A soft computing approach to projecting locational marginal price

Nwulu N. I., Fahrioglu M.

NEURAL COMPUTING & APPLICATIONS, vol.22, no.6, pp.1115-1124, 2013 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 22 Issue: 6
  • Publication Date: 2013
  • Doi Number: 10.1007/s00521-012-0875-8
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.1115-1124
  • Keywords: Locational marginal price, Artificial neural networks, Support vector machines, Back propagation learning algorithm, Radial basis function, NEURAL-NETWORK, ELECTRICITY PRICE
  • Middle East Technical University Affiliated: Yes


The increased deregulation of electricity markets in most nations of the world in recent years has made it imperative that electricity utilities design accurate and efficient mechanisms for determining locational marginal price (LMP) in power systems. This paper presents a comparison of two soft computing-based schemes: Artificial neural networks and support vector machines for the projection of LMP. Our system has useful power system parameters as inputs and the LMP as output. Experimental results obtained suggest that although both methods give highly accurate results, support vector machines slightly outperform artificial neural networks and do so with manageable computational time costs.