Computational Intelligence and Load Forecasting in Power Systems
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".
Deadline for manuscript submissions: closed (30 January 2022) | Viewed by 14672
Special Issue Editor
Interests: demand side management; forecasting; load profiling
Special Issue Information
Dear Colleagues,
Load forecasting is a key tool in the design of the electricity system. It refers to the process of predicting the amount of demand for electricity in an area and/or a transmission network over a period of time. The forecasting aims to determine the amount of electricity in a future time horizon. Load forecasting may correspond to a prediction of total energy, hourly load, peak load and load duration curve. Load forecasting explores issues such as the demand for installed capacity to meet potential demand growth, the type of energy resources to be used, the development of the transmission and distribution systems, the demand by type of consumer and by geographical area in order to implement demand side management measures and others.
Since power systems are gradually transforming to smart grids, new issues arise that can be addressed with robust forecasting algorithms. Also, the deregulation of electricity markets provide new opportunities for many market participants. Forecasting can aid to the strategic actions of market players to minimize risks and increase profits.
In the context of these challenges, the main scope of this Special Issue is to develop new algorithms for load forecasting. State-of-the-art papers together with innovative case studies are invited. Multi-disciplinary research and cutting-edge approaches are welcomed in order to address the challenges that are raised by power systems and electricity markets.
Asst. Prof. Dr. Ioannis Panapakidis
Guest Editor
Manuscript Submission Information
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Keywords
- deep learning
- shallow learning
- short-term forecasting
- medium-term forecasting
- long-term forecasting
- auto-regressive models
- neural networks