Economic Analysis of HRES Systems with Energy Storage During Grid Interruptions and Curtailment in Tamil Nadu, India:A Hybrid RBFNOEHO Technique

This work presents an economic analysis of a hybrid renewable energy source (HRES) integrated with an energy storage system (ESS) using batteries with a new proposed strategy. Here, the HRES system comprises wind turbines (WT) and a photovoltaic (PV) system. The hybrid WT, PV and energy storage system with battery offer several benefits, in particular, high wind generation utilization rate, and optimal generation for meeting supply-demand gaps. The real recorded data of various parameters of a 22 KV hybrid ‘Regen’ feeder of 110/22 KV Vagarai Substation of TANTRANSCO in Palani of Tamilnadu in India was gathered, studied for the entire year of 2018, and utilized in this paper. The proposed strategy is the hybridization of two algorithms called Radial Basis Function Neural Network (RBFNN) and Oppositional Elephant Herding Optimization (OEHO) named the RBFNOEHO technique. With the help of RBFNN, the continuous load demand required for the HRES and be tracked. OEHO is used to optimize a perfect combination of HRES with the predicted load demand. The aim of the proposed hybrid RBFNOEHO is to study the cost comparison of the HRES system with the existing conventional base method, energy storage method (ESS) with batteries and with HOMER. The proposed Hybrid RBFNOEHO technique is evaluated by comparing it with the other techniques; it is found that the proposed method yields a more optimal solution than the other techniques

A Hybrid Sailfish Whale Optimization and Deep Long Short-Term Memory (SWO-DLSTM) Model for Energy Efficient Autonomy in India by 2048

In order to formulate the long-term and short-term development plans to meet the energy needs, there is a great demand for accurate energy forecasting. Most of the existing energy demand forecasting models predict the amount of energy at a regional or national scale and failed to forecast the demand for power generation for small-scale decentralized energy systems, like micro grids, buildings, and energy communities. Deep learning models play a vital role in accurately forecasting the energy de-mand. A novel model called Sail Fish Whale Optimization-based Deep Long Short- Term memory (SFWO-based Deep LSTM) to forecast electricity demand in the distribution systems is proposed. The proposed SFWO is designed by integrating the Sail Fish Optimizer (SFO) with the Whale Optimiza-tion Algorithm (WOA). The Hilbert-Schmidt Independence Criterion Lasso (HSIC) is applied on the dataset, which is collected from the Central electricity authority, Government of India, for selecting the optimal features using the technical indicators. The proposed algorithm was implemented in MATLAB software package and the study was done using real-time data. The feature selection pro-cess improves the accuracy of the proposed model by training the features using Deep LSTM. The results of the proposed model in terms of install capacity prediction, village electrified prediction, length of R & D lines prediction, hydro, coal, diesel, nuclear prediction, etc. are compared with the existing models. The proposed model achieves good accuracy with the average normalized Root Mean Squared Error (RMSE) value of 4.4559. The hybrid approach provides improved accuracy for the prediction of energy demand in India by the year 2047.publishedVersio

Cybernetics approaches in intelligent systems for crops disease detection with the aid of IoT

Detection of crop diseases is imperative for agriculture to be sustainable. Automated crop disease detection is a major issue in the current agricultural industry due to its cluttered background. Internet of Things (IoT) has gained immense interest in the past decade, as it accumulates a high level of contextual information to identify crop diseases. This study paper presents a novel method based on Taylor‐Water Wave Optimization‐based Generative Adversarial Network (Taylor‐WWO‐based GAN) to identify diseases in the agricultural industry. In this method, the IoT nodes sense the plant leaves, and the sensed data are transmitted to the Base Station (BS) using Fractional Gravitational Gray Wolf Optimization. This technique selects the optimal path for data transmission. After performing IoT routing, crop diseases are recognized at the BS. For detecting crop disease, the input image acquired from the IoT routing phase is then forwarded to the next step, that is, preprocessing, to improve the quality of the image for further processing. Then, Segmentation Network (SegNet) is adapted to segment the images, and extraction of significant features is performed using the acquired segments. The extracted features are adapted by the GAN, which is trained by Taylor‐WWO. The proposed Taylor‐WWO is newly devised by integrating the Taylor series and WWO algorithms. The proposed Taylor‐WWO‐based GAN showed improved performance with a maximum accuracy of 91.6%, maximum sensitivity of 89.3%, and maximum specificity of 92.3% in comparison with existing methods

Causes of higher levels of stress among students in higher education who used eLearning platforms during the COVID-19 pandemic

Background: This work aims to study the levels of stress among students using e-learning platforms during the COVID-19 pandemic in higher education institutions. The major factors of higher-level stress among the student community focused on this study are: Changes in academic environment, family, social, personal, health and cognitive. Objective: the objective of this research the Partial Least Squares Structural Equation Modelling (PLS-SEM) procedure was used to explore the relationship and its impact on various levels of stress. Results: Data were collected by using a total of 1,000 email IDs of students that were made available by teachers from 12 Indian higher education institutions where they were enrolled and by using a random number method. With this procedure, a total of 800 email IDs were selected. The results drawn from this research are that students experienced more stress due to sudden changes in the academic environment, family, and personal factors. The stress levels of cognitive and social were found to be equally distributed among higher education students, but less than academic environment, family and personal. This research intends to fill the gap of short-term individual psychological changes that occur after the outbreak. Conclusion: Policy-makers can take note of the current study’s observations in continuing their fight against COVID-19 pandemic by improving the stability for student risk groups

A Comprehensive Review on Sustainable Aspects of Big Data Analytics for the Smart Grid

The role of energy is cardinal for achieving the Sustainable Development Goals (SDGs) through the enhancement and modernization of energy generation and management practices. The smart grid enables efficient communication between utilities and the end- users, and enhances the user experience by monitoring and controlling the energy transmission. The smart grid deals with an enormous amount of energy data, and the absence of proper techniques for data collection, processing, monitoring and decision-making ultimately makes the system ineffective. Big data analytics, in association with the smart grid, enable better grid visualization and contribute toward the attainment of sustainability. The current research work deals with the achievement of sustainability in the smart grid and efficient data management using big data analytics, that has social, economic, technical and political impacts. This study provides clear insights into energy data generated in the grid and the possibilities of energy theft affecting the sustainable future. The paper provides insights about the importance of big data analytics, with their effects on the smart grids’ performance towards the achievement of SDGs. The work highlights efficient real-time energy data management involving artificial intelligence and machine learning for a better future, to short out the effects of the conventional smart grid without big data analytics. Finally, the work discusses the challenges and future directions to improve smart grid technologies with big data analytics in action

A Comprehensive Review on Sustainable Aspects of Big Data Analytics for the Smart Grid

The role of energy is cardinal for achieving the Sustainable Development Goals (SDGs) through the enhancement and modernization of energy generation and management practices. The smart grid enables efficient communication between utilities and the end- users, and enhances the user experience by monitoring and controlling the energy transmission. The smart grid deals with an enormous amount of energy data, and the absence of proper techniques for data collection, processing, monitoring and decision-making ultimately makes the system ineffective. Big data analytics, in association with the smart grid, enable better grid visualization and contribute toward the attainment of sustainability. The current research work deals with the achievement of sustainability in the smart grid and efficient data management using big data analytics, that has social, economic, technical and political impacts. This study provides clear insights into energy data generated in the grid and the possibilities of energy theft affecting the sustainable future. The paper provides insights about the importance of big data analytics, with their effects on the smart grids’ performance towards the achievement of SDGs. The work highlights efficient real-time energy data management involving artificial intelligence and machine learning for a better future, to short out the effects of the conventional smart grid without big data analytics. Finally, the work discusses the challenges and future directions to improve smart grid technologies with big data analytics in action

A Hybrid Sailfish Whale Optimization and Deep Long Short-Term Memory (SWO-DLSTM) Model for Energy Efficient Autonomy in India by 2048

In order to formulate the long-term and short-term development plans to meet the energy needs, there is a great demand for accurate energy forecasting. Most of the existing energy demand forecasting models predict the amount of energy at a regional or national scale and failed to forecast the demand for power generation for small-scale decentralized energy systems, like micro grids, buildings, and energy communities. Deep learning models play a vital role in accurately forecasting the energy de-mand. A novel model called Sail Fish Whale Optimization-based Deep Long Short- Term memory (SFWO-based Deep LSTM) to forecast electricity demand in the distribution systems is proposed. The proposed SFWO is designed by integrating the Sail Fish Optimizer (SFO) with the Whale Optimiza-tion Algorithm (WOA). The Hilbert-Schmidt Independence Criterion Lasso (HSIC) is applied on the dataset, which is collected from the Central electricity authority, Government of India, for selecting the optimal features using the technical indicators. The proposed algorithm was implemented in MATLAB software package and the study was done using real-time data. The feature selection pro-cess improves the accuracy of the proposed model by training the features using Deep LSTM. The results of the proposed model in terms of install capacity prediction, village electrified prediction, length of R & D lines prediction, hydro, coal, diesel, nuclear prediction, etc. are compared with the existing models. The proposed model achieves good accuracy with the average normalized Root Mean Squared Error (RMSE) value of 4.4559. The hybrid approach provides improved accuracy for the prediction of energy demand in India by the year 2047

Realization of Sustainable Development Goals with Disruptive Technologies by Integrating Industry 5.0, Society 5.0, Smart Cities and Villages

Significant changes in society were emphasized as being required to achieve Sustainable Development Goals, a need which was further intensified with the emergence of the pandemic. The prospective society should be directed towards sustainable development, a process in which technology plays a crucial role. The proposed study discusses the technological potential for attaining the Sustainable Development Goals via disruptive technologies. This study further analyzes the outcome of disruptive technologies from the aspects of product development, health care transformation, a pandemic case study, nature-inclusive business models, smart cities and villages. These outcomes are mapped as a direct influence on Sustainable Development Goals 3, 8, 9 and 11. Various disruptive technologies and the ways in which the Sustainable Development Goals are influenced are elaborated. The investigation into the potential of disruptive technologies highlighted that Industry 5.0 and Society 5.0 are the most supportive development to underpin the efforts to achieve the Sustainable Development Goals. The study proposes the scenario where both Industry 5.0 and Society 5.0 are integrated to form smart cities and villages where the prospects of achieving Sustainable Development Goals are more favorable due to the integrated framework and Sustainable Development Goals’ interactions. Furthermore, the study proposes an integrated framework for including new age technologies to establish the concepts of Industry 5.0 and Society 5.0 integrated into smart cities and villages. The corresponding influence on the Sustainable Development Goals are also mapped. A SWOT analysis is performed to assess the proposed integrated approach to achieve Sustainable Development Goals. Ultimately, this study can assist the industrialist, policy makers and researchers in envisioning Sustainable Development Goals from technological perspectives