Quarterly electricity consumption prediction based on time series decomposition method and gray model.

Accurately predicting electricity consumption is crucial for reducing power waste and maintaining power system stability. To address the non-linear and seasonal fluctuations of electricity consumption, this paper proposes a seasonal prediction method based on Seasonal and Trend decomposition using Loess (STL) algorithm and gray model by introducing time series decomposition method. The STL decomposition algorithm decomposes fluctuating electricity data into three components: trend, seasonal, and remainder. Then reasonable methods are used to predict components with different data characteristics. The novel model is employed to analyze the quarterly electricity consumption in Zhejiang province of China from 2014Q4 to 2022Q3. The experimental results show that the prediction accuracy of this model is superior to the state-of-the art models; the MAPE and RMSPE values are 1.77% and 2.37%, respectively. Our model that can effectively identify seasonal fluctuations in data sequences provides a new method for predicting seasonal fluctuation data and optimizing seasonal electricity supply schemes.