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Neural Processing Letters

, Volume 48, Issue 1, pp 329–351 | Cite as

Small-Scale Building Load Forecast based on Hybrid Forecast Engine

  • Mohsen Mohammadi
  • Faraz Talebpour
  • Esmaeil Safaee
  • Noradin Ghadimi
  • Oveis Abedinia
Article

Abstract

Electricity load forecasting plays an important role for optimal power system operation. Accordingly, short term load forecast (STLF) is also becoming an important task by researchers to tackle the mentioned problem. As a consequence of the highly non-smooth and volatile trend of the load time series specially in building levels, its STLF is even a more complex procedure than that of a power system. For this purpose, in this paper we proposed a new prediction model based on a new feature selection algorithm and hybrid forecast engine of enhanced version of empirical mode decomposition named sliding window EMD bundled with an intelligent algorithm. The proposed forecast engine is combined with novel shark smell optimization to increase the prediction accuracy. All weights of this forecast engine have been optimized with an intelligent algorithm to find better prediction results. Effectiveness of the proposed model is carried out to real-world engineering test case in comparison with other prediction models.

Keywords

Small-scale building forecast Improved Elman neural network Feature selection SWEMD 

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Mohsen Mohammadi
    • 1
  • Faraz Talebpour
    • 2
  • Esmaeil Safaee
    • 3
  • Noradin Ghadimi
    • 4
  • Oveis Abedinia
    • 5
  1. 1.Department of Electrical EngineeringPayame Noor University (PNU)TehranIran
  2. 2.Department of Artificial IntelligenceUniversity of MalayaKuala LumpurMalaysia
  3. 3.Department of Mechanic EngineeringSharif University of TechnologyTehranIran
  4. 4.Young Researchers and Elite club, Ardabil BranchIslamic Azad UniversityArdabilIran
  5. 5.Budapest University of Technology and EconomicsBudapestHungary

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