By Jeffrey A. Newman Esq. JeffNewmanLaw.com
Energy consumption in the U.S. is projected to reach 800 Terewatt hours (TWh) annually by 2030 for quantum computing and artificial intelligence. That is 3% of the total U.S. demand now. Major project are already being engaged to use AI and machine learning to prioritize speed to power solutions via rapid deployment of nuclear, gas peaker plants and distributed renewables near data centers to minimize bottlenecks. In other words to reduce the distance the energy must travel. By the way, this will make our energy grid more efficient but also it wil be easier to protect from any attacks. AI will also balance intermitten renewables including solar and wind power with storage and demand reponse systems to reduce reliance on fossil fuel backups. Advanced AI can actual reduc energy use by 40TWh annually when compared to CPU-based systems. This has been established by Nvidia. Here are three books which reference the above summary:
Artificial Intelligence for Renewable Energy Systems (Wiley, 2025)8
Advances of Artificial Intelligence in a Green Energy Environment (Elsevier, 2022)10
6 Books on AI for Energy (AI Startups, 2025)
AI programs is already evolving the optimization of renewable energy resources including the following:
1. Renewable Energy Forecasting
AI analyzes weather forecasts, historical production data, and real-time environmental conditions to predict the output of variable sources like wind and solar. This enables energy providers to better match supply with demand, reducing the risk of shortages or overproduction.
2. Grid Management and Stability
AI algorithms optimize the flow of electricity across smart grids, integrating diverse sources (solar, wind, hydro) and maintaining grid stability despite the intermittent nature of renewables. Real-time data analysis allows for dynamic adjustments, preventing overloads and blackouts.
3. Energy Storage Optimization
AI predicts when to charge and discharge batteries, ensuring stored energy is used efficiently. This helps balance supply during periods of low renewable generation and high demand, while also extending battery lifespan.
4. Predictive Maintenance
Machine learning models detect early signs of equipment wear or failure in wind turbines, solar panels, and other assets. This proactive approach reduces downtime, increases asset lifespan, and maximizes energy output.
5. Enhanced Energy Production Efficiency
AI can optimize the orientation and operation of solar panels, track sunlight, and adjust wind turbine settings in real time. For example, Google’s DeepMind improved wind farm energy output by 20% using AI-driven forecasting and control.
6. Demand Forecasting and Cost Reduction
AI predicts energy demand patterns, enabling utilities to plan generation and distribution more efficiently. This minimizes waste, reduces operational costs, and supports a more sustainable energy system.
7. Real-World Case Studies
- Google’s DeepMind for wind energy: Improved output forecasting and grid integration.
- IBM’s AI-powered solar forecasting: Enhanced solar generation predictions for better grid management.
- Tesla’s AI-based energy storage optimization: Improved battery usage efficiency.
8. Decentralized and Community Energy Management
AI facilitates collaborative energy sharing and microgrid optimization, making local renewable systems more resilient and adaptable.
Jeffrey Newman is a whistleblower lawyer representing doctors, nurses and therapists who have become whistleblowers reporting Medicare and Medicaid fraud in False Claims Act (Qui Tam) cases. He also represents whistleblowers in tariff fraud cases and military contract fraud cases. also a frequent writer on issues relating to events affecting the world economy. Jeff can be reached at Jeff@JeffNewmanLaw.com or at 617-823-3217
