Filters








13,388 Hits in 6.7 sec

Bus Load Forecasting Method of Power System Based on VMD and Bi-LSTM

Jiajie Tang, Jie Zhao, Hongliang Zou, Gaoyuan Ma, Jun Wu, Xu Jiang, Huaixun Zhang
2021 Sustainability  
uses Bi-LSTM to improve the prediction accuracy of a single model.  ...  A bus load forecasting method based on variational modal decomposition (VMD) and bidirectional long short-term memory (Bi-LSTM) network was proposed in this article.  ...  neural network for long sequences.  ... 
doi:10.3390/su131910526 fatcat:ycche6dhlbg4lasrxfsimuslj4

A Variational Bayesian Deep Network with Data Self-Screening Layer for Massive Time-Series Data Forecasting

Xue-Bo Jin, Wen-Tao Gong, Jian-Lei Kong, Yu-Ting Bai, Ting-Li Su
2022 Entropy  
This paper proposes a deep network by selecting and understanding data to improve performance.  ...  gated recurrent unit (VBGRU) is used to improve the anti-noise ability and robustness of the model.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/e24030335 pmid:35327846 pmcid:PMC8947458 fatcat:cj5pg6dehndtheh6de5rldwfwq

Reliable optimisation control of a reactive polymer composite moulding process using ant colony optimisation and bootstrap aggregated neural networks

Kazim-Junior Rod Mohammed, Jie Zhang
2012 Neural computing & applications (Print)  
In order to overcome the difficulties in developing accurate mechanistic models for reactive polymer composite moulding processes, neural network models are developed from process operation data.  ...  In order to enhance the reliability of the optimisation control policy, model prediction confidence bound offered by bootstrap aggregated neural networks is incorporated in the optimisation objective function  ...  Fig. 5 . 5 Long range prediction SSE for bootstrap aggregated neural networks with various numbers of constituent networks The mean squared errors (MSE) of long-range predictions are calculated and shown  ... 
doi:10.1007/s00521-012-1273-y fatcat:lvpza6ny7bh27bk7doe2ilm4vm

Rain Fall Prediction using Data Mining Techniques with Modernistic Schemes and Well-Formed Ideas

2019 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
This paper contains some of the best work done in rain fall prediction using data mining techniques.  ...  There are large number of meteorologist all over the world who are trying their level best to predict the aspects of environment using data mining techniques.  ...  After critically analyzing the work we have suggested some of the modern approaches for each exiting model and work for improvement in both performance and accuracy.  ... 
doi:10.35940/ijitee.a4011.119119 fatcat:igoerzmvfzfq5b6wzyrcvd7fwy

A Probabilistic Ensemble Prediction Method for PV Power in the Nonstationary Period

Yuan An, Kaikai Dang, Xiaoyu Shi, Rong Jia, Kai Zhang, Qiang Huang
2021 Energies  
period of the data of photovoltaic output; in the deterministic point prediction module, a stacking- long-short-term memory neural network model is used for point forecasts; in the uncertainty interval  ...  To improve the interval prediction accuracy during the non-stationary periods of photovoltaic power, this paper proposes a probabilistic ensemble prediction model, which combines the modules of data preprocessing  ...  (a) BAYES neural network structure diagram; (b) Schematic diagram for the hidden layer of the Bayesian neural network. ( 1 ) 1 Input selection.  ... 
doi:10.3390/en14040859 fatcat:yz4lhhqdv5aynecxrr5wrzqfqu

A compound deep learning model for long range forecasting in electricity sale

Tao Tang, Yeqing Zhang, Wenjiang Feng
2021 International Journal of Low-Carbon Technologies  
recurrent neural network) for long range forecasting in electricity sale.  ...  Accurate prediction of electricity sale has a positive effect on power companies in rationally arranging power supply plans, scientifically optimizing power resource allocation, improving power management  ...  For example, the recurrent neural network (RNN) model [5] is a model suitable for processing sequence data.  ... 
doi:10.1093/ijlct/ctab028 fatcat:n3i6o3itpzfa5loic5szocu6gu

Energy Forecasting in Smart Grid Systems: A Review of the State-of-the-art Techniques [article]

Devinder Kaur, Shama Naz Islam, Md. Apel Mahmud, ZhaoYang Dong
2021 arXiv   pre-print
involve nonlinear patterns with long sequences.  ...  In addition, the significance of hybrid and data pre-processing techniques to support forecasting performance is also studied.  ...  dependencies (LRD) and neural networks are complex and lack interpretability, authors in [94] presented a simple yet efficient technique in which AR process is modeled using neural networks and named  ... 
arXiv:2011.12598v2 fatcat:ly2z32653zc2fap5garz6sfeom

A Review on Flood Prediction Algorithms and A Deep Neural Network Model for Estimation of Flood Occurrence

Tabassum Farhana Ullah, Gnana Prakasi O.S., Kanmani P
2020 International Research Journal of Multidisciplinary Technovation  
In addition, a design model has been proposed to predict the flood by training the Deep Neural Network with the above-mentioned factors.  ...  In this paper, we classify and analyzed the various prediction algorithms which show usage of Deep Neural Network produces better results.  ...  So, in this, a model for flood prediction based on Recurrent Neural Network is proposed.  ... 
doi:10.34256/irjmt2052 fatcat:lm5gwa7pujeb3dpgwzpump3334

Bayesian Optimization Algorithm-Based Statistical and Machine Learning Approaches for Forecasting Short-Term Electricity Demand

Nahid Sultana, S. M. Zakir Hossain, Salma Hamad Almuhaini, Dilek Düştegör
2022 Energies  
The coefficient of determination (R2) values for both models are >0.96.  ...  the model hyperparameters, (iii) hybridizing the BOA with the seasonal autoregressive integrated moving average with exogenous inputs (SARIMAX) and nonlinear autoregressive networks with exogenous input  ...  The values of R > 0.99 for all cases suggest a reliable and high predictive performance of the developed NARX neural network.  ... 
doi:10.3390/en15093425 fatcat:nfixpdgwb5cvzjlvtyvoefwsxy

Deep-Learning Prediction Model with Serial Two-Level Decomposition Based on Bayesian Optimization

Xue-Bo Jin, Hong-Xing Wang, Xiao-Yi Wang, Yu-Ting Bai, Ting-Li Su, Jian-Lei Kong
2020 Complexity  
However, it is challenging to predict the power load with a single model, especially for multistep prediction, because the time series load data have multiple periods.  ...  The results showed that the proposed prediction method could effectively improve the accuracy of power load prediction.  ...  Kollia and Kollias [23] proposed using deep convolution-recursive neural networks to process data in time series or two-dimensional information to improve prediction accuracy. Yin et al.  ... 
doi:10.1155/2020/4346803 fatcat:i2ui33pnn5eqzimacc6mr5n4ka

On the Ensemble of Recurrent Neural Network for Air Pollution Forecasting: Issues and Challenges

Ola Surakhi, Sami Serhan, Imad Salah
2020 Advances in Science, Technology and Engineering Systems  
Designing an ensemble model of recurrent neural network for time-series forecasting applications would enhance prediction accuracy and improve performance.  ...  Due to variant improvements on recurrent neural networks, choosing of the best model for better prediction generation is dependent on problem domain and model design characteristics.  ...  A successful approach to helping in the selection of the best algorithm that will improve the prediction performance and reduce variance is to train multiple models and then combine the prediction of these  ... 
doi:10.25046/aj050265 fatcat:4mphm75o3jg3notvcn6lz2p4oi

Improving Nonlinear Process Modelling Through Selective Combination of Multiple Neural Networks using Combined Correlation Coefficient Analysis

Zainal Ahmad, Rabiatul 'Adawiyah Mat Noor
2008 Jurnal Teknologi  
The result shows that combination multiple neural networks using the proposed approach improved the performance of the two nonlinear process modelling case studies in which there is a significant reduction  ...  The main objective of the proposed approach is to improve the generalisation capability of the neural network models by combining networks that are less correlated.  ...  INTRODUCTION Artificial neural networks have been used in developing non-linear models in industry for such a long time [1] and robustness of the model is one of the main criteria that need to be considered  ... 
doi:10.11113/jt.v48.237 fatcat:rsgwn4ivubcf7a2edp6fjghg4e

OCLSTM: Optimized convolutional and long short-term memory neural network model for protein secondary structure prediction

Yawu Zhao, Yihui Liu, Alexandre G. de Brevern
2021 PLoS ONE  
In this paper, we apply an optimized convolutional neural network and long short-term memory neural network models to protein secondary structure prediction, which is called OCLSTM.  ...  Then use the bidirectional long short-term memory neural network to extract the remote interactions between the internal residues of the protein sequence to predict the protein structure.  ...  To solve this problem, in this paper we used Bayesian optimization convolutional neural network was combined with long short-term memory neural network models for the prediction of protein secondary structure  ... 
doi:10.1371/journal.pone.0245982 pmid:33534819 fatcat:u3vx5behzrgideou6tlpadlfja

Multi-Regional Online Car-Hailing Order Quantity Forecasting Based on the Convolutional Neural Network

Zihao Huang, Gang Huang, Zhijun Chen, Chaozhong Wu, Xiaofeng Ma, Haobo Wang
2019 Information  
including the Bayesian Ridge Model, Linear Regression, Support Vector Regression, and Long Short-Term Memory networks.  ...  Taking advantage of the convolutional neural network in image feature extraction, the historical demand data of the first twenty-five minutes of the entire region are used as a model input to predict the  ...  Acknowledgments: The authors would like to thank Didi Chuxing for its support in data provision. Data were retrieved from Didi Chuxing through https://gaia.didichuxing.com.  ... 
doi:10.3390/info10060193 fatcat:aboifqd7mvhdpl4tidp4krlfbi

Bayesian Long Short-Term Memory Model for Fault Early Warning of Nuclear Power Turbine

Gaojun Liu, Haixia Gu, Xiaocheng Shen, Dongdong You
2020 IEEE Access  
This paper presents a Bayesian Long Short-Term Memory (LSTM) neural network method for fault early warning method of nuclear power turbine.  ...  The Long Short-Term Memory neural network prediction model is developed to address data uncertainty while taking into account complicated situation of the equipment operation.  ...  [24] proposed a hybrid prediction modeling strategy by combining the autocorrelation local characteristic-scale decomposition and the improved LSTM neural network. Zhang et al.  ... 
doi:10.1109/access.2020.2980244 fatcat:k6xtvbn6xrcutkppzlzuthj4uq
« Previous Showing results 1 — 15 out of 13,388 results