Abstract
Accurate and reliable forecasting on annual electricity consumption will be valuable for social projectors and power grid operators. With the acceleration of electricity market reformation and the development of smart grid and the energy Internet, the modern electric power system is becoming increasingly complex in terms of structure and function. Therefore, electricity consumption forecasting has become a more difficult and challenging task. In this paper, a new hybrid electricity consumption forecasting method, namely grey model (1,1) (GM (1,1)), optimized by moth-flame optimization (MFO) algorithm with rolling mechanism (Rolling-MFO-GM (1,1)), was put forward. The parameters a and b of GM (1,1) were optimized by employing moth-flame optimization algorithm (MFO), which is the latest natured-inspired meta-heuristic algorithm proposed in 2015. Furthermore, the rolling mechanism was also introduced to improve the precision of prediction. The Inner Mongolia case discussion shows the superiority of proposed Rolling-MFO-GM (1,1) for annual electricity consumption prediction when compared with least square regression (LSR), GM (1,1), FOA (fruit fly optimization)-GM (1,1), MFO-GM (1,1), Rolling-LSR, Rolling-GM (1,1) and Rolling-FOA-GM (1,1). The grey forecasting model optimized by MFO with rolling mechanism can improve the forecasting performance of annual electricity consumption significantly.
Highlights
Electricity consumption is one of the significant indices of electrical power supply system planning and operation management
Some parameters are set as fixed values in the process of prediction so that the forecasting results would be difficult for reflecting the real electricity demand accurately, which causes the traditional models to show a poor performance for electricity consumption forecasting in terms of accuracy and reliability [9]
This paper set p = 9 and q = 1, which implies nine data points are used as the input sequence feeding into Rolling-moth-flame optimization (MFO)-GM (1,1), and one data point needs to be share of total electricity demand of Inner Mongolia, so accurately forecasting electricity consumption will contribute to the sustainable development of regional industry and electric power grid
Summary
Electricity consumption is one of the significant indices of electrical power supply system planning and operation management. Liang [20] raised a prediction model based on the improved fruit fly algorithm to optimize the parameters of SVM so that the accuracy of forecasting can be enhanced. A new intelligent optimization algorithm named MFO is utilized to optimize the parameters of GM (1,1) model, and it is verified that it can increase the precision of annual electricity demand prediction. Most literature only study the combination of optimization algorithm or rolling mechanism with grey model for annual electricity consumption forecasting. In order to mathematically model the behavior of converging towards the light, a logarithmic spiral is defined for the MFO algorithm to simulate the spiral flying path of moths with respect to a flame: Mi “ SpMi , Fj q “ Diebtcosp2πtq Fj (18).
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