College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
Environ Sci Pollut Res Int. 2023 Sep;30(42):95410-95424. doi: 10.1007/s11356-023-29044-0. Epub 2023 Aug 7.
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.
准确预测用电量对于减少电力浪费和维持电力系统稳定至关重要。针对用电量的非线性和季节性波动问题，本文提出了一种基于季节性和趋势分解的灰色模型预测方法，引入时间序列分解方法。STL 分解算法将波动的用电量数据分解为趋势、季节性和剩余三个分量。然后，使用合理的方法对具有不同数据特征的分量进行预测。采用新模型对中国浙江省 2014 年第四季度至 2022 年第三季度的季度用电量进行分析。实验结果表明，该模型的预测精度优于现有模型，其平均绝对百分比误差（MAPE）和均方根误差（RMSPE）值分别为 1.77%和 2.37%。该模型能够有效识别数据序列中的季节性波动，为预测季节性波动数据和优化季节性供电方案提供了新方法。
