Using GM (1,1) Optimized by MFO with Rolling Mechanism to Forecast the Electricity Consumption of Inner Mongolia

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.

Comparison of Forecasting India’s Energy Demand Using an MGM, ARIMA Model, MGM-ARIMA Model, and BP Neural Network Model

“…The nonhomogeneous discrete grey model can better capture nonhomogeneous effects on the data [27]. In addition, GM (grey model) (1,1) by month-flame optimization with a rolling mechanism made the timeliness of the data series more clear [28,29].…”

Research and Application of a Novel Hybrid Model Based on Data Selection and Artificial Intelligence Algorithm for Short Term Load Forecasting

“…In contrast, the moth will eventually converge to the light due to maintaining a similar angle when the light source is very close, as indicated in Figure 1B [50]. A general description is presented in detail [50,51], and the main steps are described as follows: …”

Prediction of Natural Gas Consumption in Different Regions of China Using a Hybrid MVO‐NNGBM Model

“…Lin et al [38] and Zhao et al [39] optimized the grey action quantity and the development coefficient of the GM (1,1) using the artificial fish swarm algorithm and the moth-flame optimization (MFO) algorithm, respectively. Zhou et al [40] optimized the parameters of the NGBM model using particle swarm optimization (PSO).…”