The increasing pressure from energy and environmental protection has made research into advanced energy infrastructures, such as smart grids, intelligent transportation networks, heat and natural gas networks, etc. urgent. To this end, the Integrated Energy System (IESs), which focuses on the deep integration of advanced multi-energy and information technologies, is considered one of the most popular forms of resilient energy utilization for future development. Based on intelligent perception and cyber communication, the IES realizes the deep integration and real-time interaction of calculation, communication, and artificial intelligence. In addition, the IES will generate and utilize a wealth of data to achieve optimal energy distribution. There are many challenges that require further research and development on modeling, planning, control, and optimization of distributed information and energy systems, which may include interdependent infrastructures such as power, transportation, communications, thermal, and natural gas networks. This research topic will bring together researchers from different fields and specializations, such as power engineering, energy systems, communications engineering, computer science, transportation engineering, mathematics, and specialists in areas related to IES technologies.
Topics of interest include but are not limited to the following:
1. Applications of advanced data-driven artificial intelligence techniques in IES optimization, such as Convolutional Neural Network, Generative Adversarial Networks, Recurrent Neural Network, and so on.
2. Data-driven artificial intelligence for residential solar and thermal control.
3. Modern data-driven artificial intelligence-based approaches for renewable energy source, load, and electricity price prediction.
4. Modern data-driven artificial intelligence methods for condition assessment and fault detection of IES systems.
5. Applications of advanced data-driven artificial intelligence methods to the IES market to address the increasing penetration of renewable energy sources.
6. Machine learning applications for predictive maintenance and asset management.
7. Data preservation, data mining, and information extraction from IESs data.
The increasing pressure from energy and environmental protection has made research into advanced energy infrastructures, such as smart grids, intelligent transportation networks, heat and natural gas networks, etc. urgent. To this end, the Integrated Energy System (IESs), which focuses on the deep integration of advanced multi-energy and information technologies, is considered one of the most popular forms of resilient energy utilization for future development. Based on intelligent perception and cyber communication, the IES realizes the deep integration and real-time interaction of calculation, communication, and artificial intelligence. In addition, the IES will generate and utilize a wealth of data to achieve optimal energy distribution. There are many challenges that require further research and development on modeling, planning, control, and optimization of distributed information and energy systems, which may include interdependent infrastructures such as power, transportation, communications, thermal, and natural gas networks. This research topic will bring together researchers from different fields and specializations, such as power engineering, energy systems, communications engineering, computer science, transportation engineering, mathematics, and specialists in areas related to IES technologies.
Topics of interest include but are not limited to the following:
1. Applications of advanced data-driven artificial intelligence techniques in IES optimization, such as Convolutional Neural Network, Generative Adversarial Networks, Recurrent Neural Network, and so on.
2. Data-driven artificial intelligence for residential solar and thermal control.
3. Modern data-driven artificial intelligence-based approaches for renewable energy source, load, and electricity price prediction.
4. Modern data-driven artificial intelligence methods for condition assessment and fault detection of IES systems.
5. Applications of advanced data-driven artificial intelligence methods to the IES market to address the increasing penetration of renewable energy sources.
6. Machine learning applications for predictive maintenance and asset management.
7. Data preservation, data mining, and information extraction from IESs data.
