A New Approach of the Machine Learning Framework Integrating Policy Design to Predict Renewable Electricity Penetration in Resource-Constrained Settings

The transition toward sustainable energy systems in developing economies faces multifaceted constraints including limited financial resources, institutional capacity gaps, and policy implementation challenges. Conventional forecasting approaches for renewable electricity penetration predominantly emphasize technical and economic variables while neglecting the catalytic role of policy frameworks as dynamic predictors. This research introduces a novel machine learning architecture that explicitly integrates quantitative policy indicators derived from the World Bank's Regulatory Indicators for Sustainable Energy (RISE), IRENA's Renewables Readiness Assessments (RRA), and IEA Country Energy Profiles into a Long Short-Term Memory (LSTM) network for predicting non-hydro renewable electricity generation across 71 developing nations. Unlike static regression models, this research framework treats policy variables as time-evolving features that modulate the temporal dynamics of renewable adoption trajectories. The model architecture incorporates attention mechanisms to weight policy dimensions according to their contextual relevance across heterogeneous national settings. Preliminary validation demonstrates that policy-integrated LSTM forecasting reduces prediction error by 23.7% compared to purely techno-economic baselines, particularly in nations exhibiting rapid policy evolution. This work establishes policy instrumentation as a first-order predictor in renewable energy forecasting and provides a transferable methodology for evidence-based energy policy design in resource-constrained environments.