Design and optimization of photovoltaic system with a week ahead power forecast using autoregressive artificial neural networks

2021 ◽  
Author(s):  
Shafqat Nabi Mughal ◽  
Yog Raj Sood ◽  
R.K. Jarial
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Photovoltaic System ◽  
Design And Optimization ◽  
Artificial Neural

scholarly journals Applying Artificial Neural Networks In Construction

2020 ◽  
Vol 143 ◽  
pp. 01029
Author(s):  
Anna Doroshenko
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Artificial Neural Networks ◽  
Construction Industry ◽  
Structural Design ◽  
Soil Mechanics ◽  
Construction Materials ◽  
Construction Engineering ◽  
Design And Optimization ◽  
Artificial Neural

Currently, artificial neural networks (ANN) are used to solve the following complex problems: pattern recognition, speech recognition, complex forecasts and others. The main applications of ANN are decision making, pattern recognition, optimization, forecasting, data analysis. This paper presents an overview of applications of ANN in construction industry, including energy efficiency and energy consumption, structural analysis, construction materials, smart city and BIM technologies, structural design and optimization, application forecasting, construction engineering and soil mechanics.

A Multidisciplinary Design and Optimization Methodology in Electronics Packaging: Application to BGA Design

2003 ◽  
Author(s):  
Hamid Hadim ◽  
Tohru Suwa
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Objective Function ◽  
Thermal Strain ◽  
Design Parameters ◽  
Design And Optimization ◽  
Trade Offs ◽  
Artificial Neural ◽  
Optimization Methodology ◽  
The Individual

In this manuscript a systematic multidisciplinary electronic packaging design and optimization methodology based on the artificial neural networks technique is presented. This method is applied to a Ball Grid Array (BGA) package design as an example. Multidisciplinary criteria including thermal, structural (thermal strain), electromagnetic leakage, and cost are optimized simultaneously. A simplified routability criterion is also considered as a constraint. The artificial neural networks technique is used for thermal and structural performance predictions. Large calculation time reduction is achieved using the artificial neural networks, which also provide enough information to specify the individual weights for each design discipline within the objective function used for optimization. This methodology is able to provide the designers a clear view of the design trade-offs, which are represented in the objective function using various design parameters. This methodology can be applied to any electronic product design at any packaging level.

scholarly journals MPPT Based On Artificial Neural Networks (ANN) For a Photovoltaic System Under Unstable Environmental Conditions

2021 ◽  
Author(s):  
Pascal Kuate Nkounhawa ◽  
Dieunedort Ndapeu ◽  
Bienvenu Kenmeugne
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Environmental Conditions ◽  
Experimental Tests ◽  
Photovoltaic System ◽  
Neural Controller ◽  
Point Tracking ◽  
Artificial Neural ◽  
Output Characteristics ◽  
Power Point

Abstract In this article, an artificial neural networks (ANN) based maximum power point tracking controller (MPPT) was developed to improve the performance of the FL-M-160W solar panel under unstable environmental conditions. To develop and configure the neural controller, a database resulting from experimental tests was built for the training of the proposed model. Then the model was tested and validated under the Matlab / Simulink environment. The optimum voltage obtained at the output of the neural controller is compared to the voltage of the photovoltaic generator and the error is used to modify the duty cycle of the DC-DC boost converter. It is shown after simulations that unlike conventional controllers which are very slow, the neural MPPT controller offers more stable, more accurate output characteristics with very low response time and very low oscillations around the operating point both in transient and steady state, even under varying environmental conditions.

scholarly journals Prediction of Sound Insulation of Sandwich Partition Panels by Means of Artificial Neural Networks

2017 ◽  
Vol 42 (4) ◽  
pp. 643-651
Author(s):  
Naveen Garg ◽  
Siddharth Dhruw ◽  
Laghu Gandhi
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Optimization Technique ◽  
Back Propagation ◽  
National Research Council ◽  
Sound Insulation ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Design And Optimization ◽  
Artificial Neural

Abstract The paper presents the application of Artificial Neural Networks (ANN) in predicting sound insulation through multi-layered sandwich gypsum partition panels. The objective of the work is to develop an Artificial Neural Network (ANN) model to estimate the Rw and STC value of sandwich gypsum constructions. The experimental results reported by National Research Council, Canada for Gypsum board walls (Halliwell et al., 1998) were utilized to develop the model. A multilayer feed-forward approach comprising of 13 input parameters was developed for predicting the Rw and STC value of sandwich gypsum constructions. The Levenberg-Marquardt optimization technique has been used to update the weights in back-propagation algorithm. The presented approach could be very useful for design and optimization of acoustic performance of new sandwich partition panels providing higher sound insulation. The developed ANN model shows a prediction error of ±3 dB or points with a confidence level higher than 95%.

A Multidisciplinary Design and Optimization Methodology for Ball Grid Array Packages Using Artificial Neural Networks

2004 ◽  
Vol 127 (3) ◽  
pp. 306-313 ◽  
Author(s):  
Hamid Hadim ◽  
Tohru Suwa
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Ball Grid Array ◽  
System Level ◽  
Computational Time ◽  
Design Parameters ◽  
Design And Optimization ◽  
Trade Offs ◽  
Artificial Neural ◽  
Optimization Methodology

A systematic multidisciplinary electronics packaging design and optimization methodology, which takes into account the complexity of multiple design trade-offs, operated in conjunction with the artificial neural networks (ANNs) technique is presented. To demonstrate its capability, this method is applied to a plastic ball grid array package design. Multidisciplinary criteria including thermal, structural, electromagnetic leakage, and cost are optimized simultaneously using key design parameters as variables. A simplified routability criterion is also considered as a constraint. ANNs are used for thermal and structural performance predictions which resulted in large reduction in computational time. The present methodology is able to provide the designers a tool for systematic evaluation of the design trade-offs which are represented in the objective function. This methodology can be applied to any electronic product design at any packaging level from the system level to the chip level.

scholarly journals Robust Method for Diagnosis and Detection of Faults in Photovoltaic Systems Using Artificial Neural Networks

2020 ◽  
Author(s):  
Aicha Amani Djalab ◽  
Mohamed Mounir Rezaoui ◽  
Lakhdar Mazouz ◽  
Ali Teta ◽  
Nassim Sabri
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Good Accuracy ◽  
Photovoltaic System ◽  
Solar Panels ◽  
Pv Systems ◽  
Pv Module ◽  
Sophisticated Method ◽  
Artificial Neural ◽  
And Performance

During their operation, PV systems can be subject of various faults and anomalies that could lead to a reduction in the effectiveness and the profitability of the PV systems. These faults can crash, cause a fire or stop the whole system. The main objective of this work is to present a sophisticated method based on artificial neural networks ANN for diagnosing; detecting and precisely classifying the fault in the solar panels in order to avoid a fall in the production and performance of the photovoltaic system. The work established in this paper intends in first place to propose a method to detect possible various faults in PV module using the Multilayer Perceptron (MLP) ANN network. The developed artificial neural network requires a large database and periodic training to evaluate the output parameters with good accuracy. To evaluate the accuracy and the performance of the proposed approach, a comparison is carried out with the classic method (the method of thresholding). To test the effectiveness of the proposed approach in detecting and classifying different faults, an extensive simulation is carried out using Matlab SIMULINK.

scholarly journals Management of battery charging and discharging in a photovoltaic system with variable power demand using artificial neural networks

2021 ◽  
Vol 297 ◽  
pp. 01037
Author(s):  
Jarmouni Ezzitouni ◽  
Mouhsen Ahmed ◽  
Lamhammedi Mohammed ◽  
Krari Ayoub
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Energy Demand ◽  
Fossil Fuels ◽  
Management Strategies ◽  
Photovoltaic System ◽  
Proposed Model ◽  
Artificial Neural ◽  
Control And Management ◽  
Solar Batteries

The energy is the basis of all human activities. Nowadays, much of the world’s energy demand is taken from fossil fuels. However, fossil fuel reserves are limited. The use of solar photovoltaic energy is therefore a necessity for the future. With the rapid increase of photovoltaic or hybrid systems, solar batteries provide an unforgettable energy storage tool in this type of systems in order to ensure an energy supply to consumers. Due to the sensitivity of solar batteries and the random operation of photovoltaic systems that depend on solar irradiance, control and management strategies are quite important. In this paper, we present a technique based on artificial neural networks to control the charging and discharging of solar batteries in order to protect the batteries from overcharging and deep discharging. In addition, ensuring continuous supply to consumers. The proposed model is developed and simulated in Matlab/Simulink.

Sign in / Sign up

Export Citation Format

Share Document