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Intelligent Forecasting and Optimization in Electrical Power Systems

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 t...

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Bibliographic Details
Main Author: Piotrowski, Paweł
Other Authors: Dudek, Grzegorz, Baczyński, Dariusz
Format: eBook
Language:English
Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2023
Subjects:
Time Series Analysis
Electric Vehicles (evs)
Machine Learning
Smart Grids
Forecast
Short-term Load Forecasting
Deep Learning
Neuromorphic Computing
Operation Control
Swarm Intelligence
Wind Power Forecasting
Technology: General Issues / Bicssc
Elliptic Envelope (ee)
Machine Learning (ml)
Medium Voltage
Information And Communication Technology
Classical Forecasting Methods
Ga
Electricity Load Forecasting
Short-term Wind Power Forecasting
Long Short-term Memory (lstm)
Artificial Intelligence Methods
Optimisation
One-class Support Vector Machine (ocsvm)
Short-term Forecasting
Voltage Quality
Big Data
Wind Energy
Forecasting Error
Electrical Power Demand
Time Series Prediction
Pattern-based Forecasting
Isolation Forest (if)
Time-series Preprocessing
Wind Power
Singular Spectrum Analysis
Evolutionary Multi-objective Optimization
Pattern Representation Of Time Series
Power Systems
Distributed Storage And Generation
Time Series Forecasting
Microgrids
Mid-term Forecast
Evaluation Criteria Metrics
Cooling Towers
Power Distribution Reliability
Data Mining
Spatio-temporal
Statistical Analysis Of Errors
Energy Industries And Utilities / Bicssc
Wind Farm
Clonalg
Random Forest
Renewable Energy
Medium-term Load Forecasting
N/a
Regression Tree
Modified Hypermutation Operator
Pv System
Pso
Energy Efficiency
Time Series
Wind Power Prediction
Behind-the-meter (btm)
History Of Engineering And Technology / Bicssc
Deep Neural Network
Photovoltaic (pv)
Chillers
Load Profile Forecast
Deep Neural Networks
Lstm
Ensemble Methods
Distribution Of Electric Power
Autoregressive Forecasting Methods
Interval Type-2 Fuzzy Logic System
E-mobility
Auto-regressive Integrated Moving Average (arima)
Transfer Learning
Spiking Neural Network
Bootstrap Aggregating
Optimization Of Configuration And Operating States
Voltage Control
Electric Energy Production
Q(u) Characteristics
Hybrid Methods
Metaheuristic Optimisation
Power Generation
Autoencoders
Power System Demand
Strategic Training
Calendar Variation
Hybrid Ac/dc Microgrid
Very-short-term Forecasting
Wind Turbine
Optuna
Photovolatic Power
Online Access:
Collection: Directory of Open Access Books - Collection details see MPG.ReNa
Description
Summary: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.
Item Description:Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
Physical Description:1 electronic resource (468 p.)
ISBN:9783036590806
9783036590813
books978-3-0365-9081-3

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