 Short term electric load forecasting by wavelet transform and grey model improved by PSO (particle swarm optimization) algorithmSaadat Bahrami, Rahmat-Allah Hooshmand and Moein Parastegari Energy, 2014, vol. 72, issue C, 434-442 Abstract: STLF (short term electric load forecasting) plays an important role in the operation of power systems. In this paper, a new model based on combination of the WT (wavelet transform) and GM (grey model) is presented for STLF and is improved by PSO (particle swarm optimization) algorithm. In the proposed model, the weather data including mean temperature, mean relative humidity, mean wind speed, and previous days load data are considered as the model inputs. Also, the wavelet transform is used to eliminate the high frequency components of the previous days load data and improve the accuracy of prediction. To improve the accuracy of STLF, the generation coefficient of GM is enhanced using PSO algorithm. To verify its efficiency, the proposed method is used for New York's and Iran's load forecasting. Simulation results confirm favourable performance of the proposed method in comparison with the previous methods studied. Keywords: Short term load forecasting; Grey model; Particle swarm optimization; Wavelet transform (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:72:y:2014:i:c:p:434-442 DOI: 10.1016/j.energy.2014.05.065 Access Statistics for this article Energy is currently edited by Henrik Lund and Mark J. Kaiser More articles in Energy from Elsevier |
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