Intelligent Forecasting and Optimization in Electrical Power Systems: Advances in Models and Applications
1. Introduction
- AI/ML/deep learning for forecasting electricity generation from RESs;
- AI/ML/deep learning for forecasting power demand for electrical power systems;
- Optimization of electrical power systems;
- Forecasting of meteorological data (wind speed and solar radiation) that is important for forecasting electricity generation from RESs;
- Statistical analyses of data for forecasting models (including problems related to big, missing, distorted, and uncertain data);
- Reliability of electrical power systems.
2. Summary of the Contributions
2.1. Electricity Demand Forecasting
2.1.1. Relevance of the Subject
2.1.2. Main Forecasting Problems
- Very short-term forecasting, which refers to the prediction of electricity demand or production for a time horizon from seconds to a few hours ahead.
- Short-term forecasting, which refers to predicting electricity demand in the immediate future, usually up to several days ahead.
- Medium-term forecasting, which involves predicting electricity demand for a period of a few weeks to a few months ahead.
- Long-term forecasting, which involves predicting electricity demand for a period several years in advance.
- Peak electricity demand forecasting, which is forecasting of the highest level of electricity consumption within a particular period, typically daily or yearly.
- Seasonal forecasting, which involves predicting the electricity demand for different seasons of the year.
- Special event forecasting, which involves predicting the electricity demand for special events, such as holidays, sports events, or festivals.
- Probabilistic forecasting, which is forecasting of not only a single predicted value, but also a probability distribution or range of potential values with their associated probabilities.
- Uncertainty forecasting, which involves predicting electricity demand in the presence of uncertainty, such as changes in weather patterns, economic conditions, or energy policies.
- Seasonality and trends. There can be significant seasonality and trends in electricity demand, such as increased usage during hot summer months or the growth in the electricity market over time.
- Volatility. Electricity demand can be volatile and subject to unexpected changes due to weather events, economic conditions, or other unforeseen factors.
- Data quality. Electricity demand data may be incomplete or contain errors, which can affect the accuracy of forecasting models.
- Non-linear relationships. There may be non-linear relationships between electricity demand and various factors such as temperature, time of day, and day of the week.
- Uncertainty. The accuracy of forecasting models can be affected by uncertainty around future events or conditions, such as changes in regulations or the introduction of new technologies.
2.1.3. Overview of Article Content
2.2. Wind Power Forecasting
2.2.1. Relevance of the Subject
2.2.2. Main Forecasting Problems
2.2.3. Overview of Article Content
2.3. Photovoltaic Power Forecasting
2.3.1. Relevance of the Subject
- Control of microgrid elements (sources, receivers, and storage), especially important and difficult in islanded operation mode;
- Energy market participation of PV source operators;
- Control and operation planning of conventional electricity sources (transmission system operator level);
- Control and operation planning of power grids (distribution system operator level).
2.3.2. Main Forecasting Problems
2.3.3. Overview of Article Content
2.4. Optimization
2.4.1. Relevance of the Subject
2.4.2. Overview of Article Content
Author Contributions
Funding
Conflicts of Interest
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Dudek, G.; Piotrowski, P.; Baczyński, D. Intelligent Forecasting and Optimization in Electrical Power Systems: Advances in Models and Applications. Energies 2023, 16, 3024. https://doi.org/10.3390/en16073024
Dudek G, Piotrowski P, Baczyński D. Intelligent Forecasting and Optimization in Electrical Power Systems: Advances in Models and Applications. Energies. 2023; 16(7):3024. https://doi.org/10.3390/en16073024
Chicago/Turabian StyleDudek, Grzegorz, Paweł Piotrowski, and Dariusz Baczyński. 2023. "Intelligent Forecasting and Optimization in Electrical Power Systems: Advances in Models and Applications" Energies 16, no. 7: 3024. https://doi.org/10.3390/en16073024
APA StyleDudek, G., Piotrowski, P., & Baczyński, D. (2023). Intelligent Forecasting and Optimization in Electrical Power Systems: Advances in Models and Applications. Energies, 16(7), 3024. https://doi.org/10.3390/en16073024