Research Topic

Forecasting Techniques for Energy Systems with Data Analytics and Machine Learning

About this Research Topic

Energy security and the integration of renewable energy resources has become a hot spot of concern to the international community. The stochastic nature of the resources and usage requires more detailed energy management in which forecasting techniques will play an important role. The use of data analytics technologies to forecast energy resources and usage is considered to be an effective means to handle this issue. Furthermore, the emergence of smart sensors allows energy companies to collect large-scale energy data, while how to utilize these data to solve practical problems is a matter worth discussing.

On this background, the present Research Topic of Frontiers in Energy Research will accept contributions in the recent advances on the forecasting techniques for energy systems with data analytics and machine learning techniques.

Topics of interest include, but are not limited to, the following:
1. Wind speed/power forecasting with data analytics technologies;
2. Photovoltaic power and solar irradiation prediction with machine learning techniques;
3. Load forecasting with data analytics technologies;
4. Market-based electricity price forecasting with data analytics technologies;
5. Forecasting-based optimal dispatching for energy management;
6. Probability/interval prediction for energy systems;
7. Data processing strategies for complex energy prediction issues;
8. Forecasting technologies for energy systems based on composite models;
9. Forecasting techniques for energy systems based on deep learning.


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.

Energy security and the integration of renewable energy resources has become a hot spot of concern to the international community. The stochastic nature of the resources and usage requires more detailed energy management in which forecasting techniques will play an important role. The use of data analytics technologies to forecast energy resources and usage is considered to be an effective means to handle this issue. Furthermore, the emergence of smart sensors allows energy companies to collect large-scale energy data, while how to utilize these data to solve practical problems is a matter worth discussing.

On this background, the present Research Topic of Frontiers in Energy Research will accept contributions in the recent advances on the forecasting techniques for energy systems with data analytics and machine learning techniques.

Topics of interest include, but are not limited to, the following:
1. Wind speed/power forecasting with data analytics technologies;
2. Photovoltaic power and solar irradiation prediction with machine learning techniques;
3. Load forecasting with data analytics technologies;
4. Market-based electricity price forecasting with data analytics technologies;
5. Forecasting-based optimal dispatching for energy management;
6. Probability/interval prediction for energy systems;
7. Data processing strategies for complex energy prediction issues;
8. Forecasting technologies for energy systems based on composite models;
9. Forecasting techniques for energy systems based on deep learning.


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

31 May 2021 Abstract
31 August 2021 Manuscript

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

31 May 2021 Abstract
31 August 2021 Manuscript

Participating Journals

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

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