From Paradox to Progress A Net Positive AI Energy Framework 2025

Emerging deploy for impact use case examples TABLE 3 Source: AI Energy Impact public use case database. AIoT-enabled industrial decarbonization BOX 3 Challenge Ordos, a heavily industrialized region in China long reliant on coal, aimed to transition towards net- zero operations. Challenges included managing renewable intermittency, maintaining energy stability, and achieving carbon visibility and compliance. Solution –Deployed an artificial intelligence of things (AIoT) platform as the industrial park’s digital brain, integrating wind, solar, storage and industrial loads –Enabled real-time multi-energy forecasting, dispatch and carbon accounting across wind, solar, hydrogen, storage and EVs –Embedded AI for product-level carbon traceability and life cycle emissions verification –Established a collaborative ecosystem connecting government, utilities and industry partners to synchronize energy and carbon data Impact Near-term impacts realized or anticipated (less than one year) –100,000 megawatt-hours (MWh) energy savings –5% peak demand reduction –80% forecasting accuracy Further impacts realized or anticipated (more than one year) –200,000MWh energy savings –10% peak demand reduction –1,000,000 tons of carbon dioxide equivalent (CO2e) avoided Reviewing the levers in action Smart grid optimization: Real-time dispatch, forecasting, and grid balancing Industrial process control: AI-driven efficiency optimization and carbon reduction Building energy management: Smart systems for monitoring and facility optimization Renewable forecasting: Enhanced reliability of intermittent wind and solar generation Transport and logistics: Coordinated fleet charging with renewable supply and park energy loadsSmart grid optimizationLarge European energy utility (AI-enabled grid optimization): Applied AI across 30 million customers, reducing losses, improving reliability and cutting network energy waste Industrial process controlSiemens (Chengdu smart factory): Tailored AI for process control cut electricity use by 24% and waste by 48% Building energy managementJapan (smart city pilots): Coordinated AI for transport, lighting and building control reduced urban energy use by 35% Renewable forecastingChile (solar plant optimization): AI integration improved renewable output 15%, reducing curtailment and enhancing grid reliability Hitachi Energy (intelligent price forecasting): Energy price forecasting was made 20% more accurate with AI tools Transport and logisticsGlobal retailer (fleet route optimization): AI-enabled logistics optimized fleet routing, eliminating 30 million unnecessary miles driven, lowering fuel use and supply-chain emissions From Paradox to Progress: A Net-Positive AI Energy Framework 17