Research Topic

Artificial Intelligence Applications to Energy Systems

About this Research Topic

The transformation of energy systems to make them fit for a zero carbon future poses many technological, organizational and socio-economic challenges. These emanate from electrification of existing heating and transport, understanding the impact of increased demand and intermittent generation on existing power and energy system assets, informing the business processes that procure energy and quantifying weather related risks. Many important problems in these areas are currently being studied across technical, social and environmental sciences, common to all of these is the greater availability of data. The application of machine learning (ML) and artificial intelligence (AI) spans these disciplines, offering powerful tools and methods for aiding the understanding of the new data streams that are now being generated from energy systems and unlocking the operational value from them.

Examples of topics of interest relevant to this Article Collection may include, but are not limited to:

• Demonstration of smart grid technologies by means of application cases and experiments that illustrate current consumption patterns and their spatial distribution;
• Modelling and forecast of production and consumption with future energy pricing concepts;
• Power plant and electric grid operation, such as modelling of energy production at different time scales, asset health monitoring, and the optimization of system operation and life cycle.

All of these topics are currently experiencing a large flow of data from smart meters, environmental monitoring systems, renewable energy power plants, electricity market, electrical grid monitoring systems, as well as physics-based models. Thus, novel applications of state-of-the-art methods for supervised and unsupervised classification, time series modelling and forecasting, computer vision, optimization with feedback from data are expected. For the sake of strengthening analyses and results from new methods, comparisons are encouraged. Similarly, aspects of data and model selection, and hyperparameter tuning.

Transforming the way we produce and consume energy to reduce our environmental footprint, requires also social and economic aspects to be taken into account. Such aspects of the energy transition also benefit from a large influx of data. Thus, themes such as environmental benefits of electrification, management of energy needs at the city, cantonal, national or even inter-country level, and analyses and modelling of environmental data are welcome in this Article Collection, as they can yield new valuable feedback to inform policymakers. Contributions may also include a diversity of heterogeneous data, including time series, images, and unstructured data.

The contributions presented at Applied Machine Learning Days (AMLD) are welcome, particularly the "AI & Energy" track at AMLD2020 and “AI & Sustainable Energy” at AMLD2021 (26th of April 2021). Additionally, contributions fitting in the scope of the Topic as outlined above are also encouraged. Submission of abstracts in advance is welcome but not mandatory for manuscript submission.


Keywords: artificial intelligence, smart grid, energy simulation, machine learning, deep learning, hybrid models, electrical grid, smart meters, thermal grid


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

The transformation of energy systems to make them fit for a zero carbon future poses many technological, organizational and socio-economic challenges. These emanate from electrification of existing heating and transport, understanding the impact of increased demand and intermittent generation on existing power and energy system assets, informing the business processes that procure energy and quantifying weather related risks. Many important problems in these areas are currently being studied across technical, social and environmental sciences, common to all of these is the greater availability of data. The application of machine learning (ML) and artificial intelligence (AI) spans these disciplines, offering powerful tools and methods for aiding the understanding of the new data streams that are now being generated from energy systems and unlocking the operational value from them.

Examples of topics of interest relevant to this Article Collection may include, but are not limited to:

• Demonstration of smart grid technologies by means of application cases and experiments that illustrate current consumption patterns and their spatial distribution;
• Modelling and forecast of production and consumption with future energy pricing concepts;
• Power plant and electric grid operation, such as modelling of energy production at different time scales, asset health monitoring, and the optimization of system operation and life cycle.

All of these topics are currently experiencing a large flow of data from smart meters, environmental monitoring systems, renewable energy power plants, electricity market, electrical grid monitoring systems, as well as physics-based models. Thus, novel applications of state-of-the-art methods for supervised and unsupervised classification, time series modelling and forecasting, computer vision, optimization with feedback from data are expected. For the sake of strengthening analyses and results from new methods, comparisons are encouraged. Similarly, aspects of data and model selection, and hyperparameter tuning.

Transforming the way we produce and consume energy to reduce our environmental footprint, requires also social and economic aspects to be taken into account. Such aspects of the energy transition also benefit from a large influx of data. Thus, themes such as environmental benefits of electrification, management of energy needs at the city, cantonal, national or even inter-country level, and analyses and modelling of environmental data are welcome in this Article Collection, as they can yield new valuable feedback to inform policymakers. Contributions may also include a diversity of heterogeneous data, including time series, images, and unstructured data.

The contributions presented at Applied Machine Learning Days (AMLD) are welcome, particularly the "AI & Energy" track at AMLD2020 and “AI & Sustainable Energy” at AMLD2021 (26th of April 2021). Additionally, contributions fitting in the scope of the Topic as outlined above are also encouraged. Submission of abstracts in advance is welcome but not mandatory for manuscript submission.


Keywords: artificial intelligence, smart grid, energy simulation, machine learning, deep learning, hybrid models, electrical grid, smart meters, thermal grid


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

05 March 2021 Manuscript
26 April 2021 Manuscript Extension

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

05 March 2021 Manuscript
26 April 2021 Manuscript Extension

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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