致命
发表于 2025-3-25 07:17:12
https://doi.org/10.1007/978-3-030-77696-1Artificial neural networks (ANNs); Expert systems; Fuzzy systems; Evolutionary-based methods; Fault/Even
mitral-valve
发表于 2025-3-25 09:06:20
978-3-030-77698-5The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
反省
发表于 2025-3-25 12:19:44
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.
归功于
发表于 2025-3-25 17:46:24
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驳船
发表于 2025-3-25 23:59:25
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
发表于 2025-3-26 03:28:09
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intrude
发表于 2025-3-26 06:07:32
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
发表于 2025-3-26 10:22:19
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malign
发表于 2025-3-26 13:21:21
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.
CHART
发表于 2025-3-26 19:53:36
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