Scientific Reports Article in Press
Predicting energy prices and renewable energy adoption through an optimized tree-based learning framework with explainable artificial intelligence
Author: Tao Tang
Published online: 30 January 2026
This research offers a comprehensive analysis of global energy consumption, focusing on predicting two key metrics: the Energy Price Index and the Renewable Energy Share. The study employs advanced Machine Learning (ML) regression techniques, all further optimized using metaheuristic algorithms. In addition, a primary objective of this study is to determine which variables most significantly affect model performance and predictive accuracy. Through SHAP (Shapley Additive exPlanations) and CAM (Cosine Amplitude Method) sensitivity analyses, the study systematically interprets model outputs and quantifies the influence of each input feature. Findings demonstrate that, according to the SHAP-based model interpretation, the prediction of Renewable Energy Share is most strongly influenced by fossil fuel dependency and carbon emissions. These results underscore the pivotal role of consumption intensity and environmental indicators in shaping both global energy price trajectories and renewable energy adoption rates. Integrating optimization algorithms with advanced models improved both predictive accuracy and model robustness. The resulting analytical framework provides a technically rigorous and interpretable approach to global energy forecasting. Such a framework is valuable for informing energy policy, supporting sustainability strategies, and enabling stakeholders to monitor environmental impacts and optimize energy system performance. By leveraging data-driven insights, this study advances practical tools and methodologies for strategic planning in the context of a sustainable global energy future.
Key Executive Impact Metrics
Our analysis highlights significant advancements in predictive accuracy and interpretability for critical energy metrics.
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Energy Price Index (HGOA R²)
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Energy Price Index (HGOA RMSE)
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Renewable Energy Share (HGOA R²)
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Renewable Energy Share (HGOA RMSE)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Enterprise Process Flow
Data Gathering & Preprocessing
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Model Selection (ETR, HGBR, DTR)
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Metaheuristic Optimization (COA, OOA)
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5-Fold Cross-Validation
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Hyperparameter Tuning
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SHAP & CAM Sensitivity Analysis
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Predictive Forecasting & Interpretation
Optimized Tree-Based Learning Framework
This study employs advanced Machine Learning (ML) regression techniques, Extra Trees Regression (ETR), Histogram Gradient Boosting Regression (HGBR), and Decision Tree Regression (DTR), all further optimized using metaheuristic algorithms like Coyote Optimization Algorithm (COA) and Osprey Optimization Algorithm (OOA). This synergy enhances predictive accuracy and robustness, particularly for high-dimensional, non-linear, or noisy energy data. The framework is designed to capture complex relationships in global energy consumption, price, and renewable adoption trends.
0.9762
HGOA R² for Energy Price Index (Testing Phase)
The HGOA (Histogram Gradient Boosting Regression optimized with Osprey Optimization Algorithm) model consistently achieved the highest R² (0.9762) and lowest RMSE (0.0341) during the testing phase for the Energy Price Index, demonstrating superior generalization and reliable predictive capacity across all evaluation phases.
0.9882
HGOA R² for Renewable Energy Share (Training Phase)
For Renewable Energy Share prediction, HGOA again emerged as the leading model, recording a high R² of 0.9882 and lowest RMSE of 4.4820 in the training phase. The consistent performance across phases highlights the model’s robust stability, crucial for effective renewable energy planning.
Key Drivers of Renewable Energy Adoption
Through SHAP and CAM sensitivity analyses, the study reveals that the prediction of Renewable Energy Share is most strongly influenced by fossil fuel dependency and carbon emissions. This underscores the pivotal role of environmental indicators in shaping renewable energy adoption rates. Policies targeting reductions in fossil fuel reliance and carbon output are identified as critical levers for accelerating the transition to sustainable energy.
Dominant Factors in Energy Price Forecasting
The analysis confirms that total energy consumption is the predominant factor affecting the Energy Price Index. This variable encapsulates demand across industrial, residential, and commercial sectors, making it a central driver of price volatility. Understanding and managing consumption intensity is crucial for stabilizing energy markets and informing pricing strategies.
Actionable Policy Interventions
The findings provide actionable insights for energy policy and market planning. By identifying key drivers such as total energy consumption, fossil fuel dependency, and carbon emissions, policymakers can target interventions effectively. Reducing fossil fuel dependency or incentivizing efficient energy use can directly impact both energy costs and the share of renewables, fostering global sustainability and economic stability. The detected interaction effects between variables emphasize the need for holistic policy design to avoid unintended consequences.
Value-Oriented Forecasting for Decision Making
The predictive framework, while accuracy-oriented, is designed to serve as high-quality input for value-oriented forecasting. This means the insights derived can directly enhance downstream decision outcomes in areas like energy portfolio allocation, capacity expansion planning, and risk management. This study advances practical tools and methodologies for strategic planning in the context of a sustainable global energy future.
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Your AI Implementation Roadmap
A clear path to integrating advanced energy forecasting and explainable AI into your operations.
Phase 01: Data & Discovery
Comprehensive assessment of your existing energy data, infrastructure, and strategic objectives. This includes data quality assessment, integration planning, and identification of key performance indicators.
Phase 02: Model Customization & Optimization
Tailoring and optimizing tree-based learning models (ETR, HGBR, DTR) with metaheuristic algorithms (COA, OOA) to your unique data, ensuring maximum predictive accuracy and robustness for energy price and renewable adoption forecasts.
Phase 03: Explainable AI Integration (XAI)
Implementing SHAP and CAM analyses to provide transparent, interpretable insights into model predictions, helping you understand the "why" behind energy trends and feature importance.
Phase 04: Validation & Deployment
Rigorous cross-validation and testing to confirm model stability and generalization. Seamless deployment of the optimized framework into your existing systems, ensuring operational readiness.
Phase 05: Strategic Integration & Monitoring
Embedding AI insights into your decision-making processes for energy policy, resource allocation, and sustainability strategies. Continuous monitoring, recalibration, and support to ensure long-term value and adaptation to evolving market dynamics.
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