A novel hybrid short-term electricity forecasting technique for residential loads using Empirical Mode Decomposition and Extreme Learning Machines

• Published in: Computers & electrical engineering Vol. 98; p. 107663
• Main Authors: Sulaiman, S.M., Jeyanthy, P. Aruna, Devaraj, D., Shihabudheen, K.V.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Ltd 01.03.2022; Elsevier BV
• Subjects: Artificial neural networks; Electrical loads; Electricity meters; Empirical analysis; Empirical Mode Decomposition; Extreme Learning Machines; Forecasting; Forecasting techniques; Learning theory; Machine learning; Residential energy; Short-term load forecasting; Smart Grid; Smart Meter Data; Support vector machines; Empirical Mode Decomposition; Extreme Learning Machines; Short-term load forecasting; Smart Meter Data; Smart Grid
• ISSN: 0045-7906; 1879-0755
• DOI: 10.1016/j.compeleceng.2021.107663

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. [Display omitted] •A hybrid method is proposed for accurate prediction of Residential Loads.•Proposed method is validated using a real-time residential smart meter data.•Three hybrid and three non-hybrid models are evaluated.•Proposed method improves the prediction accuracy when more peaks are present.•Hybrid method based on Extreme Learning Machines offer better performance.