Rational and self-adaptive evolutionary extreme learning machine for electricity price forecast

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• 作者：Chixin Xiao ; Zhaoyang Dong ; Yan Xu ; Ke Meng ; Xun Zhou ; Xin Zhang
• 关键词：Approximation model ; Differential evolution ; Extreme learning machine ; Electricity price prediction
• 刊名：Memetic Computing
• 出版年：2016
• 出版时间：September 2016
• 年：2016
• 卷：8
• 期：3
• 页码：223-233
• 全文大小：1,222 KB
• 刊物类别：Engineering
• 刊物主题：Applied Mathematics and Computational Methods of Engineering
  Artificial Intelligence and Robotics
  Automation and Robotics
  Complexity
  Bioinformatics
  Applications of Mathematics
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1865-9292
• 卷排序：8

文摘

Electricity price forecast is of great importance to electricity market participants. Moreover, various prediction approaches based on extreme learning machine (ELM) have been identified as effective on normal decision space. Especially, evolutionary extreme learning machine (E-ELM) may obtain better solution quality. However, in high dimensional space, E-ELM is time-consuming because it is difficult to converge into optimal region when just relied on stochastic searching approaches. In addition, due to the complex functional relationship is often complicated, the objective function of E-ELM seems hard to be mined directly for obtaining useful mathematical information to guide the optimum exploring. This paper proposes a new differential evolution (DE)-like algorithm to enhance E-ELM for more accurate and reliable prediction of electricity price. The approximation model for producing DE-like trail vector is the key mechanism, which may use simpler mathematical mapping to replace the original yet complicated functional relationship within a small region. Thus, the evolutionary procedure frequently guided by rational searching directions may make the E-ELM more robust and faster than supported only by those stochastic methods. Several benchmarks are applied to test the performances of the proposed algorithm and the experimental results have shown that the new method can improve the performance of E-ELM.