scholarly journals Artificial intelligence based prognostic maintenance of renewable energy systems: A review of techniques, challenges, and future research directions

2021 ◽  
Author(s):  
Yasir Saleem Afridi ◽  
Kashif Ahmad ◽  
Laiq Hassan
Keyword(s):  
Artificial Intelligence ◽  
Renewable Energy ◽  
Energy Systems ◽  
Future Research ◽  
Research Directions ◽  
Renewable Energy Systems ◽  
Future Research Directions

Artificial Intelligence Review

2019 ◽  
pp. 23-39
Author(s):  
Amal Kilani ◽  
Ahmed Ben Hamida ◽  
Habib Hamam
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Intelligent System ◽  
Route Planning ◽  
Turing Test ◽  
Future Research ◽  
Interaction Data ◽  
Research Directions ◽  
Future Research Directions

In this chapter, the authors present a profound literature review of artificial intelligence (AI). After defining it, they briefly cover its history and enumerate its principal fields of application. They name, for example, information system, commerce, image processing, human-computer interaction, data compression, robotics, route planning, etc. Moreover, the test that defines an artificially intelligent system, called the Turing test, is also defined and detailed. Afterwards, the authors describe some AI tools such as fuzzy logic, genetic algorithms, and swarm intelligence. Special attention will be given to neural networks and fuzzy logic. The authors also present the future research directions and ethics.

Artificial Intelligence

2019 ◽  
pp. 13-22
Author(s):  
Steven Walczak
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
Future Research ◽  
Human Intelligence ◽  
Research Directions ◽  
Intelligent Machines ◽  
History Of ◽  
Future Research Directions ◽  
New Research

Artificial intelligence is the science of creating intelligent machines. Human intelligence is comprised of numerous pieces of knowledge as well as processes for utilizing this knowledge to solve problems. Artificial intelligence seeks to emulate and surpass human intelligence in problem solving. Current research tends to be focused within narrow, well-defined domains, but new research is looking to expand this to create global intelligence. This chapter seeks to define the various fields that comprise artificial intelligence and look at the history of AI and suggest future research directions.

scholarly journals Artificial Intelligence Techniques for Cognitive Sensing in Future IoT: State-of-the-Art, Potentials, and Challenges

2020 ◽  
Vol 9 (2) ◽  
pp. 21 ◽  
Author(s):  
Martins O. Osifeko ◽  
Gerhard P. Hancke ◽  
Adnan M. Abu-Mahfouz
Keyword(s):  
Artificial Intelligence ◽  
Data Collection ◽  
Energy Efficient ◽  
State Of The Art ◽  
Future Internet ◽  
Future Research ◽  
Research Directions ◽  
Efficient Data ◽  
Future Research Directions ◽  
Efficient Data Collection

Smart, secure and energy-efficient data collection (DC) processes are key to the realization of the full potentials of future Internet of Things (FIoT)-based systems. Currently, challenges in this domain have motivated research efforts towards providing cognitive solutions for IoT usage. One such solution, termed cognitive sensing (CS) describes the use of smart sensors to intelligently perceive inputs from the environment. Further, CS has been proposed for use in FIoT in order to facilitate smart, secure and energy-efficient data collection processes. In this article, we provide a survey of different Artificial Intelligence (AI)-based techniques used over the last decade to provide cognitive sensing solutions for different FIoT applications. We present some state-of-the-art approaches, potentials, and challenges of AI techniques for the identified solutions. This survey contributes to a better understanding of AI techniques deployed for cognitive sensing in FIoT as well as future research directions in this regard.

scholarly journals Thermodynamic analysis and kinetic optimization of high-energy batteries based on multi-electron reactions

2020 ◽  
Vol 7 (8) ◽  
pp. 1367-1386 ◽  
Author(s):  
Yong-Xin Huang ◽  
Feng Wu ◽  
Ren-Jie Chen
Keyword(s):  
Electrode Materials ◽  
Energy Systems ◽  
Material Design ◽  
High Energy ◽  
Future Research ◽  
Research Directions ◽  
High Theoretical Capacity ◽  
Future Research Directions ◽  
High Output Voltage ◽  
High Output

Abstract Multi-electron reaction can be regarded as an effective way of building high-energy systems (>500 W h kg−1). However, some confusions hinder the development of multi-electron mechanisms, such as clear concept, complex reaction, material design and electrolyte optimization and full-cell fabrication. Therefore, this review discusses the basic theories and application bottlenecks of multi-electron mechanisms from the view of thermodynamic and dynamic principles. In future, high-energy batteries, metal anodes and multi-electron cathodes are promising electrode materials with high theoretical capacity and high output voltage. While the primary issue for the multi-electron transfer process is sluggish kinetics, which may be caused by multiple ionic migration, large ionic radius, high reaction energy barrier, low electron conductivity, poor structural stability, etc., it is urgent that feasible and versatile modification methods are summarized and new inspiration proposed in order to break through kinetic constraints. Finally, the remaining challenges and future research directions are revealed in detail, involving the search for high-energy systems, compatibility of full cells, cost control, etc.

scholarly journals Machine learning for energy-water nexus: challenges and opportunities

2018 ◽  
Vol 2 (3) ◽  
pp. 228-267 ◽  
Author(s):  
Zaidi ◽  
Chandola ◽  
Allen ◽  
Sanyal ◽  
Stewart ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Systems ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Technological Advancement ◽  
Research Directions ◽  
Learning Techniques ◽  
Challenges And Opportunities ◽  
Future Research Directions ◽  
System Sustainability

Modeling the interactions of water and energy systems is important to the enforcement of infrastructure security and system sustainability. To this end, recent technological advancement has allowed the production of large volumes of data associated with functioning of these sectors. We are beginning to see that statistical and machine learning techniques can help elucidate characteristic patterns across these systems from water availability, transport, and use to energy generation, fuel supply, and customer demand, and in the interdependencies among these systems that can leave these systems vulnerable to cascading impacts from single disruptions. In this paper, we discuss ways in which data and machine learning can be applied to the challenges facing the energy-water nexus along with the potential issues associated with the machine learning techniques themselves. We then survey machine learning techniques that have found application to date in energy-water nexus problems. We conclude by outlining future research directions and opportunities for collaboration among the energy-water nexus and machine learning communities that can lead to mutual synergistic advantage.

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