Comparison of Holt-Winters and ARIMA Models for Hydropower Forecasting in Guangxi

Abstract

Nowadays, electrical power supply has had in the main role in developing countries, so the hydropower attaches great importance to all countries, in order to solve the electricity problems of industrial and agricultural production and people's daily life. Hydropower is a clean energy source that is more seasonal than other forms of power generation. Forecasting the hydropower generation is a challenging problem, and time series forecasting has been considered as an effective forecasting method. However, under the principle of minimum forecast variance, the longer the forecast time, the greater the variance of the forecast value, so time series data are only suitable for short-term forecasting. This paper used the Holt-Winters additive model and the ARIMA model for solving the shortage of time series forecasting to forecast Guangxi's hydropower generation for the next five months from January 2010 to February 2020, and the forecast results is compared with the real data from the National Bureau of Statistics of China. The result show that ARIMA model is much less accurate than Holt-Winters' additive model in when analysis data with seasonal fluctuations. This paper, can provide some information for energy managers and policymakers.