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Intelligent Forecasting and Optimization in Electrical Power Systems
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This reprint explores the latest developments and advancements in the application of artificial intelligence (AI) and machine learning (ML) for forecasting and optimization in the field of power engineering. In recent years, AI and ML methods have been gaining significant traction and are becoming two of the most important fields in computing. These methods have proven to be effective in solving forecasting and optimization problems in power engineering. The topics covered in the chapters fall into four categories: electricity demand forecasting, wind power forecasting, photovoltaic power forecasting, and optimization.
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Keywords
- artificial intelligence methods
- auto-regressive integrated moving average (ARIMA)
- autoencoders
- autoregressive forecasting methods
- behind-the-meter (BTM)
- big data
- bootstrap aggregating
- calendar variation
- chillers
- classical forecasting methods
- CLONALG
- cooling towers
- Data mining
- deep learning
- deep neural network
- deep neural networks
- distributed storage and generation
- distribution of electric power
- E-Mobility
- Economics, finance, business & management
- electric energy production
- electric vehicles (EVs)
- electrical power demand
- electricity load forecasting
- elliptic envelope (EE)
- Energy Efficiency
- Energy industries & utilities
- ensemble methods
- evaluation criteria metrics
- evolutionary multi-objective optimization
- forecast
- forecasting error
- GA
- History of engineering & technology
- hybrid AC/DC microgrid
- Hybrid Methods
- Industry & industrial studies
- information and communication technology
- interval type-2 fuzzy logic system
- isolation forest (IF)
- load profile forecast
- Long Short-Term Memory (LSTM)
- LSTM
- Machine learning
- machine learning (ML)
- medium voltage
- medium-term load forecasting
- metaheuristic optimisation
- microgrids
- mid-term forecast
- modified hypermutation operator
- n/a
- neuromorphic computing
- one-class support vector machine (OCSVM)
- operation control
- optimisation
- optimization of configuration and operating states
- Optuna
- pattern representation of time series
- pattern-based forecasting
- photovolatic power
- photovoltaic (PV)
- power distribution reliability
- power generation
- power system demand
- power systems
- PSO
- PV system
- Q(U) characteristics
- random forest
- regression tree
- renewable energy
- short-term forecasting
- short-term load forecasting
- short-term wind power forecasting
- singular spectrum analysis
- Smart Grids
- spatio-temporal
- Spiking Neural network
- statistical analysis of errors
- strategic training
- Swarm intelligence
- Technology, engineering, agriculture
- Technology: general issues
- time series
- Time Series Analysis
- time series forecasting
- time series prediction
- time-series preprocessing
- transfer learning
- very-short-term forecasting
- voltage control
- voltage quality
- Wind energy
- wind farm
- Wind power
- wind power forecasting
- wind power prediction
- wind turbine
Links
DOI: 10.3390/books978-3-0365-9081-3Editions