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The model of chaotic sequences based on adaptive particle swarm optimization arithmetic combined with seasonal term. (English) Zbl 1243.90258

Summary: Within a competitive electric power market, the price of electricity is one of the core elements, which is crucial to all the market participants. Accurately forecasting of electricity price becomes highly desirable. This paper propose a forecasting model of electricity price using chaotic sequences for forecasting of short term electricity price in the Australian power market. One modified model is applies seasonal adjustment and another modified model is employed seasonal adjustment and adaptive particle swarm optimization (APSO) that determines the parameters for the chaotic system. The experimental results show that the proposed methods performs noticeably better than the traditional chaotic algorithm.

MSC:

90C59 Approximation methods and heuristics in mathematical programming
62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH)
91B76 Environmental economics (natural resource models, harvesting, pollution, etc.)
37N40 Dynamical systems in optimization and economics
68T05 Learning and adaptive systems in artificial intelligence
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