Intelligent Statistical Learning Framework for Optimal Distributed Energy Planning in Industrial Parks

Abstract

Industrial parks are undergoing rapid transformation toward decentralized and renewable-based energy infrastructures to improve sustainability, operational efficiency, and economic competitiveness. However, distributed energy planning remains a challenging optimization problem because renewable energy resources exhibit significant spatial and temporal uncertainty, making conventional deterministic planning approaches inadequate. Statistical learning techniques provide an effective mechanism for extracting hidden patterns from historical energy data, while probabilistic scenario generation improves planning robustness under uncertain operating conditions. This paper proposes an intelligent statistical learning framework that integrates probabilistic scenario generation, statistical forecasting, Bayesian learning, and optimization strategies for distributed energy planning in industrial parks. The framework combines renewable scenario generation with statistical learning to model uncertain wind energy behavior, generate representative operating scenarios, and support optimal planning decisions. Bayesian generative learning, deep scenario generation, dependent stochastic optimization, and frequency-based forecasting collectively improve prediction accuracy while reducing planning risk. The proposed methodology establishes a systematic workflow consisting of data acquisition, statistical preprocessing, probabilistic scenario generation, uncertainty modeling, optimization, and decision support. Analytical findings indicate that integrating statistical learning with stochastic optimization significantly enhances planning flexibility, energy utilization efficiency, renewable penetration, and operational reliability. The framework also supports adaptive planning under changing environmental conditions and provides practical guidance for intelligent industrial energy management. The proposed research contributes an integrated planning architecture that combines modern statistical learning with distributed energy optimization for future smart industrial parks.