Clean Energy Transition in USA: Big Data Analytics for Renewable Energy Forecasting and Carbon Reduction

Abstract

The United States is at a critical juncture in its effort to overhaul the national energy system to meet bold climate objectives. The Biden Administration has committed to reducing greenhouse gas (GHG) emissions by 50–52% from 2005 levels by 2030 and reaching net-zero emissions by 2050. Meeting these goals demands a swift rollout of renewable energy sources while keeping the electricity grid reliable, affordable, and equitable. A key driver in this transition is big data analytics, which plays a vital role in forecasting the performance of variable renewable sources and supporting carbon-conscious decision-making. This paper explores how advanced data-driven technologies—such as machine learning, IoT sensors, satellite imaging, and predictive modeling—can help accelerate the U.S. shift toward clean energy. By examining recent peer-reviewed studies (2018–2022), policy developments, and case studies, it introduces a structured big data framework for forecasting renewable energy output and modeling carbon intensity. Findings show that data analytics can increase forecasting accuracy by up to 40%, cut reserve costs by billions annually, and reduce over 100 million metric tons of CO₂ through smarter, carbon-aware decisions. Highlighted case studies—including CarbonCast, predictive wind turbine maintenance, and open-source datasets—demonstrate the real-world technical and economic value of analytics. Cost-benefit assessments reveal that improving forecast accuracy by just 1% could cut CO₂ emissions by roughly 20 million metric tons each year. However, regional readiness varies: while CAISO and PJM are well-equipped for scaling these tools, ERCOT and ISO-NE face more infrastructure-related hurdles. The paper concludes that big data analytics is a powerful and timely tool for decarbonization. When integrated into energy markets, grid operations, and policy design, analytics can fast-track emission reductions while advancing equity, as seen in programs like Justice40. Future research should prioritize probabilistic forecasting, marginal carbon modeling, and inclusive data governance.