The increasing use of non-dispatchable Renewable Energy Sources (RES) requires that the load-generation balance is no longer exclusively addressed in a centralized way and driven by rigid demand. Consumers gain new roles and importance because their distributed generation capability and demand flexibility can mitigate RES uncertainty and quick variation. However, consumers do not have sufficient experience or technical knowledge to manage their generation and demand flexibility properly. The lack of decision support and automated solutions to support them is the main barrier to benefiting from the great potential of consumers’ participation. Existing solutions to support consumers in energy management (EM) and trading are limited in terms of intelligence and automation, while most consumers do not have enough knowledge and trust to use the available solutions. The explainibility of intelligent decision support models becomes, thereby, essential to motivate consumers' widespread use of such tools.
Despite the promising advances based on Artificial Intelligence (AI), particularly on Machine Learning (ML) and Knowledge-Based Systems (KBS), the conception and development of adequate decision support models for energy management and trading is still limited, as well as the interpretability of such models. This Research Topic addresses the most recent advances regarding explainability models for intelligent decision support and management systems in the scope of power and energy systems. The goal is to bring together the most recent and relevant contributions on Explainable AI that enable improving the acceptability, trust and willingness of users to adopt advanced models in this domain, as a way to foster their widespread use. The Research Topic comprises both theoretical conceptual models and applicational models that constitute significant contributions to the body of knowledge on explainability of AI-based solutions related to energy management and decision support.
This Research Topic focuses on models, solutions, methodologies, approaches and tools on the most recent investigations and studies to address the technical issues and research gaps within applications of explainable AI in ML techniques and knowledge-based systems in the scope of smart grids. Researchers and experts worldwide are welcomed to submit high-quality articles of the following types: Hypothesis and Theory, Methods, Original Research, Review, and Technology and Code.
Potential topics include, but are not limited to the proposal or application of explainability models for:
• Advanced computational, simulation, and analysis methods for large-scale systems
• Advanced and multi-objective optimization methods
• Artificial Intelligence-based methods for energy management and trading
• Asset and network management under uncertainty
• Cyber-secure energy management
• Data-driven control of resources
• Data sharing, privacy preservation, and data markets for energy
• Demand Response
• Innovative data sources and data preparation for energy management
• Machine learning methods for energy management and decision support
• Real-time and very short-term energy management
• Robust optimization under multiple uncertainties
The increasing use of non-dispatchable Renewable Energy Sources (RES) requires that the load-generation balance is no longer exclusively addressed in a centralized way and driven by rigid demand. Consumers gain new roles and importance because their distributed generation capability and demand flexibility can mitigate RES uncertainty and quick variation. However, consumers do not have sufficient experience or technical knowledge to manage their generation and demand flexibility properly. The lack of decision support and automated solutions to support them is the main barrier to benefiting from the great potential of consumers’ participation. Existing solutions to support consumers in energy management (EM) and trading are limited in terms of intelligence and automation, while most consumers do not have enough knowledge and trust to use the available solutions. The explainibility of intelligent decision support models becomes, thereby, essential to motivate consumers' widespread use of such tools.
Despite the promising advances based on Artificial Intelligence (AI), particularly on Machine Learning (ML) and Knowledge-Based Systems (KBS), the conception and development of adequate decision support models for energy management and trading is still limited, as well as the interpretability of such models. This Research Topic addresses the most recent advances regarding explainability models for intelligent decision support and management systems in the scope of power and energy systems. The goal is to bring together the most recent and relevant contributions on Explainable AI that enable improving the acceptability, trust and willingness of users to adopt advanced models in this domain, as a way to foster their widespread use. The Research Topic comprises both theoretical conceptual models and applicational models that constitute significant contributions to the body of knowledge on explainability of AI-based solutions related to energy management and decision support.
This Research Topic focuses on models, solutions, methodologies, approaches and tools on the most recent investigations and studies to address the technical issues and research gaps within applications of explainable AI in ML techniques and knowledge-based systems in the scope of smart grids. Researchers and experts worldwide are welcomed to submit high-quality articles of the following types: Hypothesis and Theory, Methods, Original Research, Review, and Technology and Code.
Potential topics include, but are not limited to the proposal or application of explainability models for:
• Advanced computational, simulation, and analysis methods for large-scale systems
• Advanced and multi-objective optimization methods
• Artificial Intelligence-based methods for energy management and trading
• Asset and network management under uncertainty
• Cyber-secure energy management
• Data-driven control of resources
• Data sharing, privacy preservation, and data markets for energy
• Demand Response
• Innovative data sources and data preparation for energy management
• Machine learning methods for energy management and decision support
• Real-time and very short-term energy management
• Robust optimization under multiple uncertainties
