White noise

Quantum mechanics

Hilbert–Huang transform

Engineering

Biology

Extreme learning machine

Statistics

Time series

Wind power forecasting

Anomaly detection

Telecommunications

Power (physics)

Computer science

Regression analysis

Outlier

Paleontology

Wind speed

Physics

Regression

Mathematics

Electric power system

Robust regression

Artificial intelligence

Series (stratigraphy)

Wind power

Meteorology

Artificial neural network

Mean squared error

Machine learning

Electrical engineering

Applied Soft Computing

Wind energy is a core sustainable source of electric power, and accurate wind-speed forecasting is pivotal to enhancing the power stability, efficiency, and utilization. The existing forecasting methods are still limited by the influence of outliers and the modelling difficulties caused by complex features in wind speed series. This paper proposes a new wind speed forecasting system based on a designed adaptive robust extreme learning machine (ARELM) model and signal decomposition algorithms. Firstly, the ARELM is designed to sufficiently lessen the violation of normality assumptions and contamination by outliers. ARELM takes an adaptive scaled Huber’s loss as its objective function, which can limit the influence of outliers and adaptively determine an appropriate mixture distribution of normal distribution and Laplace distribution at the same time. Secondly, the empirical mode decomposition (EMD) method and its improved methods (EEMD, CEEMD and CEEMDAN) are introduced to our wind-speed forecasting system, where the low-frequent sub-series are modelled by basic ELM and the high-frequent ones are modelled by ARELM. This can decompose the modelling complex wind speed series into modelling several simple sub-series and reduce the difficulty of modelling. Experimental results show that our combined forecasting system, ELM-ARELM, obtains up to 78% improvement in forecasting performance comparing with the methods using general Huber’s loss and other comparison methods, which show the superiority of the adaptive scaled Huber’s loss. The error indexes (MAE and RMSE) by the proposed system, which are (0.25, 0.34), (0.32, 0.45) and (0.38, 0.53) for 5 min head, 15 min ahead and 25 min ahead experiments respectively, demonstrate the effectiveness of decomposition methods on improving accuracy of wind speed prediction.

Robustified extreme learning machine regression with applications in outlier-blended wind-speed forecasting