDS-3 Broadcast Ephemeris Orbit Correction Model Based on Improved PSO Combined with BP Neural Network

Reference Model Adaptive Control Scheme on PMVG-Based WECS for MPPT under a Real Wind Speed

Design of Investment Potential Analysis Model of Integrated Energy Project Based on Deep Learning Neural Network

Contact Info

Product

Resources

About