Titlebook: Application of Machine Learning and Deep Learning Methods to Power System Problems; Morteza Nazari-Heris,Somayeh Asadi,Milad Sadat-Moh Boo

致命

https://doi.org/10.1007/978-3-030-77696-1Artificial neural networks (ANNs); Expert systems; Fuzzy systems; Evolutionary-based methods; Fault/Even

mitral-valve

978-3-030-77698-5The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

反省

Book 2021ced by power system operators. Written by leading experts, the book will be an ideal resource for researchers and engineers working in the electrical power engineering and power system planning communities, as well as students in advanced graduate-level courses. 

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驳船

1612-1287 Covers theoretical background and experimental analysisThis book evaluates the role of innovative machine learning and deep learning methods in dealing with power system issues, concentrating on recent developments and advances that improve planning, operation, and control of power systems. Cutting-

临时抱佛脚

intrude

Sarah Meram,Theodore Falcon,James H. Paxtona huge historical data. The proposed feature selection is based on Kalman-Kohonen model for load forecasting and adaptive neuro-fuzzy inference system model for price forecasting. The obtained results for a distribution system confirmed the model’s effective performance.

羞辱

malign

The Adaptive Neuro-Fuzzy Inference System Model for Short-Term Load, Price, and Topology Forecastina huge historical data. The proposed feature selection is based on Kalman-Kohonen model for load forecasting and adaptive neuro-fuzzy inference system model for price forecasting. The obtained results for a distribution system confirmed the model’s effective performance.

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