Research on an Energy Economic Demand Time Series Prediction Model Based on the Integration of XGBoost and Deep Learning

Guona Chen1*
1 School of Business Administration/School of Marxism, China University of Petroleum-Beijing at Karamay, Karamay, Xinjiang, China
* Corresponding author: Guona Chen. Email: cgn@st.cupk.edu.cn
Journal of Discovery Core 2026, Vol. 1, No. 1, pp. 1-18
DOI: 10.67541/jdc2601
Received: 26 May 2026; Revised: 23 June 2026; Accepted: 30 June 2026; Published: 9 July 2026
Abstract

To address the significant nonlinear characteristics of energy economic demand time series, the complex coupling of multiple influencing factors, and the insufficient generalization ability of traditional prediction models, this paper proposes a time series prediction model for energy economic demand based on the integration of XGBoost and deep learning. First, the energy demand data, macroeconomic indicators, and meteorological variables are uniformly preprocessed and feature-enhanced to construct a multi-dimensional input system that includes lag features, sliding window features, and periodic features. Then, an improved XGBoost is used to mine nonlinear relationships within structured features, while a deep learning network extracts temporal dynamic features and long-term dependencies. A dynamic weight fusion mechanism is designed to achieve collaborative prediction of the two models. Finally, model performance is verified through comparative, ablation, robustness, and interpretability analyses. The experimental results show that the RMSE, MAE, and MAPE on the test set reach 28.74, 21.52, and 2.83%, respectively, and the coefficient of determination (R²) reaches 0.983. Compared with the Transformer model, the MAPE is reduced by 32.8%, and compared with the traditional ARIMA model, it is reduced by 68.2%. Furthermore, under conditions of 20% outlier perturbation and 20% missing data, the model's performance retention rates still reach 88.2% and 90.8%, respectively, demonstrating good stability and generalization ability. The research results show that the proposed integrated model can effectively improve the accuracy of energy economic demand prediction, providing reliable technical support for energy planning, load management, and smart energy decision-making.

Keywords
Energy economic demand forecasting Time series prediction XGBoost Deep learning Ensemble model
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© 2026 The Author(s). Published by Digital Intelligence Press Limited. This is an open access article under the CC BY 4.0 license.

How to cite: Chen, G. (2026). Research on an Energy Economic Demand Time Series Prediction Model Based on the Integration of XGBoost and Deep Learning. Journal of Discovery Core, 1(1), 1-18. https://doi.org/10.67541/jdc2601