The rapid development of various renewable energy sources such as wind, solar, and energy storage technologies, as well as the integration of demand response, combined cooling, heating and power gas turbine, etc, have greatly changed the current power and energy production and consumption. In the meantime, advanced sensors, communication and control technologies have increased the potential for the economic and reliable operation of modern power and energy systems with the integration of the fluctuating renewable energies. A large amount of data can be collected from the generation side, the grid side and the demand side. How to effectively utilize the big data of all the entities to ensure energy adequacy, system security, reliability and stability, remains a great challenge for power and energy system researchers.
In the mean time, the implementation of various advanced machine learning and artificial intelligence technologies are essential to solving the planning, scheduling and control problems in the whole process of energy supply and demand. Data-driven techniques have been successfully developed and widely used to solve the classification, regression and clustering problems in the power and energy area, including generation and load forecasting, power and energy planning, economic dispatch, state estimation, situational awareness, stability evaluation, etc. Such problems are generally complicated and difficult to solve with traditional model-based techniques, such that advanced big data, machine learning and artificial intelligence techniques are required.
This Research Topic welcomes Original Research and Review articles about advanced data-driven methods and applications applied to power and energy systems. Target readers include both academic researchers and power and energy industry engineers. This Research Topic also aims to provide a platform to promote state-of-the-art research methods and results in related fields. All submitted papers must be written in excellent English and contain only original works, which have not been published by or are currently under review for any other journals or conferences.
Potential topics include but are not limited to the Data-Driven Techniques in the following fields:
• Renewable energy generation, multi-energy load consumption, and energy price forecast
• Demand response
• Energy storage applications
• Integrated energy system applications
• Smart grid and microgrid technologies
• Smart home, smart building and smart city applications
• Cyber physical system security applications
• Power and energy system planning, operation, protection and controls
• Energy economic dispatch
• Electricity markets and energy markets
The rapid development of various renewable energy sources such as wind, solar, and energy storage technologies, as well as the integration of demand response, combined cooling, heating and power gas turbine, etc, have greatly changed the current power and energy production and consumption. In the meantime, advanced sensors, communication and control technologies have increased the potential for the economic and reliable operation of modern power and energy systems with the integration of the fluctuating renewable energies. A large amount of data can be collected from the generation side, the grid side and the demand side. How to effectively utilize the big data of all the entities to ensure energy adequacy, system security, reliability and stability, remains a great challenge for power and energy system researchers.
In the mean time, the implementation of various advanced machine learning and artificial intelligence technologies are essential to solving the planning, scheduling and control problems in the whole process of energy supply and demand. Data-driven techniques have been successfully developed and widely used to solve the classification, regression and clustering problems in the power and energy area, including generation and load forecasting, power and energy planning, economic dispatch, state estimation, situational awareness, stability evaluation, etc. Such problems are generally complicated and difficult to solve with traditional model-based techniques, such that advanced big data, machine learning and artificial intelligence techniques are required.
This Research Topic welcomes Original Research and Review articles about advanced data-driven methods and applications applied to power and energy systems. Target readers include both academic researchers and power and energy industry engineers. This Research Topic also aims to provide a platform to promote state-of-the-art research methods and results in related fields. All submitted papers must be written in excellent English and contain only original works, which have not been published by or are currently under review for any other journals or conferences.
Potential topics include but are not limited to the Data-Driven Techniques in the following fields:
• Renewable energy generation, multi-energy load consumption, and energy price forecast
• Demand response
• Energy storage applications
• Integrated energy system applications
• Smart grid and microgrid technologies
• Smart home, smart building and smart city applications
• Cyber physical system security applications
• Power and energy system planning, operation, protection and controls
• Energy economic dispatch
• Electricity markets and energy markets