Short-term load forecasting model for metro power supply system based on echo state neural network
| Autor: | Jia Xu, Zhang Zhisheng, Yu Litao, Wang Li, Han Aoyang |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Engineering
Dynamic network analysis Artificial neural network business.industry Load forecasting 0206 medical engineering Real-time computing Echo (computing) 02 engineering and technology 020601 biomedical engineering Power (physics) Term (time) 0202 electrical engineering electronic engineering information engineering Slow convergence 020201 artificial intelligence & image processing State (computer science) business Simulation |
| Zdroj: | 2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS). |
| DOI: | 10.1109/icsess.2016.7883212 |
| Popis: | The paper presents a short-term load forecasting model for metro power supply system based on echo state neural network. Echo state neural network composed of input layer, reserve pool, the output layer. Reserve pool as a dynamic network is connected by a large number of random sparse of neurons. Reserve pool is used to overcome the slow convergence speed and avoid neural network into the local minimum. Using the actual historical data of the metro power supply system to simulate, the simulation results show that the short-term load forecasting model for metro power supply system based on echo state neural network has good prediction accuracy. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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