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A hybrid model based on modified multi-objective cuckoo search algorithm for short-term load forecasting

Author:
Wu, Zhuochun, Zhao, Xiaochen, Ma, Yuqing, Zhao, Xinyan
Source:
Applied energy 2019 v.237 pp. 896-909
ISSN:
0306-2619
Subject:
algorithms, electric power, neural networks, Australia
Abstract:
To ensure the safe operation of electrical power systems, short-term load forecasting (STLF) plays a significant role. With the development of artificial neural network (ANN), many forecasting models based on ANN are proposed to enhance the forecasting accuracy. However, forecasting stability is also an important aspect when considering a forecasting model. Both forecasting accuracy and stability are affected heavily by the random initial values of weights and thresholds of ANN. Thus, in this paper, a new hybrid model based on the modified generalized regression neural network (GRNN) is proposed for short-term load forecasting (STLF). Meanwhile, a non-dominated sorting-based multi-objective cuckoo search algorithm (NSMOCS) is proposed to realize accurate and stable forecasting simultaneously. To utilize the similarities and reduce interference existing in the original data, some data pre-processing techniques are also incorporated. With half-hourly load data from five states in Australia, experimental results clearly show that the proposed hybrid model could obtain more accurate and stable forecasting results, compared with the comparison models.
Agid:
6284773