Short-term electricity-load forecasting using a tsk-based extreme learning machine with knowledge representation

HIGHLIGHTS

• who: Chan-Uk Yeom and Keun-Chang Kwak from the Department of Control and Instrumentation Engineering, Chosun University, Gwangju, Korea have published the Article: Short-Term Electricity-Load Forecasting Using a TSK-Based Extreme Learning Machine with Knowledge Representation, in the Journal: (JOURNAL)
• what: The authors propose a new ELM predictor with knowledge representation, e_g, if-then rules, for short-term electricity-load forecasting. In this experiment, the authors compared the proposed method with conventional methods. Figure 15. in Linguistic contexts obtained the initial random-partition method of ELM used in this experiment demonstrated the . . .
  

   

  

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