This paper conducts a comprehensive study on the application of big data and machine learning in the electrical power grid, introduced through the emergence of the next-generation power system – the smart grid (SG). Connectivity lies at the core of this new grid infrastructure, which is provided by the internet of things (IoT). This connectivity, and constant communication required in this system, also introduced a massive data volume that demands techniques far superior than conventional methods for proper analysis and decision-making. IoT integrated SG system can provide efficient load forecasting and data acquisition technique, along with cost effectiveness. Big data analysis and machine learning techniques are essential to reap these benefits. In the complex connected system of SG, cyber security becomes a critical issue; IoT devices and their data turning into major targets of attacks. Such security concerns and their solutions are also included in this study. Key information obtained through literature review are tabulated in corresponding sections to provide a clear synopsis; and the findings of this rigorous review are listed to give a concise picture of this area of study and promising future fields of academic and industrial research, with current limitations with viable solutions along with their effectiveness.

The number of used batteries is increasing in quantity as time passes by, and this amount is to expand drastically, as electric vehicles are getting increasingly popular. Proper disposal of the spent batteries has always been a concern, but it has also been discovered that these batteries often retain enough energy perfectly suited for other uses, which can extend the batteries' operational lifetime into a second one. Such use of batteries has been termed as the “second-life,” and it is high time to adopt such usage in large scale to properly exploit the energy and economics that went into battery production and reduce the environmental impacts of battery waste ending up in landfills. This paper aids in that quest by providing a complete picture of the current state of the second-life battery (SLB) technology by reviewing all the prominent work done in this field previously. The second-life background, manufacturing process of energy storage systems using the SLBs, applications, and impacts of this technology, required business strategies and policies, and current barriers of this technology along with potential solutions are discussed in detail in this paper to act as a major stepping stone for future research in this ever-expanding field.

Load forecasting is a pivotal part of the power utility companies. To provide load-shedding free and uninterrupted power to the consumer, decision-makers in the utility sector must forecast the future demand for electricity with a minimum amount of error percentage. Load prediction with less percentage of error can save millions of dollars to the utility companies. There are numerous Machine Learning (ML) techniques to amicably forecast the demand of electricity among which the hybrid models show the best result. Two or more than two predictive models are amalgamated to design a hybrid model, each of which provides improved performances by the merit of individual algorithms. This paper reviews the current state-of-the-art of electric load forecasting technologies and presents recent works pertaining to the combination of different ML algorithms into two or more methods for the construction of hybrid models. A comprehensive study of each single and multiple load forecasting model is performed with an in-depth analysis of their advantages, disadvantages, and functions. A comparison between their performance in terms of Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) values are developed with pertinent literature of several models to aid the researchers with the selection of suitable models for load prediction.

Driven by global concerns about the climate and the environment, the world is opting for renewable energy sources (RESs), such as wind and solar. However, RESs suffer from the discredit of intermittency, for which energy storage systems (ESSs) are gaining popularity worldwide. Surplus energy obtained from RESs can be stored in several ways, and later utilized during periods of intermittencies or shortages. The idea of storing excess energy is not new, and numerous researches have been conducted to adorn this idea with innovations and improvements. This review is a humble attempt to assemble all the available knowledge on ESSs to benefit novice researchers in this field. This paper covers all core concepts of ESSs, including its evolution, elaborate classification, their comparison, the current scenario, applications, business models, environmental impacts, policies, barriers and probable solutions, and future prospects. This elaborate discussion on energy storage systems will act as a reliable reference and a framework for future developments in this field. Any future progress regarding ESSs will find this paper a helpful document wherein all necessary information has been assembled.

Recent technological advances enable gripper-equipped robots to perform many tasks traditionally associated with the human hand, allowing the use of grippers in a wide range of applications. Depending on the application, an ideal gripper design should be affordable, energy-efficient, and adaptable to many situations. However, regardless of the number of grippers available on the market, there are still many tasks that are difficult for grippers to perform, which indicates the demand and room for new designs to compete with the human hand. Thus, this paper provides a comprehensive review of robotic arm grippers to identify the benefits and drawbacks of various gripper designs. The research compares gripper designs by considering the actuation mechanism, degrees of freedom, grasping capabilities with multiple objects, and applications, concluding which should be the gripper design with the broader set of capabilities.

Background: Building control architecture that balances the assistive manipulation systems with the benefits of direct human control is a crucial challenge of human–robot collaboration. It promises to help people with disabilities more efficiently control wheelchair and wheelchair-mounted robot arms to accomplish activities of daily living.Methods: In this study, our research objective is to design an eye-tracking assistive robot control system capable of providing targeted engagement and motivating individuals with a disability to use the developed method for self-assistance activities of daily living. The graphical user interface is designed and integrated with the developed control architecture to achieve the goal.Results: We evaluated the system by conducting a user study. Ten healthy participants performed five trials of three manipulation tasks using the graphical user interface and the developed control framework. The 100% success rate on task performance demonstrates the effectiveness of our system for individuals with motor impairments to control wheelchair and wheelchair-mounted assistive robotic manipulators.Conclusions: We demonstrated the usability of using this eye-gaze system to control a robotic arm mounted on a wheelchair in activities of daily living for people with disabilities. We found high levels of acceptance with higher ratings in the evaluation of the system with healthy participants.