The Green Algorithm: Optimizing Renewable Energy Integration and ESG Compliance through AI-Powered Market Analytics

Abstract

Rapid growth in renewable energy capacity and rising investor demand for measurable ESG outcomes create a critical need for tools that align energy market operations with Environmental, Social, Governance objectives. This paper introduces the Green Algorithm, an AI-powered market analytics framework that integrates renewable generation forecasting, market-clearing optimization, anomaly detection, and explainable ESG scoring to support decision-making across utilities, traders, and financial institutions. The Green Algorithm aims to optimize renewable energy integration into electricity markets while improving ESG compliance and operational resilience. The goals are to (1) increase usable renewable supply, (2) reduce market inefficiencies and fraudulent transactions, and (3) produce transparent, audit-ready explanations of how market and investment decisions affect ESG outcomes. We combine multiple machine learning techniques tailored to specific subproblems: sequence models including LSTM and Transformer variants for high-resolution renewable generation forecasting; gradient-boosted decision trees for price impact and portfolio-level prediction; graph neural networks to model participant interactions and systemic risk in energy markets; and autoencoder-based and ensemble anomaly detectors for transaction-level fraud and manipulation detection. Explainability is provided by SHAP value decompositions and counterfactual generators to attribute ESG score changes to specific operational decisions. Models were trained and tested on an integrated dataset linking smart-meter generation traces, market-clearing prices, trading logs, and third-party ESG ratings. Performance was evaluated with standard predictive metrics and domain-specific operational and regulatory metrics: RMSE, MAE, MAPE for forecasts; precision, recall, F1-score, and AUC for classification tasks; and business-oriented measures including reduced curtailment, marginal cost savings, renewable utilization ratio, and changes in portfolio-level ESG scores. The Green Algorithm delivered consistent improvements across forecasting, market efficiency, fraud detection, and ESG alignment. Renewable generation forecast error decreased by 18% relative to established baselines, enabling a 12% reduction in curtailment and a 6% decrease in marginal balancing costs. Market anomaly detectors achieved a precision of 0.92 and a recall of 0.88 on validated incident sets, reducing settlement disputes and suspected fraud volume. When integrated into portfolio decision rules, the system increased aggregate ESG compliance scores by 9% while preserving or improving expected financial returns through optimized bidding and hedging strategies. Explainability outputs improved regulator and investor confidence, shown by reduced model explanation dispute rates and faster audit turnaround. The Green Algorithm demonstrates that thoughtfully combined AI methods can materially improve renewable energy integration and advance ESG compliance in energy-financial markets.