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Electricity is the guarantee of economic development and daily life. Thus, accurate monthly electricity consumption forecasting can provide reliable guidance for power construction planning. In this paper, a hybrid model in combination of least squares support vector machine(LSSVM) model with fruit fly optimization algorithm(FOA) and the seasonal index adjustment is constructed to predict monthly electricity consumption. The monthly electricity consumption demonstrates a nonlinear characteristic and seasonal tendency. The LSSVM has a good fit for nonlinear data, so it has been widely applied to handling nonlinear time series prediction. However, there is no unified selection method for key parameters and no unified method to deal with the effect of seasonal tendency. Therefore, the FOA was hybridized with the LSSVM and the seasonal index adjustment to solve this problem. In order to evaluate the forecasting performance of hybrid model, two samples of monthly electricity consumption of China and the United States were employed, besides several different models were applied to forecast the two empirical time series. The results of the two samples all show that, for seasonal data, the adjusted model with seasonal indexes has better forecasting performance. The forecasting performance is better than the models without seasonal indexes. The fruit fly optimized LSSVM model outperforms other alternative models. In other words, the proposed hybrid model is a feasible method for the electricity consumption forecasting.
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Basic Information:
DOI：10.19884/j.1672-5220.2019.01.010
China Classification Code:TM715
Citation Information:
[1]WANG Zilong,XIA Chenxia,College of Economics and Management,Nanjing University of Aeronautics and Astronautics.Seasonal Least Squares Support Vector Machine with Fruit Fly Optimization Algorithm in Electricity Consumption Forecasting[J].Journal of Donghua University (English Edition),2019,36(01):67-76.DOI:10.19884/j.1672-5220.2019.01.010.
Fund Information:
National Social Science Foundation of China(No.18AGL028);; Social Science Foundation of the Higher Education Institutions Jiangsu Province,China(No.2018SJZDI070);; Social Science Foundation of the Jiangsu Province,China(Nos.16ZZB004,17ZTB005)