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A novel hybrid short-term electricity forecasting technique for residential loads using Empirical Mode Decomposition and Extreme Learning Machines by S.M. Sulaiman; P. Aruna Jeyanthy; D. Devaraj; K.V. Shihabudheen is a Computer Science article available to read on EtoBox.

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In recent years, the residential load forecasting problem has been gaining renewed interest due to the advent of Smart Meters and Data Analytics. A novel hybrid method based on Empirical Mode Decomposition (EMD) in tandem with Extreme Learning Machine (ELM) is proposed in this paper to improve the forecast accuracy of residential load signals derived from Smart Meter data. Three state-of-the-art machine learning methods, namely Artificial Neural Network (ANN), Support Vector Regression (SVR), and ELM, are selected for performance comparison. It is observed from the results that the proposed method is found effective in picking the peaks that are usually present in residential loads and hence improved the forecast accuracy. Further, the results show that the performance of EMD based models is improved when the test data is characterized by more peaks. Smart\*, a public dataset containing residential load measurements, is used for evaluation.

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Author

S.M. Sulaiman; P. Aruna Jeyanthy; D. Devaraj; K.V. Shihabudheen

Publisher

Elsevier BV

Published

2022

Language

EN

Field

Computer Science (Physical Sciences)