10.57647/ijeee.2025.1603.14

Intelligent Optimization of Management Decision Support Systems Using Data Mining Techniques

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  • Shizhou Feng1,2,
  • Jing Du*3,
  1. Chongqing College of Mobile Communication, Chongqing, 401520 China
  2. Chongqing Key Laboratory of Public big Data Security Technology, Chongqing, 401420 China
  3. Chongqing College of International Business and Economics, Chongqing, 401520 China

Received: 2025-07-07

Accepted: 2025-10-21

Published in Issue 2025-12-31

Copyright (c) 2025 Shizhou Feng, Jing Du (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Feng, S., & Du, J. (2025). Intelligent Optimization of Management Decision Support Systems Using Data Mining Techniques. International Journal of Energy and Environmental Engineering, 16(04). https://doi.org/10.57647/ijeee.2025.1603.14
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Abstract

As patterns of energy usage become more complicated, smart, data-driven strategies are needed for effective forecasting and management. In order to optimise decision support systems in the energy industry, this study suggests a hybrid data mining framework that combines Extreme Gradient Boosting (XGBoost) with K-Means clustering. The model is intended to increase the precision and interpretability of energy usage forecasts while detecting discrete consumption behaviour clusters by utilising the publicly accessible UCI Individual Household Electric Power Consumption dataset. The suggested XGBoost + K-Means model performs noticeably better than conventional models like Linear Regression, Decision Tree, and Random Forest, according to a comparative analysis. It achieves a high R2 score of 0.91, a mean absolute error (MAE) of 39.7 Wh, and a root mean square error (RMSE) of 49.6 Wh. Furthermore, evaluation criteria including F1-score, precision, and recall attest to the model's resilience and appropriateness for real-time applications. These results demonstrate how hybrid machine learning techniques can convert energy data into useful insights, which will ultimately help develop more intelligent and sustainable energy management plans.

Keywords

  • Energy Consumption,
  • Data Mining,
  • Decision support System,
  • Management,
  • XGBoost+ K-Means Clustering
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