A hybrid short-term load forecasting with a new input selection framework

M. Ghofrani, M. Ghayekhloo, A. Arabali and A. Ghayekhloo

Energy, 2015, vol. 81, issue C, 777-786

Abstract: This paper proposes a hybrid STLF (short-term load forecasting) framework with a new input selection method. BNN (Bayesian neural network) is used to forecast the load. A combination of the correlation analysis and ℓ2-norm selects the appropriate inputs to the individual BNNs. The correlation analysis calculates the correlation coefficients between the training inputs and output. The Euclidean distance with respect to a desired correlation coefficient is then calculated using the ℓ2-norm. The input sub-series with the minimum Euclidean norm is selected as the most correlated input and decomposed by a wavelet transform to provide the detailed load characteristics for BNN training. The sub-series whose Euclidean norms are closest to the minimum norm are further selected as the inputs for the individual BNNs. A weighted sum of the BNN outputs is used to forecast the load for a particular day. New England load data are used to evaluate the performance of the proposed input selection method. A comparison of the proposed STLF with the existing state-of-the-art forecasting techniques shows a significant improvement in the forecast accuracy.

Keywords: Bayesian neural network; Correlation analysis; Input selection; Short-term load forecasting; Wavelet decomposition (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (39)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:81:y:2015:i:c:p:777-786

DOI: 10.1016/j.energy.2015.01.028

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