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A self-similar local neuro-fuzzy model for short-term demand forecasting

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Abstract

This paper proposes a self-similar local neuro-fuzzy (SSLNF) model with mutual information-based input selection algorithm for the short-term electricity demand forecasting. The proposed selfsimilar model is composed of a number of local models, each being a local linear neuro-fuzzy (LLNF) model, and their associated validity functions and can be interpreted itself as an LLNF model. The proposed model is trained by a nested local liner model tree (NLOLIMOT) learning algorithm which partitions the input space into axis-orthogonal sub-domains and then fits an LLNF model and its associated validity function on each sub-domain. Furthermore, the proposed approach allows different input spaces for rule premises (validity functions) and consequents (local models). This appealing property is employed to assign the candidate input variables (i.e., previous load and temperature) which influence short-term electricity demand in linear and nonlinear ways to local models and validity functions, respectively. Numerical results from short-term load forecasting in the New England in 2002 demonstrated the accuracy of the SSLNF model for the STLF applications.

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Authors and Affiliations

  1. Executive Business Centre, Bournemouth University, Bournemouth, 89 Holdenhurst Road, BH8 8EB, UK

    Hossein Hassani

  2. Department of Mechanical Engineering, Pardis Branch, Islamic Azad University, Pardis new city, Tehran, Iran

    Majid Abdollahzadeh & Arash Miranian

  3. Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Hossein Iranmanesh

  4. Institute of International Energy Studies (IIES), Tehran, Iran

    Hossein Iranmanesh

Authors
  1. Hossein Hassani
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  2. Majid Abdollahzadeh
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  3. Hossein Iranmanesh
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  4. Arash Miranian
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Correspondence to Hossein Hassani.

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Hassani, H., Abdollahzadeh, M., Iranmanesh, H. et al. A self-similar local neuro-fuzzy model for short-term demand forecasting. J Syst Sci Complex 27, 3–20 (2014). https://doi.org/10.1007/s11424-014-3299-y

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  • DOI: https://doi.org/10.1007/s11424-014-3299-y

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