Manuscript Topics

A major global challenge is the shift from fossil fuels to alternative power resources, which necessitates advancements in substances, technologies, and strategies for the effective gathering, storing, converting, and managing of energy from nature. Machine learning (ML) approaches have been introduced by energy experts to expedite these advancements. The computational domain of prospective substances must be used to select suitable substance prospects for energy conservation exploration. These possibilities must then be generated in sufficient quantity and quality for usage in gadgets. Machine learning (ML) models have several applications: they can be employed to create novel substance constructions with the intended characteristics, anticipate particular attributes of new materials without expensive identification, perceive designs in the creation and usage of clean energy sources, and enhance energy utilisation at both device and grid levels in establishing policies regarding energy. Creating efficient resources that are prepared to receive market adoption is the main objective of material innovation.

A novel material must undergo extensive study for as long as two decades before it can be commercialised, hence, the aim of any quicker strategy should be to achieve industrialization one order of magnitude faster. Analyzing the case of vaccine development can be beneficial for the subject of substance physics. The approach and guiding concepts of machine learning for energy are similar to those of machine learning for other domains, such as pharmaceuticals. In actuality, though, ML models for different platforms are subject to extra-special criteria. For instance, machine learning simulations intended for use in health care typically feature intricate architectures that consider legal compliance and guarantee the secure creation, operation, and observation of systems. The efficacy of supervision and unsupervised learning approaches to support power system optimisation is assessed in this work. Using a method of supervised learning, an alternative model is created to avoid using the extremely computational Actual Engineering Model (AEM). When combining wind turbines with solar power innovations, energy distribution networks can be quite important. The ability of dispersed systems of energy to integrate non-dispatchable energy sources with minimal influence on the grid has given rise to a number of diverse notions, including integrated electricity systems, smart miniature grids, energy hubs, virtualized plants, and more.

In recent times, optimising issues have been solved more quickly through the use of surrogacy theories, also known as meta-models, in a variety of domains. The AEM, which requires greater processing time when transferring choice space components into objective distance, is circumvented by using an alternate model. The transportation technique was not considered in any of the earlier investigations when creating a substitute model. Taking the transportation method into account will greatly increase the number of deciding variables and the complexity of the substitute model. Therefore, due to approximation constraints, relying solely on the surrogate model may result in less-than-ideal solutions for design. Because this has not been considered in the current state of the art from the standpoint of the electricity system approach, it is crucial to investigate potential techniques that can be utilised to merge alternative models with AEMs throughout the optimisation process. We invite submissions and article proposals for this Machine Learning in Energy Optimization for New Energy Solutions.

The special issue invites the original contributions, but not limited to, the themes and topics in following areas of research:
• Techniques for machine learning to support energy system optimization
• Identifying features for energy-saving applications in machine learning prediction models
• A comprehensive analysis of machine learning and meta-heuristic methods for renewable energy
• Grid optimization and alternative power systems: enabling dispersed solutions
• Optimization of high-temperature reservoir electrical energy storage using machine learning support
• An analysis of deep reinforcement learning for controlling energy in smart buildings
• Advanced deep learning for hybrid real-time management of energy
• Machine learning-powered intelligent energy systems: Present developments and fresh viewpoints
• An overview of machine learning for estimating building efficiency and efficiency
• An adaptive learning system for power grid upkeep and service optimisation.

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