Research Topic

Frontier Research in Energy Forecasting

About this Research Topic

The basepoint of most of the studies inside the power and energy industry is the forecasted energy signals. By energy signals, here, we refer to the conventional ones such as load, price, PV and wind power generations as well as newly emerged ones like demand-response, EV charging demand, and also reactive ...

The basepoint of most of the studies inside the power and energy industry is the forecasted energy signals. By energy signals, here, we refer to the conventional ones such as load, price, PV and wind power generations as well as newly emerged ones like demand-response, EV charging demand, and also reactive power forecasting. The forecast values of the aforementioned signals play an important role in energy systems planning and designing, operation and control, maintenance and many other analysis and studies. Recent technological evolution in all parts of the energy industry caused a changing situation with a rapid dynamic which caused these predictions to be more complex than before. Moreover, the recent worldwide COVID-19 pandemic, which changed historical trends in all parts of society, attracts more attention to the importance of having precise and reliable forecasting tools in all industries, including the power and energy industry.

This Research Topic will target frontier research in the forecasting of energy signals. Both Review and Original Research articles are welcome. Real-world use cases discussing new application areas and resulting new developments are especially welcome. Considering the recent COVID-19 pandemic, research and experiences who hit this topic are particularly welcome. We invite papers and case reports from different energy-linked disciplines and utilities.

Topics that are of interest within the scope of this Research Topic include, but are not limited to:
1. The energy signals to be forecasted:
  a) Electric load forecasting;
  b) Electricity price forecasting;
  c) Renewable generation (e.g. PV (Solar) and wind power generations) forecasting;
  d) Demand-response forecasting;
  e) EV charging demand forecasting;
  f) Reactive power forecasting;
  g) Dynamic thermal rating forecasting.
2. Energy forecasting according to the power system pillars:
  a) Energy forecasting in smart grids;
  b) Energy forecasting in microgrids;
  c) Energy forecasting in the active buildings (nano-grids).
3. Energy forecasting of the different time horizon:
  a) Very-short-term energy forecasting;
  b) Short-term energy forecasting;
  c) Mid-term energy forecasting;
  d) Long-term energy forecasting.
4. Methods and approaches of the energy forecasting:
  a) Machine learning, artificial neural networks, and Statistical-based forecasting;
  b) Evolutionary and advanced computational approaches;
  c) Hybrid forecast engines;
  d) Hierarchical forecasters;
  e) Probabilistic forecasting;
  f) Bayesian approaches to forecasting;
  g) Real-time forecasting using big data;
  h) Quantum artificial intelligence application in forecasting;
  i) Deep learning application in forecasting;
  j) Reinforcement learning application in forecasting;
  k) Beyond point forecasting (e.g. interval forecasting, density forecasting);
  l) Forecasting based on the smart meters and IoT data.
5. Data processing and input data validation for the energy forecasting purpose:
  a) Data imputation;
  b) Data reduction;
  c) Data clustering;
  d) Data classification;
  e) Correlation analysis;
  f) Pattern recognition;
  g) Machine learning.
6. Energy forecasting based on exogenous data/parameters:
  a) Price-based load forecasting;
  b) Whether based energy forecasting;
  c) Solar forecasting considering the cloud movement;
  d) Solar forecasting based on the image processing
  e) Application of Lyapunov vectors for forecasting in coupled systems;
  f) Weather based dynamic thermal rating forecasting.


Keywords: load forecasting, renewable generation forecasting, machine learning, data analytics


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.

Recent Articles

Loading..

About Frontiers Research Topics

With their unique mixes of varied contributions from Original Research to Review Articles, Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author.

Topic Editors

Loading..

Submission Deadlines

08 December 2020 Manuscript
20 January 2021 Manuscript Extension

Participating Journals

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

Loading..

Topic Editors

Loading..

Submission Deadlines

08 December 2020 Manuscript
20 January 2021 Manuscript Extension

Participating Journals

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

Loading..
Loading..

total views article views article downloads topic views

}
 
Top countries
Top referring sites
Loading..