Multifractional Brownian motion and quantum-behaved particle swarm optimization for short term power load forecasting: An integrated approach

Wanqing Song, Carlo Cattani and Chi-Hung Chi

Energy, 2020, vol. 194, issue C

Abstract: Power load fluctuation is generally agreed to be a non-stationary stochastic process. The Fractional Brownian Motion (FBM) model is proposed to forecast a non-stationary time series with high accuracy. Computation of the Hurst exponent (H) for the power load data series using the Rescaled Range Analysis (R/S) in this study. This method is used to verify the Long-Range Dependent (LRD) characteristics of non-stationary power load data. For the real power load, however, H exponent takes on the self-similarity characteristics in a certain finite range of intervals, the global self-similarity is very rare to exist. The H exponent of the self-similarity usually has more than one value. We generalize multifractional H(t) to replace constant H. To improve the forecasting accuracy, the H(t) is optimized by the Quantum-Behaved Particle Swarm Optimization (QPSO). Once the optimal H(t) is obtained, then the optimal and parameters in the multi-Fractional Brownian Motion (mFBM) model can be deduced to forecast next power load data series with a higher accuracy.

Keywords: Fractional brownian motion; Long-range dependence; Particle swarm optimization; Power load forecasting; Quantum-behaved particle swarm optimization (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:194:y:2020:i:c:s0360544219325423

DOI: 10.1016/j.energy.2019.116847

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