Advanced Methods of Power Load Forecasting
This special issue belongs to the section "Energy Science and Technology".
Special Issue Information
Dear Colleagues,
Advanced societies are characterized by the intensive use of energy produced, distributed, and consumed in an uninterrupted, efficient, economic, reliable, and safe way. A case of special importance is electrical energy obtained as a result of a mix of fossil and renewable energy sources.
Predicting the power load is a crucial task for the proper functioning of the energy system within today’s liberalized energy markets. Improving the accuracy of prediction of energy demand as well as of peak loads to ensure the supply of energy by the energy system to end consumers has been of increasing interest to researchers in recent years.
The objective of this Special Issue is to present new, emerging methodologies that improve the traditional tools used in load forecasting. Artificial intelligence, machine learning, deep learning, and hybrid models are some of the new methods that can help improve decision-making in today’s energy markets, characterized by high uncertainty and volatility.
For this reason, we encourage researchers to submit their contributions in this field that represent advances in current scientific knowledge along with practical and/or real applications.
Prof. J.Carlos García-Díaz
Prof. Óscar Trull
Guest Editors
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Keywords
- Short-term load forecasting
- Statistical forecasting models (ARIMA, ARMAX, exponential smoothing, linear and non-linear regression)
- Advanced forecasting methods
- Artificial neural networks
- Fuzzy regression models
- Tree-based regression methods
- Stacked and ensemble machine learning methods
- Deep learning architectures
- Support vector regression
- Hybrid and ensemble machine learning
- Adaptive load forecasting
- Advanced optimization methods for energy demand forecast
- Peak load forecasting
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Published Papers
Appl. Sci. - ISSN 2076-3417