Hybrid Artificial Intelligence and Machine Learning Techniques in Renewable Energy Systems

Submission Deadline: 30 December 2022 (closed) View: 134

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Summary

In the recent century, renewable energy systems have undergone a tremendous change globally by applying various technological trends. Indeed, hybrid AI (Artificial Intelligence) and ML (Machine Learning) techniques are gaining attention among researchers for their distinctive attributes. Modest AI techniques play a quintessential role in analysing, predicting, and activating the performance of renewable energy resources. Hybrid AI techniques also aid in enhancing the control power of renewable energy resources to a greater extent. Thus, technological implications pave the way for new formulations of energy systems.

The necessity of incorporating hybrid AI and ML techniques in power systems holds the potentiality of transfiguring a conventional power generation system to a low-cost, efficient renewable energy system. In addition, both hybrid AI and ML techniques empower a paradigm shift from market-based commodity to market-based technological developments. Indeed, the implication of ML techniques in renewable energy systems is expected to increase the feasibility of RES (Renewable Energy System) to a considerable extent. In specific, ML techniques are found to predict the rate of energy consumption, analyses weather conditions, track market demands of renewable energy sources and identify potential applications and issues appreciably. Eventually, AI is considered the game-changer for the development of renewable energy since it holds the ability to unravel the higher potentials of renewable energy sources that further aids in higher energy conservation. The influential prediction abilities of artificial intelligence and machine learning are expected to enhance the efficacy and demands of energy forecasting systems. Furthermore, the automation power of AI can direct operational efficiency in many energy-assisted sectors considerably. However, incorporating such technologies in energy systems aids in the construction of smart, centralised control networks, an efficient combination of microgrids, etc. Further, it enhances the safety and reliability measures of renewable energy systems.

The challenges and future outlook of implementing advanced features of AI and ML in renewable energy systems indulge the concerns of factors such as resource location, power quality supplies, and cost issues. Limitations include privacy threats, data vulnerability, data theft, hacking issues, storage problems, etc. Future research focuses on factors that aid to overcome the limitations described above considerably. Researchers and policymakers are most invited to present a theoretical research framework against this background. The special issue provides various opportunities for scholars to discuss secure methodologies for implementing technologies in the renewable energy system.

List of potential topics of the special issue include, but are not limited to the following:

· Emerging trends and applications of hybrid AI and ML in renewable energy systems

· Fundamental and applications of artificial intelligence in renewable energy sources

· Frontier applications of AI and ML in monitoring energy sources

· Insights of AI and ML applications in the renewable energy system

· Limitations and drawbacks of ML applications in the renewable energy system

· Need of substantial policies for the incorporation of AI and ML algorithms in the renewable energy system

· Recent trends and perspectives for the incorporation of AI and ML in the renewable energy systems

· New trends and policies in the enhancement of AI in the development of renewable energy resources

· Research statistics of smart AI applications in the development of renewable energy sources

· Contribution of governmental organisations and non-governmental organisations for the enhancement of renewable energy system

· Future research perspectives of the implication of AI in the renewable energy systems

· Applications of AI and ML in renewable energy sources

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