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An intelligent ACO-SA approach for short term electricity load prediction

Ghanbari, A ; Sharif University of Technology | 2010

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  1. Type of Document: Article
  2. DOI: 10.1007/978-3-642-14932-0_77
  3. Publisher: 2010
  4. Abstract:
  5. Intelligent solutions, based on artificial intelligence (AI) technologies, to solve complicated practical problems in various sectors are becoming more and more widespread nowadays. On the other hand, electrical load prediction is one of the important concerns of power systems so development of intelligent prediction tools for performing accurate predictions is essential. This study presents an intelligent hybrid approach called ACO-SA by hybridization of Ant Colony Optimization (ACO) and Simulated Annealing (SA). The hybrid approach consists of two general stages. At the first stage time series inputs will be fed into ACO and it performs a global search to find a globally optimum solution. At the second stage, ACO's outcome will be fed into SA as initial solution and then SA starts local search around the ACO's global optimum and performs the tuning process on the initial solution. The superiority and applicability of the ACO-SA approach is shown for Iranian monthly load prediction problem and outcomes of the hybrid method are compared with Single ACO, Single SA and ANN technique (which is a common technique in the field of load prediction). Results show that ACO-SA approach outperforms rest of the methods regarding to prediction accuracy, so it can be considered as a promising alternative for load prediction studies
  6. Keywords:
  7. Ant-colony optimization ; Artificial Neural Networks ; Intelligent prediction ; Short-Term Electricity Load Prediction ; Time Series Modeling ; Algorithms ; Annealing ; Computation theory ; Forecasting ; Hand tools ; Intelligent computing ; Neural networks ; Problem solving ; Time series ; Simulated annealing
  8. Source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 18 August 2010 through 21 August 2010 ; Volume 6216 LNAI , 2010 , Pages 623-633 ; 03029743 (ISSN) ; 9783642149313 (ISBN)
  9. URL: http://link.springer.com/chapter/10.1007%2F978-3-642-14932-0_77