Fox News anchor Bret Baier examines the U.S. power supply on'Special Report.' POWER UP: A new White House study warns that electricity prices may spike due to artificial intelligence demand if the United States does not boost energy output. TURNED OFF: Google is making a push to ensure its AI, Gemini, is tightly integrated with Android systems by granting it access to core apps like WhatsApp, Messages, and Phone. The rollout of this change started on July 7, 2025, and it may override older privacy configurations unless you know how to disable Gemini on Android. Here's what you need to know. OPINION: DIGITAL DOMINANCE: The global race to harness the power of artificial intelligence (AI) has begun.

In this paper, we introduce a nonparametric end-to-end method for probabilistic forecasting of distributed renewable generation outputs while including missing data imputation. Firstly, we employ a nonparametric probabilistic forecast model utilizing the long short-term memory (LSTM) network to model the probability distributions of distributed renewable generations' outputs. Secondly, we design an end-to-end training process that includes missing data imputation through iterative imputation and iterative loss-based training procedures. This two-step modeling approach effectively combines the strengths of the nonparametric method with the end-to-end approach. Consequently, our approach demonstrates exceptional capabilities in probabilistic forecasting for the outputs of distributed renewable generations while effectively handling missing values. Simulation results confirm the superior performance of our approach compared to existing alternatives.

Neural networks play an important role in machine learning. Inspired by how human brains work, these computational systems learn a relationship between complex and often non-linear inputs and outputs. A basic neural network consists of an input layer, a hidden layer and an output layer. Each layer is made of a certain number of nodes or neurons. Neural networks with many layers are referred to as deep learning systems.

Researchers from Sandia National Laboratories recently designed machine learning algorithms intended to improve the energy output of nuclear fusion reactors. The research team utilized AI algorithms to simulate the interactions between plasma and materials within the walls of a nuclear fusion reactor. Unlike nuclear fission, which involves splitting atoms apart, the energy created by fusion reactions releases energy through the creation of plasma. Hydrogen atoms are superheated to create a plasma cloud and this cloud releases energy as the particles within it smash into one another and fuse together. This process is chaotic, and if scientists can better control the fusion process, it could lead to substantial increases in the amount of usable energy created by nuclear fusion reactors.

More and more cities are looking to go green. And renewable energy is, if current trends hold, the future of the energy industry. But as renewable energy technologies like wind farms are implemented at larger scales than ever, local officials are running into their limitations. The energy production of wind farms is hard to predict, and this makes energy grid design difficult. Experts hope that machine learning can be applied to renewable energy to solve this problem. If it works, this new tech may make energy officials more enthusiastic about implementing renewables.

But as renewable energy technologies like wind farms are implemented at larger scales than ever, local officials are running into their limitations. The energy production of wind farms is hard to predict, and this makes energy grid design difficult. Experts hope that machine learning can be applied to renewable energy to solve this problem. If it works, this new tech may make energy officials more enthusiastic about implementing renewables. One downside of renewables is how hard it can be to predict the energy they produce. Wind speeds can vary widely from hour to hour and from day to day.

Thanks to a new partnership with the Alan Turing Institute, National Grid Electricity System Operator (ESO) announced it has developed new AI prediction models that have improved solar forecasting by one-third. Knowing how much power will be flowing into the grid on any given day is becoming increasingly crucial as the proportion of intermittent renewable power serving the grid goes up. Rob Rome, commercial operations manager at the ESO, said the new forecast models means the power system can become much more efficient at managing supply and demand. Improved solar forecasts will help us run the system more efficiently, ultimately meaning lower bills for consumers. National Grid worked with researchers and doctoral students at the Institute to develop the improved forecasting models.

Google announced today that it has made energy produced by wind farms more viable using the artificial intelligence software of its London-based subsidiary DeepMind. By using DeepMind's machine learning algorithms to predict the wind output from the farms Google uses for its green energy initiatives, the company says it can now schedule set deliveries of energy output, which are more valuable to the grid than standard, non-time-based deliveries. According to Google, this software has improved the "value" of the wind energy these farms are providing by 20 percent over a baseline where no such time-based predictions are being performed. We don't know exactly what that value is in monetary terms or in terms of energy output. We also don't know where exactly this is being deployed, although Google works with wind farms largely in the Midwest, where some of its US data centers are located.

Google announced today that it has made energy produced by wind farms more viable using the artificial intelligence software of its London-based subsidiary DeepMind. By using DeepMind's machine learning algorithms to predict the wind output from the farms Google uses for its green energy initiatives, the company says it can now schedule set deliveries of energy output, which are more valuable to the grid than standard, non-time-based deliveries. According to Google, this software has improved the "value" of the wind energy these farms are providing by 20 percent over a baseline where no such time-based predictions are being performed. We don't know exactly what that value is in monetary terms or in terms of energy output. We also don't know where exactly this is being deployed, although Google works with wind farms largely in the Midwest, where some of its US data centers are located.

Solar energy products and services developers and vendors continue to leverage the latest in distributed information and communications technology (ICT) in bids to drive further declines in the cost and boost the productivity of solar energy systems. Development and use of an expanding range of machine-to-machine (M2M) communications and "Internet of Things" devices – wireless network sensors and "smart," network-connected inverters, meters and other devices – along with high-reliability wireless/mobile networking and cloud software- and infrastructure-as-a-service (SaaS and IaaS) platforms are enabling vendors and their customers to collect, analyze and act upon continuous streams of digital data and approach ideal maximum electrical power and energy production while coincidentally minimizing installation, operations and maintenance costs. With more than nine gigawatts (GWs) worth of its products installed on five continents, in 1991 Fremont, California-based NEXTracker published a groundbreaking white paper describing a new algorithm that improved solar tracking and resulted in gains of around three percent in solar PV facility production. While that methodology continues to be applied in nearly all solar energy tracking systems today, NEXTracker is pushing the technological envelope out further. On July 11, the company introduced its latest innovation to the market, a "first-of-its-kind intelligent, self-adjusting tracker control system for solar power plants."