Optimal Decomposition and Reconstruction of Discrete Wavelet Transformation for Short-Term Load Forecasting

Happy Aprillia, Hong-Tzer Yang and Chao-Ming Huang
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Happy Aprillia: Department of Electrical Engineering, National Cheng Kung University, East Dist., Tainan City 701, Taiwan
Hong-Tzer Yang: Department of Electrical Engineering, National Cheng Kung University, East Dist., Tainan City 701, Taiwan
Chao-Ming Huang: Department of Electrical Engineering, Kun-Shan University, Yongkang Dist., Tainan City 710, Taiwan

Energies, 2019, vol. 12, issue 24, 1-23

Abstract: To achieve high accuracy in prediction, a load forecasting algorithm must model various consumer behaviors in response to weather conditions or special events. Different triggers will have various effects on different customers and lead to difficulties in constructing an adequate prediction model due to non-stationary and uncertain characteristics in load variations. This paper proposes an open-ended model of short-term load forecasting (STLF) which has general prediction ability to capture the non-linear relationship between the load demand and the exogenous inputs. The prediction method uses the whale optimization algorithm, discrete wavelet transform, and multiple linear regression model (WOA-DWT-MLR model) to predict both system load and aggregated load of power consumers. WOA is used to optimize the best combination of detail and approximation signals from DWT to construct an optimal MLR model. The proposed model is validated with both the system-side data set and the end-user data set for Independent System Operator-New England (ISO-NE) and smart meter load data, respectively, based on Mean Absolute Percentage Error (MAPE) criterion. The results demonstrate that the proposed method achieves lower prediction error than existing methods and can have consistent prediction of non-stationary load conditions that exist in both test systems. The proposed method is, thus, beneficial to use in the energy management system.

Keywords: short-term load forecasting; day-ahead load prediction; multiple linear regression; discrete wavelet transforms; Daubechies wavelet; whale optimization algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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