INTEGRATING ADVANCED COMPUTING TECHNIQUES IN CLEAN-ENERGY TRANSITION FORECASTING AND POLICY SIMULATION

Abstract

This study addresses the problem that advanced computing techniques are widely demonstrated in energy-modelling research but their actual integration into organizational clean-energy forecasting and policy simulation remains poorly understood. The purpose is to quantify how advanced computing capability, organizational readiness and data quality jointly shape forecasting quality and policy simulation effectiveness in real clean-energy transition contexts. Using a quantitative, cross-sectional, case-based design, the study surveys 230 professionals from utilities, system operators, regulators and energy consultancies that rely on cloud-based analytics and enterprise decision platforms. Advanced computing capability, forecasting quality, policy simulation effectiveness, organizational readiness and data quality are measured with five-point Likert scales; analysis combines descriptive statistics, reliability testing, Pearson correlations, multiple regression and moderation analysis. Results show moderate-to-high advanced computing adoption (ACC mean 3.78) and strong positive links with forecasting quality (β = 0.41, r = 0.63) and policy simulation effectiveness (β = 0.29, r = 0.59), with models explaining 54 percent and 58 percent of variance respectively. Organizational readiness strongly predicts advanced computing capability (β = 0.52, R² = 0.33), while data quality not only improves outcomes directly but also amplifies the benefit of advanced computing for forecasting quality (interaction β = 0.14). Headline findings indicate that advanced computing delivers the greatest value when embedded in organizations that combine leadership support, skills and high-quality data pipelines. The study implies that energy planners and policymakers should treat investments in AI enabled forecasting, big data platforms and digital twins as part of a broader capability and data governance agenda for credible clean-energy transition pathways.