scholarly journals PV Power Prediction, Using CNN-LSTM Hybrid Neural Network Model. Case of Study: Temixco-Morelos, México

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6512
Author(s):  
Mario Tovar ◽  
Miguel Robles ◽  
Felipe Rashid
Keyword(s):  
Neural Network ◽  
Hybrid Model ◽  
Network Model ◽  
Neural Network Model ◽  
Short Term Memory ◽  
Lasso Regression ◽  
Neural Network Models ◽  
Model Case ◽  
Hybrid Neural Network ◽  
Photovoltaic Power

Due to the intermittent nature of solar energy, accurate photovoltaic power predictions are very important for energy integration into existing energy systems. The evolution of deep learning has also opened the possibility to apply neural network models to predict time series, achieving excellent results. In this paper, a five layer CNN-LSTM model is proposed for photovoltaic power predictions using real data from a location in Temixco, Morelos in Mexico. In the proposed hybrid model, the convolutional layer acts like a filter, extracting local features of the data; then the temporal features are extracted by the long short-term memory network. Finally, the performance of the hybrid model with five layers is compared with a single model (a single LSTM), a CNN-LSTM hybrid model with two layers and two well known popular benchmarks. The results also shows that the hybrid neural network model has better prediction effect than the two layer hybrid model, the single prediction model, the Lasso regression or the Ridge regression.

Genetic algorithms and hybrid neural network modelling for aluminium stress—strain prediction

2003 ◽  
Vol 217 (1) ◽  
pp. 7-21 ◽  
Author(s):  
Y Y Yang ◽  
D A Linkens ◽  
M Mahfouf
Keyword(s):  
Neural Network ◽  
Genetic Algorithms ◽  
Constitutive Model ◽  
Hybrid Model ◽  
Network Model ◽  
Neural Network Model ◽  
Constitutive Equations ◽  
Model Performance ◽  
Hybrid Neural Network ◽  
Real Value

This paper addresses the design of genetic algorithms in developing a hybrid neural network model for aluminium alloy flow stress prediction. The hybrid neural network model consists of a parallel grey-box model structure, with the resulting predictions combining the outputs from the constitutive equations and a neural network. Previous work shows that the hybrid neural network model can deliver better model performance than a neural network model or the constitutive equations. However, the level of performance improvement of the hybrid model depends on the quality of the constitutive model used. This motivates the search for a better constitutive model, with genetic algorithms being employed to optimize its parameters. The advantage of genetic algorithms is that they do not require any gradient information nor continuity assumption in searching for the best parameters. A number of genetic optimization schemes, with different coding schemes (such as binary coding and real-value chromosomes) and different genetic operators for selection, crossover and mutation, have been investigated. The real-value coded genetic algorithms converge much more rapidly and are more efficient since there is no need for chromosome encoding and decoding. Compared with previous work, the resulting hybrid model performance has been improved, mainly in the generalization capability and with a simpler neural network structure. Also, the model response surfaces are much smoother and more metallurgically convincing.

scholarly journals Lithium Battery SOH Monitoring and an SOC Estimation Algorithm Based on the SOH Result

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4506
Author(s):  
Jong-Hyun Lee ◽  
In-Soo Lee
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Short Term Memory ◽  
Network Models ◽  
Estimation Algorithm ◽  
The State ◽  
State Of Health ◽  
Neural Network Models ◽  
Health Diagnosis

Lithium batteries are the most common energy storage devices in items such as electric vehicles, portable devices, and energy storage systems. However, if lithium batteries are not continuously monitored, their performance could degrade, their lifetime become shortened, or severe damage or explosion could be induced. To prevent such accidents, we propose a lithium battery state of health monitoring method and state of charge estimation algorithm based on the state of health results. The proposed method uses four neural network models. A neural network model was used for the state of health diagnosis using a multilayer neural network model. The other three neural network models were configured as neural network model banks, and the state of charge was estimated using a multilayer neural network or long short-term memory. The three neural network model banks were defined as normal, caution, and fault neural network models. Experimental results showed that the proposed method using the long short-term memory model based on the state of health diagnosis results outperformed the counterpart methods.

scholarly journals RISC-V Virtual Platform-Based Convolutional Neural Network Accelerator Implemented in SystemC

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1514
Author(s):  
Seung-Ho Lim ◽  
WoonSik William Suh ◽  
Jin-Young Kim ◽  
Sang-Young Cho
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
System Level ◽  
Neural Network Models ◽  
Data Set ◽  
Embedded Device ◽  
Virtual Platform

The optimization for hardware processor and system for performing deep learning operations such as Convolutional Neural Networks (CNN) in resource limited embedded devices are recent active research area. In order to perform an optimized deep neural network model using the limited computational unit and memory of an embedded device, it is necessary to quickly apply various configurations of hardware modules to various deep neural network models and find the optimal combination. The Electronic System Level (ESL) Simulator based on SystemC is very useful for rapid hardware modeling and verification. In this paper, we designed and implemented a Deep Learning Accelerator (DLA) that performs Deep Neural Network (DNN) operation based on the RISC-V Virtual Platform implemented in SystemC in order to enable rapid and diverse analysis of deep learning operations in an embedded device based on the RISC-V processor, which is a recently emerging embedded processor. The developed RISC-V based DLA prototype can analyze the hardware requirements according to the CNN data set through the configuration of the CNN DLA architecture, and it is possible to run RISC-V compiled software on the platform, can perform a real neural network model like Darknet. We performed the Darknet CNN model on the developed DLA prototype, and confirmed that computational overhead and inference errors can be analyzed with the DLA prototype developed by analyzing the DLA architecture for various data sets.

scholarly journals Supervised Mover's Distance: A simple model for sentence comparison

2018 ◽  
Author(s):  
Muktabh Mayank Srivastava
Keyword(s):  
Neural Network ◽  
Simple Model ◽  
Network Model ◽  
Neural Network Model ◽  
Short Term Memory ◽  
Network Module ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Simple Neural Network

We propose a simple neural network model which can learn relation between sentences by passing their representations obtained from Long Short Term Memory(LSTM) through a Relation Network. The Relation Network module tries to extract similarity between multiple contextual representations obtained from LSTM. Our model is simple to implement, light in terms of parameters and works across multiple supervised sentence comparison tasks. We show good results for the model on two sentence comparison datasets.

scholarly journals Initial Results of Modeling and Improvement of BDS-2/GPS Broadcast Ephemeris Satellite Orbit Based on BP and PSO-BP Neural Networks

2021 ◽  
Vol 13 (23) ◽  
pp. 4801
Author(s):  
Hanlin Chen ◽  
Fei Niu ◽  
Xing Su ◽  
Tao Geng ◽  
Zhimin Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Orbit Error ◽  
Broadcast Ephemeris ◽  
Bp Neural Network Model ◽  
Orbit Errors

With the rapid development and gradual perfection of GNSS in recent years, improving the real-time service performance of GNSS has become a research hotspot. In GNSS single-point positioning, broadcast ephemeris is used to provide a space–time reference. However, the orbit parameters of broadcast ephemeris have meter-level errors, and no mathematical model can simulate the variation of this, which restricts the real-time positioning accuracy of GNSS. Based on this research background, this paper uses a BP (Back Propagation) neural network and a PSO (Particle Swarm Optimization)–BP neural network to model the variation in the orbit error of GPS and BDS broadcast ephemeris to improve the accuracy of broadcast ephemeris. The experimental results showed that the two neural network models in GPS can model the broadcast ephemeris orbit errors, and the results of the two models were roughly the same. The one-day and three-day improvement rates of RMS(3D) were 30–50%, but the PSO–BP neural network model was better able to model the trend of errors and effectively improve the broadcast ephemeris orbit accuracy. In BDS, both of the neural network models were able to model the broadcast ephemeris orbit errors; however, the PSO–BP neural network model results were better than those of the BP neural network. In the GEO satellite outcome of the PSO–BP neural network, the STD and RMS of the orbit error in three directions were reduced by 20–70%, with a 20–30% improvement over the BP neural network results. The IGSO satellite results showed that the PSO–BP neural network model output accuracy of the along- and radial-track directions experienced a 70–80% improvement in one and three days. The one- and three-day RMS(3D) of the MEO satellites showed that the PSO–BP neural network has a greater ability to resist gross errors than that of the BP neural network for modeling the changing trend of the broadcast ephemeris orbit errors. These results demonstrate that using neural networks to model the orbit error of broadcast ephemeris is of great significance to improving the orbit accuracy of broadcast ephemeris.

A COMPARISON OF NEURAL NETWORK MODEL SELECTION STRATEGIES FOR THE PRICING OF S&P 500 STOCK INDEX OPTIONS

2007 ◽  
Vol 16 (06) ◽  
pp. 1093-1113 ◽  
Author(s):  
N. S. THOMAIDIS ◽  
V. S. TZASTOUDIS ◽  
G. D. DOUNIAS
Keyword(s):  
Neural Network ◽  
Model Selection ◽  
Network Model ◽  
Neural Network Model ◽  
Information Criteria ◽  
Stock Index ◽  
Statistical Hypothesis ◽  
Neural Network Models ◽  
Index Options ◽  
Pruning Technique

This paper compares a number of neural network model selection approaches on the basis of pricing S&P 500 stock index options. For the choice of the optimal architecture of the neural network, we experiment with a “top-down” pruning technique as well as two “bottom-up” strategies that start with simple models and gradually complicate the architecture if data indicate so. We adopt methods that base model selection on statistical hypothesis testing and information criteria and we compare their performance to a simple heuristic pruning technique. In the first set of experiments, neural network models are employed to fit the entire options surface and in the second they are used as parts of a hybrid intelligence scheme that combines a neural network model with theoretical option-pricing hints.

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