Optimal decomposition and reconstruction of discrete wavelet transformation for short-term load forecasting

Happy Aprillia, Hong Tzer Yang, Chao Ming Huang

研究成果: Article同行評審

8 引文 斯高帕斯(Scopus)


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.

出版狀態Published - 2019 十二月 7

All Science Journal Classification (ASJC) codes

  • 可再生能源、永續發展與環境
  • 能源工程與電力技術
  • 能源(雜項)
  • 控制和優化
  • 電氣與電子工程


深入研究「Optimal decomposition and reconstruction of discrete wavelet transformation for short-term load forecasting」主題。共同形成了獨特的指紋。