Smart grid (SG), an evolving concept in the modern power infrastructure, enables the two-way flow of electricity and data between the peers within the electricity system networks (ESN) and its clusters. The self-healing capabilities of SG allow the peers to become active partakers in ESN. In general, the SG is intended to replace the fossil fuel-rich conventional grid with the distributed energy resources (DER) and pools numerous existing and emerging know-hows like information and digital communications technologies together to manage countless operations. With this, the SG will able to “detect, react, and pro-act” to changes in usage and address multiple issues, thereby ensuring timely grid operations. However, the “detect, react, and pro-act” features in DER-based SG can only be accomplished at the fullest level with the use of technologies like Artificial Intelligence (AI), the Internet of Things (IoT), and the Blockchain (BC). The techniques associated with AI include fuzzy logic, knowledge-based systems, and neural networks. They have brought advances in controlling DER-based SG. The IoT and BC have also enabled various services like data sensing, data storage, secured, transparent, and traceable digital transactions among ESN peers and its clusters. These promising technologies have gone through fast technological evolution in the past decade, and their applications have increased rapidly in ESN. Hence, this study discusses the SG and applications of AI, IoT, and BC. First, a comprehensive survey of the DER, power electronics components and their control, electric vehicles (EVs) as load components, and communication and cybersecurity issues are carried out. Second, the role played by AI-based analytics, IoT components along with energy internet architecture, and the BC assistance in improving SG services are thoroughly discussed. This study revealed that AI, IoT, and BC provide automated services to peers by monitoring real-time information about the ESN, thereby enhancing reliability, availability, resilience, stability, security, and sustainability.

As education is an essential enabler in achieving Sustainable Development Goals (SDGs), it should “ensure inclusive, equitable quality education, and promote lifelong learning opportunities for all”. One of the frameworks for SDG 4 is to propose the concepts of “equitable quality education”. To attain and work in the context of SDG 4, artificial intelligence (AI) is a booming technology, which is gaining interest in understanding student behavior and assessing student performance. AI holds great potential for improving education as it has started to develop innovative teaching and learning approaches in education to create better learning. To provide better education, data analytics is critical. AI and machine learning approaches provide rapid solutions with high accuracy. This paper presents an AI-based analytics tool created to predict student performance in a first-year Information Technology literacy course at The University of the South Pacific (USP). A Random Forest based classification model was developed which predicted the performance of the student in week 6 with an accuracy value of 97.03%, sensitivity value of 95.26%, specificity value of 98.8%, precision value of 98.86%, Matthews correlation coefficient value of 94% and Area Under the ROC Curve value of 99%. Hence, such a method is very useful in predicting student performance early in their courses of allowing for early intervention. During the COVID-19 outbreak, the experimental findings demonstrate that the suggested prediction model satisfies the required accuracy, precision, and recall factors for forecasting the behavioural elements of teaching and e-learning for students in virtual education systems.

Classification of electroencephalography (EEG) signals for motor imagery based brain computer interface (MI-BCI) is an exigent task and common spatial pattern (CSP) has been extensively explored for this purpose. In this work, we focused on developing a new framework for classification of EEG signals for MI-BCI. We propose a single band CSP framework for MI-BCI that utilizes the concept of tangent space mapping (TSM) in the manifold of covariance matrices. The proposed method is named CSP-TSM. Spatial filtering is performed on the bandpass filtered MI EEG signal. Riemannian tangent space is utilized for extracting features from the spatial filtered signal. The TSM features are then fused with the CSP variance based features and feature selection is performed using Lasso. Linear discriminant analysis (LDA) is then applied to the selected features and finally classification is done using support vector machine (SVM) classifier. The proposed framework gives improved performance for MI EEG signal classification in comparison with several competing methods. Experiments conducted shows that the proposed framework reduces the overall classification error rate for MI-BCI by 3.16%, 5.10% and 1.70% (for BCI Competition III dataset IVa, BCI Competition IV Dataset I and BCI Competition IV Dataset IIb, respectively) compared to the conventional CSP method under the same experimental settings. The proposed CSP-TSM method produces promising results when compared with several competing methods in this paper. In addition, the computational complexity is less compared to that of TSM method. Our proposed CSP-TSM framework can be potentially used for developing improved MI-BCI systems.

Farm machinery like water sprinklers (WS) and pesticide sprayers (PS) are becoming quite popular in the agricultural sector. The WS and PS are two distinct types of machinery, mostly powered using conventional energy sources. In recent times, the battery and solar-powered WS and PS have also emerged. With the current WS and PS, the main drawback is the lack of intelligence on water and pesticide use decisions and autonomous control. This paper proposes a novel multi-purpose smart farming robot (MpSFR) that handles both water sprinkling and pesticide spraying. The MpSFR is a photovoltaic (PV) powered battery-operated internet of things (IoT) and computer vision (CV) based robot that helps in automating the watering and spraying process. Firstly, the PV-powered battery-operated autonomous MpSFR equipped with a storage tank for water and pesticide drove with a programmed pumping device is engineered. The sprinkling and spraying mechanisms are made fully automatic with a programmed pattern that utilizes IoT sensors and CV to continuously monitor the soil moisture and the plant’s health based on pests. Two servo motors accomplish the horizontal and vertical orientation of the spraying nozzle. We provided an option to remotely switch the sprayer to spray either water or pesticide using an infrared device, i.e., within a 5-m range. Secondly, the operation of the developed MpSFR is experimentally verified in the test farm. The field test’s observed results include the solar power profile, battery charging, and discharging conditions. The results show that the MpSFR operates effectively, and decisions on water use and pesticide are automated.

The automobile industry and technology are putting a great significance in improving vehicles to become more fuel economical, but with incremental costs relative to conventional vehicle technologies; these new vehicles are electric vehicles (EV), plug-in hybrid electric vehicles (PHEV), and hybrid electric vehicles (HEV). However, their significant capabilities to reduce petroleum consumption and achieve efficiency over their life cycles offer economic benefits for customers, industry, carmakers, and policymakers. In this paper, an HEV concept based on renewable energy resources (RERs) is proposed. The proposed HEV design utilizes solar PV energy, wind energy, fuel cell, and a supercapacitor (PV + WE + FC + SC) which generates electrical energy via a proton exchange membrane (PEM) and an SC to cater for strong torque requirements. The vehicle incorporates a battery pack in conjunction with an SC for the power demands and an FC as the backup energy supply. An alternator connected to turbine blades runs by wind energy while the car is moving forward, which produces electricity through the alternator to charge the battery. The design aims to ensure zero carbon emission and improved energy efficiency, is lightweight, and incorporates in-wheel motors to eliminate the mechanical transmissions. Modeling and simulation were carried out for each subsystem using MATLAB^® and Simulink^® packages. ANSYS Fluent simulation was used to analyze wind energy. The standard analysis, e.g., pressure, velocity, and vector contour, were also considered while designing the final model. To regulate the power supply and demand, the selection of energy sources was controlled by a rule-based supervisory controller following a logical sequence that prioritizes energy sources with the SC as a source in-vehicle stop-and-go situations while the battery acts as the primary source, FC as a backup supply, and wind and solar power to recharge the battery. Solar charging is switched on automatically once the vehicle is parked, and the controller controls the energy flow from the alternator during that period.

Several studies state that phase change material (PCM) improves the electrical power and efficiency of the photovoltaic (PV) module. To find the suitable PCM for tropical climatic conditions, multi-PCMs are examined simultaneously with melting temperatures of 31 °C, 35 °C, 37 °C, and 42 °C. In this study, PCM containers are integrated behind the PV module with a thickness of 50 mm. The performance of the multi PV-PCMs is monitored year-round and compared with PV-noPCM. The experimental results show that the selected four PCMs performed the cooling process autonomously in all the climates, such as PCM with a melting temperature of 37 °C and 42 °C enhanced the higher cooling rate in summer, and the same PCMs failed to achieve a higher cooling rate in winter. The lowest temperature drop was noted for pre-monsoon and monsoon seasons due to the low irradiance. On the other hand, the highest temperature drop of 16.33 °C is observed for pre-summer (March) and 15.7 °C, and 17.14 °C for summer (April) as compared to PV-noPCM. The results of the present investigation highlight the requirement for choosing the proper PCM melting temperature based on optimal year-round performance. Further, it is recommended that a single PCM melting temperature for cooling the PV modules year-round in tropical climates is inappropriate, and instead, a cascaded structure with different PCM melting temperatures is recommended.