AI-driven predictive models for sustainability

TY - JOUR

T1 - AI-driven predictive models for sustainability

AU - Olawumi, Mattew A.

AU - Oladapo, Bankole I.

N1 - Publisher Copyright: © 2024 The Authors

PY - 2025/1

Y1 - 2025/1

N2 - This research presents an AI-driven, explainable energy management model that aligns with Net Zero sustainability objectives by optimizing energy consumption, enhancing predictive accuracy, and ensuring transparency. The model integrates machine learning algorithms, like Gradient Boosting Machines (GBM) and Random Forests, and utilizes techniques like SHAP and LIME for interpretability. Data was split 70/30 for training and validation, with 10-times validation to avoid overfitting, achieving a Mean Absolute Error (MAE) of 1.26–1.53 and Root Mean Squared Error (RMSE) of 1.97–2.06. The model's predictive accuracy reached an R2 of 0.92, with precision and recall scores of 85–90% and 80–88%, respectively, demonstrating significant improvements over traditional methods. Sensitivity analysis revealed high influence from temperature and historical consumption data, requiring careful monitoring. This model performed robustly across diverse scenarios, reducing CO₂ emissions by 30% and cutting costs by 18%, highlighting its adaptability in real-world applications. Conclusions affirm that the explainable AI model advances sustainable energy management by providing reliable, actionable insights, aligning with Net Zero goals, and supporting informed decision-making through enhanced transparency and accuracy.

AB - This research presents an AI-driven, explainable energy management model that aligns with Net Zero sustainability objectives by optimizing energy consumption, enhancing predictive accuracy, and ensuring transparency. The model integrates machine learning algorithms, like Gradient Boosting Machines (GBM) and Random Forests, and utilizes techniques like SHAP and LIME for interpretability. Data was split 70/30 for training and validation, with 10-times validation to avoid overfitting, achieving a Mean Absolute Error (MAE) of 1.26–1.53 and Root Mean Squared Error (RMSE) of 1.97–2.06. The model's predictive accuracy reached an R2 of 0.92, with precision and recall scores of 85–90% and 80–88%, respectively, demonstrating significant improvements over traditional methods. Sensitivity analysis revealed high influence from temperature and historical consumption data, requiring careful monitoring. This model performed robustly across diverse scenarios, reducing CO₂ emissions by 30% and cutting costs by 18%, highlighting its adaptability in real-world applications. Conclusions affirm that the explainable AI model advances sustainable energy management by providing reliable, actionable insights, aligning with Net Zero goals, and supporting informed decision-making through enhanced transparency and accuracy.

KW - Artificial intelligence

KW - Energy optimization

KW - Explainable AI (XAI)

KW - Machine learning

KW - Predictive analytics

KW - Sustainable computing

UR - https://www.scopus.com/pages/publications/85210066224

U2 - 10.1016/j.jenvman.2024.123472

DO - 10.1016/j.jenvman.2024.123472

M3 - Article

C2 - 39612794

AN - SCOPUS:85210066224

SN - 0301-4797

VL - 373

JO - Journal of Environmental Management

JF - Journal of Environmental Management

M1 - 123472

ER -