scholarly journals AN OPTIMIZED DESIGN MODELLING OF A NEURAL NETWORK BASED GREEN HOUSE MANAGEMENT SYSTEM USING SOLAR AND RECTIFYING ANTENNA

10.6036/10089 ◽  
2022 ◽  
Vol 97 (1) ◽  
pp. 85-91
Author(s):  
Sathiyaraj Kasinathan ◽  
RAJARAM AYYASAMY
Keyword(s):  
Neural Network ◽  
Energy Management ◽  
Research Work ◽  
Boost Converter ◽  
Signal Generation ◽  
Optimized Design ◽  
Renewable Energy Resources ◽  
Feed Forward Neural Network ◽  
System Utilization ◽  
Rf Signal

The renewable energy resources are widely used in various real time applications, which utilized the solar, wind, fuel cell, etc. From this, the energy management and controlling strategy improves the results. The conventional approach uses Quantum Tunneling PSO for optimization and it is managed with various utility on power grid system. The work utilized the solar and EM waves for energy management scheme and it utilized the controlling parameter by optimization algorithm. The drawback of conventional method is that, the hybrid system utilization and switching is performed with random selection and it not capable for hybrid resources of multiple array functioning. The proposed research work performed with the solar with MPPT tracking and EM with rectenna are utilized and with the help of neural network model, the PV and RF signal generations are stored as array and based on the switching duty cycle from the function of proposed particle swarm optimization, the boost converter act to provide the supply to grid. Through the inverter control, the model fed with the grid, which uses PI controlling with PWM signal generation. Based on the demand and grid utility the LC compensation improves the boost converter performance. The PV and RF signal generation utilized on the continuous utility and obtains the demand free grid circuit. By comparing with the proposed and existing approach, the proposed greenhouse management model obtains the better result. Overall simulink model is done with MATLAB 2018a. Keywords- PV module; EM waves; Rectenna; Proposed PSO; Feed Forward neural network; PI controller and grid utility;

Energy Optimization and Power Stack Shed Reduction of Oil Depots by using Renewable Energy Resources

2021 ◽  
Vol 4 (2) ◽  
pp. 125-130
Author(s):  
Muhammad Azhar Mahmood ◽  
Muhammad Kamran Liaqat Bhatti ◽  
S. Raza ◽  
M. Riaz
Keyword(s):  
Renewable Energy ◽  
Wind Energy ◽  
Energy Management ◽  
Unit Cost ◽  
Research Work ◽  
Energy Resource ◽  
Energy Resources ◽  
Renewable Energy Resources ◽  
Oil Sector ◽  
Continuous Power

Most of the industries including the oil sector are looking forward towards the renewable energy resources with proper energy management system (EMS) as it is the need of time. For this purpose, solar and wind energy are the renewable energy resources, which are obtained from natural resources and produce clean and environment -friendly electrical energy and can be used for oil depots. The proper utilization of solar and wind energy from natural resource may result in economical and cost-effective EMS. In the proposed research work, an effective energy management demonstration is delivered to ensure the ceaseless flexibility of power. Furthermore, reduction of production per unit cost to the oil sector industry by utilizing multiple objectives streamlining. In the proposed oil depot, connected loads are divided into Shiftable and Non-Shiftable loads and then apply Branch and Bound Algorithm (BnB) with binary integer linear programming (BILP). By using the BnB technique, selected shiftable loads are shifted to the low cost energy resource automatically and resultantly, we get the low price unit cost and continuous power supply. Simulation results for the above-mentioned research work are performed on MATLAB. The proposed technique helps to reduce the power stack shedding issue as well.

scholarly journals Day-Ahead Solar Irradiance Forecasting Using Hybrid Recurrent Neural Network with Weather Classification for Power System Scheduling

2021 ◽  
Vol 11 (15) ◽  
pp. 6738
Author(s):  
Rehman Zafar ◽  
Ba Hau Vu ◽  
Munir Husein ◽  
Il-Yop Chung
Keyword(s):  
Neural Network ◽  
Renewable Energy ◽  
Power System ◽  
Recurrent Neural Network ◽  
Solar Irradiance ◽  
Short Term Memory ◽  
Dew Point ◽  
Support Vector ◽  
Renewable Energy Resources ◽  
Feed Forward Neural Network

At the present time, power-system planning and management is facing the major challenge of integrating renewable energy resources (RESs) due to their intermittent nature. To address this problem, a highly accurate renewable energy generation forecasting system is needed for day-ahead power generation scheduling. Day-ahead solar irradiance (SI) forecasting has various applications for system operators and market agents such as unit commitment, reserve management, and biding in the day-ahead market. To this end, a hybrid recurrent neural network is presented herein that uses the long short-term memory recurrent neural network (LSTM-RNN) approach to forecast day-ahead SI. In this approach, k-means clustering is first used to classify each day as either sunny or cloudy. Then, LSTM-RNN is used to learn the uncertainty and variability for each type of cluster separately to predict the SI with better accuracy. The exogenous features such as the dry-bulb temperature, dew point temperature, and relative humidity are used to train the models. Results show that the proposed hybrid model has performed better than a feed-forward neural network (FFNN), a support vector machine (SVM), a conventional LSTM-RNN, and a persistence model.

scholarly journals Application of Feed Forward Neural Network for Prediction of Optimum Coagulant Dose in Water Treatment Plant

2019 ◽  
Vol 8 (12) ◽  
pp. 1853-1856
Keyword(s):  
Neural Network ◽  
Water Treatment ◽  
Network Architecture ◽  
Assessment Tool ◽  
Research Work ◽  
Treatment Plant ◽  
Training Algorithm ◽  
Feed Forward Neural Network ◽  
Plant Laboratory ◽  
Feed Forward

Coagulation is anecessary process used mainly to reduce turbidity and natural organic matter in water treatment. The dosage of coagulant required is conventionally determined by carrying out jar tests which consume time and chemicals.In India, coagulant dose in a WTP remains constant during certain periods due to delay in jar testing, which may lead to under-dosing or over-dosing of coagulant. This research work is focused on applying artificial neural network (ANN) approach to predict coagulant dose in a WTP. Forty-eight months daily water testing data concerning inlet & outlet water turbidity and coagulant dose were obtained from the plant laboratory for ANN modelling. The appropriate architecture of feed forward neural network (FFNN) coagulant models were established with several steps of training and testing by applying various training algorithms vizLevenberg-Marquardt (LM) and Bayesian regularization (BR), resilient back propagation (RBP), one step secant(OSS),variants of conjugate gradient(CG) and modifications of gradient descent (GD) with evaluating coefficient of correlation (R) & mean square error (MSE). Further, best performed LM and BR training algorithm were used for development of four ANN models of FFNN for prediction of coagulant dose at WTP. FFNN coagulant model with BR training algorithm provided excellent estimates with network architecture (2-50-1) for coagulant dose with maximum value of R= 0.943 (training) and R = 0.945 (testing). Thus, ANN provided an effective diagnosing tool to understand the non-linear behavior of the coagulation process, and can be used as a valuable performance assessment tool for plant operators and decision makers.

scholarly journals SSNN-Based Energy Management Strategy in Grid Connected System for Load Scheduling and Load Sharing

2022 ◽  
Vol 2022 ◽  
pp. 1-9
Author(s):  
Yuvaraja Teekaraman ◽  
K. A. Ramesh Kumar ◽  
Ramya Kuppusamy ◽  
Amruth Ramesh Thelkar
Keyword(s):  
Energy Management ◽  
Management Strategy ◽  
Storage System ◽  
Research Work ◽  
Load Sharing ◽  
Renewable Energy Resources ◽  
Energy Management Strategy ◽  
System A ◽  
Load Demand ◽  
Pv Generation

The proposed research work focused on energy management strategy (EMS) in a grid connected system working in islanding mode with the connected renewable energy resources and battery storage system. The energy management strategy developed provides a balancing operation at its output by utilizing perfect load sharing strategy. The EMS technique using smart superficial neural network (SSNN) is simulated, and numerical analyses are presented to validate the effectiveness of the centralized energy management strategy in a grid connected islanded system. A SSNN prediction model is unified to forecast the associated household load demand, PV generation system under various time horizons (including the disaster condition), EV availability, and status on EV section and distance. SSNN is one the most reliable forecasting methods in many of the applications. The developed system is also accounted for degradation battery model and its associated cost. The incorporation of energy management strategy (EMS) reduces the amount of energy drawn from the grid connected system when compared with the other optimized systems.

Valid Signature Detection and Verification for Security of Individual Person

2016 ◽  
Vol 1 (1) ◽  
pp. 50-53 ◽  
Author(s):  
Varun Sharma ◽  
Narpat Singh
Keyword(s):  
Neural Network ◽  
Comparative Analysis ◽  
Recent Work ◽  
Research Work ◽  
Early Research ◽  
Individual Person ◽  
Signature Detection ◽  
Online Signature ◽  
The Right ◽  
Handwritten Signature

In the recent research work, the handwritten signature is a suitable field to detection of valid signature from different environment such online signature and offline signature. In early research work, a lot of unauthorized person put the signature and theft the data in illegal manner from organization or industries. So we have to need identify, the right person on the basis of various parameters that can be detected. In this paper, we have proposed two methods namely LDA and Neural Network for the offline signature from the scan signature image. For efficient research, we have focused the comparative analysis in terms of FRR, SSIM, MSE, and PSNR. These parameters are compared with the early work and the recent work. Our proposed work is more effective and provides the suitable result through our method which leads to existing work. Our method will help to find legal signature of authorized use for security and avoid illegal work.

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