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Recently, Andreas Schierenbeck, CEO of Hitachi Energy, the world's largest transformer manufacturer, stated in an interview with the Financial Times that as large technology companies see a surge in power demand when training artificial intelligence models, governments need to take measures to limit these fluctuations to ensure the stability of the power supply. Image source note: The image is generated by AI, and the image licensing service provider is Midjourney. Schierenbeck said that the power demand fluctuations in AI data centers are extremely severe,

Google's latest sustainability report reveals a startling fact: within just four years, the company's data center power consumption more than doubled, rising from 14.4 million megawatt-hours in 2020 to 30.8 million megawatt-hours in 2024. If the timeline is extended to ten years, compared to an estimated 4 million megawatt-hours in 2014, Google's data center power consumption has increased sevenfold. Growing electricity demand: data centers are major energy consumers, efficiency improvements face bottlenecks. Data shows that Google's power issues are almost entirely concentrated in data centers.

On June 30th, the world's first trillion-parameter large model for the power generation industry, Qinyuan, developed independently by the State Energy Group, was officially released. This innovative large model, with its unique full-stack independent and controllable characteristics, marks an important step forward for the power generation industry into a new era of intelligent decision-making. The Qinyuan large model aims to integrate the diverse scenarios, high complexity, and strong professionalism of the power generation industry, fully utilizing the State Energy Group's globally largest installed capacity and massive data resources. The launch of this model not only creates a new engine for multi-energy coordination and dynamic optimization, but also establishes an electricity

Recently, SoftBank and Intel have jointly developed a new AI-specific memory chip aimed at significantly reducing power consumption to provide more efficient support for Japan's AI infrastructure. According to Nikkei Asia, the goal of their cooperation is to design a new type of stacked DRAM chip. The wiring method of this chip will be different from the popular High Bandwidth Memory (HBM) on the market, expected to reduce power consumption by about 50%. Image source note: The image was generated by AI, and the image rights service provider is Midjourney.

According to a new analysis, the power consumption of artificial intelligence is expected to reach nearly half of the total electricity consumption of global data centers by the end of 2025, meaning AI energy consumption will soon surpass bitcoin mining. The analysis was conducted by Alex de Vries-Gao, a Ph.D. student at the Environmental Institute of Vrije Universiteit Amsterdam. He has previously tracked the power consumption and environmental impact of cryptocurrencies and published related data on his website Digiconomist. Currently, AI has already accounted for a significant portion of data center energy usage.

PJM, the largest electricity grid operator in the United States, recently announced a partnership with Google and its subsidiary Tapestry to leverage artificial intelligence (AI) to transform how it manages its power system. This collaboration is expected to accelerate the integration of new power sources and address challenges such as delays in approving renewable energy projects and risks of power shortages. PJM noted in a press release that the project will mark the first use of AI to manage grid interconnection requests. Tapestry

Accurate power generation forecasting and supply-demand management are crucial for the rapid development of the new energy industry. On March 26, Ant Group released its new energy power time series large model – EnergyTS. This model aims to help companies optimize their business strategies and improve operational efficiency through high-precision data analysis. EnergyTS demonstrates particularly strong performance in photovoltaic scenarios, surpassing Google's TimesFM-V2.0 and Amazon's Chronos-Lar in power generation prediction accuracy.

On March 26th, at the Spring Summit of the New Energy Digital Asset Community in Suzhou, Ant Group officially launched EnergyTS, a time-series model for energy and power. This marks a significant breakthrough for Chinese companies in the field of energy AI. Specifically designed for the new energy sector, EnergyTS demonstrated significantly higher accuracy in photovoltaic power generation prediction compared to leading international general-purpose time-series models such as Google's (TimesFM-V2.0) and Amazon's (Chronos-Large). In T+1 day prediction, the model's mean absolute error is only...

EcoDataCenter, a Swedish sustainable data center company, has received $478 million in funding to expand its operations and build more environmentally friendly data centers.

At the recent 360 Nano AI search event for new energy vehicles, Zhou Hongyi, founder and chairman of 360 Group, shared in-depth insights on the widely-discussed DeepSeek. He highly praised its open-source spirit and its positive role in advancing the industry.

Recently, a study conducted by the non-profit organization Epoch AI revealed the energy consumption of OpenAI's chatbot platform ChatGPT, showing that its energy use is significantly lower than earlier estimates. According to some reports, ChatGPT consumes about 3 watt-hours of power to answer a question; however, Epoch AI's research suggests that this figure has been overstated. The study indicates that when using OpenAI's latest default model GPT-4o, the average energy consumption per query is only...