Forecasting China’s Coal Power Installed Capacity: A Comparison of MGM, ARIMA, GM-ARIMA, and NMGM Models

Abstract

Construction of new coal-fired power plants in China has posed a huge challenge to energy sustainability. Forecasting the installed capacity more accurately can serve to develop better energy sustainability strategy. A comparison between linear and non-linear forecasting models can more comprehensively describe the characteristics of the prediction data and provide multi-angle analysis of the prediction results. In this paper, we develop four time-series forecasting techniques-metabolism grey model (MGM), autoregressive integrated moving average (ARIMA), grey model (GM)-ARIAM, and nonlinear metabolism grey model (NMGM)-for better forecasting of coal-fired power installed capacity. The average relative errors between the simulation and actual data of the MGM, GM-ARIMA, ARIMA, and NMGM model are 3.37%, 2.13%, 3.71% and 2.36% respectively, which indicate those four models can produce highly accurate results. The forecasting results show the average annual growth rate of China's coal-fired power installed capacity in the next ten years (2017-2016) will be 5.26% a year, which is slower than the average annual growth rate (8.20% a year) for 2007-2016. However, the average annual new added installed capacity for 2017-2026 will be 74 gigawatts, which is higher than the average annual added installed capacity (56 gigawatts) for 2007-2016.

Forecasting Energy-Related Carbon Dioxide Emissions in Thailand’s Construction Sector by Enriching the LS-ARIMAXi-ECM Model

“…From the review of the literature, this kind of study has been shown to be relevant to other past research in terms of model applications in CO 2 emission forecasting. Zhao, Zhao, and Guo [9] used GM (1,1) optimized by MFO with a rolling mechanism to forecast the electricity consumption of Inner Mongolia; Chang, Sun, and Gu [13] forecast energy CO 2 emissions using a quantum harmony search algorithm-based DMSFE combination model; Zeng, Xu, Wang, Chen, and Li [14] forecasted the allocative efficiency of carbon emission allowance financial assets in china at the provincial level in 2020; Liang, Niu, Wang, and Chen [15] did an assessment analysis and forecast for the secure early warning of energy consumption carbon emissions in Hebei Province, China; Li, Yang, and Li [30] forecast China's coal power installed capacity using a comparison of MGM, ARIMA, GM-ARIMA, and NMGM Models.…”

“…The study reported the discovery of a new way of predicting structural behavior, based on data processing, laying a basis for a bridge health monitoring system based on sensor data using sensing technology. Li, Yang and Li [30] developed four time-series forecasting techniques, including a metabolism grey model (GM), ARIMA, grey model (GM)-ARIAMA and non-linear metabolism grey model (NMGM), to forecast China's coal power installed capacity for Sustainability 2018, 10, 3593 6 of 19 the next 10 years (2017-2026). The prediction results present an average annual growth rate of 5.26% for the predicted period.…”

Predicting Coal Consumption in South Africa Based on Linear (Metabolic Grey Model), Nonlinear (Non-Linear Grey Model), and Combined (Metabolic Grey Model-Autoregressive Integrated Moving Average Model) Models

“…In this study, the first 5-year data sequence is used as an example. In the next forecasting cycle, we added one new data point and removed one old data point to ensure that the forecasting data size of every step is five [40].…”

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].…”

“…Without comparison, it is hard to find the models with higher accuracy. In addition, compared with a single model, forecasting with multiple models gives greater superiority in the precision of results [29,[45][46][47]. In this paper, we used the MGM, ARIMA model, MGM-ARIMA model, and BP neural network model to forecast India's energy demand.…”