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2 results on '"Qiu, Xueheng"'

1. Ensemble incremental learning Random Vector Functional Link network for short-term electric load forecasting.

Author

Qiu, Xueheng, Suganthan, Ponnuthurai Nagaratnam, and Amaratunga, Gehan A.J.

Subjects
*LOAD forecasting (Electric power systems), *ELECTRICAL load, *MACHINE learning, *RANDOM variables, *DISCRETE wavelet transforms, *HILBERT-Huang transform, *TIME series analysis, *RANDOM forest algorithms, *MATHEMATICAL models
Abstract

Short-term electric load forecasting plays an important role in the management of modern power systems. Improving the accuracy and efficiency of electric load forecasting can help power utilities design reasonable operational planning which will lead to the improvement of economic and social benefits of the systems. A hybrid incremental learning approach composed of Discrete Wavelet Transform (DWT), Empirical Mode Decomposition (EMD) and Random Vector Functional Link network (RVFL) is presented in this work. RVFL network is a universal approximator with good efficiency because of the randomly generated weights between input and hidden layers and the close form solution for parameter computation. By introducing incremental learning, along with ensemble approach via DWT and EMD into RVFL network, the forecasting performance can be significantly improved with respect to both efficiency and accuracy. The electric load datasets from Australian Energy Market Operator (AEMO) were used to evaluate the effectiveness of the proposed incremental DWT-EMD based RVFL network. Moreover, the attractiveness of the proposed method can be demonstrated by the comparison with eight benchmark forecasting methods. [ABSTRACT FROM AUTHOR]

Published
2018
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2. Oblique random forest ensemble via Least Square Estimation for time series forecasting.

Author

Qiu, Xueheng, Zhang, Le, Nagaratnam Suganthan, Ponnuthurai, and Amaratunga, Gehan A.J.

Subjects
*TIME series analysis, *LEAST squares, *RANDOM forest algorithms, *MACHINE learning, *DECISION trees, *ARTIFICIAL neural networks
Abstract

Recent studies in Machine Learning indicates that the classifiers most likely to be the bests are the random forests. As an ensemble classifier, random forest combines multiple decision trees to significant decrease the overall variances. Conventional random forest employs orthogonal decision tree which selects one “optimal” feature to split the data instances within a non-leaf node according to impurity criteria such as Gini impurity, information gain and so on. However, orthogonal decision tree may fail to capture the geometrical structure of the data samples. Motivated by this, we make the first attempt to study the oblique random forest in the context of time series forecasting. In each node of the decision tree, instead of the single “optimal” feature based orthogonal classification algorithms used by standard random forest, a least square classifier is employed to perform partition. The proposed method is advantageous with respect to both efficiency and accuracy. We empirically evaluate the proposed method on eight generic time series datasets and five electricity load demand time series datasets from the Australian Energy Market Operator and compare with several other benchmark methods. [ABSTRACT FROM AUTHOR]

Published
2017
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