Predicting seasonal patterns of energy production: A grey seasonal trend least squares support vector machine. (1st March 2023)

• Record Type: Journal Article
• Authors: Zhou, Weijie; Jiang, Huiming; Cheng, Yuke; Pei, Lingling; Ding, Song
• Is Part Of: Expert systems with applications. Volume 213:Part A(2023)
• Publication Date: 2023-03-01
• Subjects: Energy forecasting -- Seasonal fluctuations -- Least squares support vector machine -- Grey prediction model -- China; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.118874
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A grey seasonal trend support vector machine for energy forecasting is proposed. The model can efficiently identify the seasonal fluctuations in energy data. The model is powerful to predict China's energy production. The predictive performance of the new model is much superior to the benchmarks. Abstract: This paper proposed a hybrid ensemble forecasting technique that incorporates the merits of the accumulation generation operation ( AGO ), least-square support vector regression ( LSSVR ), dummy variable, and time trend item to forecast the seasonal time series characterized by nonlinearity and uncertainty. This proposed framework, a grey seasonal trend least squares support vector machine ( GSTLSSVR ), can make it easy to add arbitrary seasonality to any feature, which improves model realism significantly. Furthermore, the grid search method and the last block validation are developed to identify the optimal hyperparameter, making it possible for the developed method to capture seasonal and nonlinear patterns of the original data. For illustration and verification, the quarterly petroleum coke production and monthly crude oil production in China are employed to construct diverse competing models, including the seasonal fluctuation grey model ( SGM(1, 1) ), seasonal autoregressive integrated moving average ( SARIMA ), Holt-Winters (HW), backpropagation neural network ( BPNN ), a grey seasonal least squares support vector machine ( GSLSSVR ), long short term memory (LSTM) and LSSVR . The forecasting values of quarterly petroleum coke production are 668.74, 682.68, 679.99, 704.35 for 2019 and 717.59, 734.99, 727.16, 741.53 for 2020, with the lowest MAPEP value (3.7566%). Moreover, the monthly predicted crude oil production for 2019 are 3113.9, 1645, 1562.2, 1625.4, 1599.3, 1619.1, 1627, 1576.9, 1629.6, 1583.6, and 1615.6, while those counterparts for 2020 are 3093.2, 1625, 1547.5, 1621.5, 1610.6, 1644.9, 1662.9, 1615.4, 1667.4, 1615, 1667.4, 1615, and 1640.2, with the lowest MAPEP value (1.0034%). Generally, the empirical results show that the proposed model obtains superior overall performance to other benchmarks in both case studies. Therefore, this proposed model exhibits promising prospects for identifying seasonal patterns in energy production sequences.