A new arXiv preprint introduces Solar Intelligence, a language model built specifically for solar energy decision support. The authors argue that existing solar dashboards present data without explanation and that general-purpose models answer solar questions without citing evidence. Their system is positioned as a domain-focused alternative aimed at producing better-grounded responses for energy planning.
A new arXiv paper proposes combining large language models with multi-agent reinforcement learning to jointly optimize electric vehicle charging scheduling in public charging systems. The approach targets three competing goals at once: driver charging satisfaction, charging station profitability, and stability of the smart grid. It is positioned as a unified optimization method for connected EV infrastructure in IoT settings.
A new arXiv preprint describes a hierarchical ensemble method for short-term photovoltaic power forecasting. The approach pairs temporal neural models with expert fusion that adapts to the forecast horizon, reflecting that regular solar cycles and weather-driven variation matter differently at different lead times. The work targets improved accuracy in solar generation forecasts.
A new arXiv paper systematically examines how much energy large language models consume when serving users in different languages. The authors frame the gap between language coverage and energy use as a "language-energy divide," suggesting that inference cost varies by language and is not well documented. The work aims to give a measurement basis for evaluating the efficiency of multilingual deployments.
A new arXiv preprint introduces Aries, a machine-learned model that produces medium-range weather forecasts tailored to the energy sector. The authors note that weather prediction was traditionally handled by national meteorological agencies, but recent machine-learning advances have opened the field to other developers. The model targets operational and planning use cases where energy companies depend on multi-day forecasts.
A new arXiv paper proposes a game-theoretic approach to coordinating distributed AI training when compute nodes face limits on renewable energy availability. The framework aims to align the incentives of participating nodes so that collaborative training stays both efficient and energy-aware. It addresses settings where heterogeneous hardware and varying power supplies make central planning impractical.
Silicon Valley is moving from simple chatbot interactions toward agentic AI systems that require more computing resources. This shift is helping drive a buildout of data centers to meet the added power and infrastructure demands. The trend raises questions about energy use as AI agents become more capable and widely deployed.
Former Environmental Protection Agency officials say the Trump administration is relaxing pollution rules to accelerate construction of AI data centers. In a new report and briefing, they argue the rollback puts Americans' health at risk and are pressing the president to reverse course. The group's appeal is seen as unlikely to change the administration's approach.
A new arXiv paper proposes an intrusion detection approach for digitised electrical distribution networks that combines neuromorphic temporal embeddings with a hybrid spiking neural network and XGBoost classifier. The authors frame the work as a response to the high computational cost of existing deep-learning-based detection systems. They also evaluate robustness when machine unlearning attacks are used against the model.
A new arXiv preprint introduces LoaDiff, a method for conditionally generating residential electricity consumption time series. The work is motivated by the energy transition, where distributed generation, electrified appliances and demand-response programs are shifting how households use power. The authors argue that granular synthetic consumption data can support energy analytics; the abstract is truncated in this report.
A new arXiv paper examines the "job power elasticity" of large language model training, looking at how much these workloads can adjust their electricity consumption. Power supply is described as a key constraint on the growth of AI data centers, which are among the fastest-rising sources of electricity demand. The work aims to characterize how training jobs could respond to power limits.
Researchers propose a chat-based explainability assistant designed to help building operators and facility managers understand predictions from complex energy consumption models, including symbolic regressors built with genetic programming. The tool is presented as a way to make model outputs more accessible to non-experts who manage energy use.
A Guardian editorial cartoon looks at both the advantages and the criticisms attached to the rapid build-out of AI data centres. It touches on themes such as energy and water consumption, land use and local community impact that increasingly shape debate over AI infrastructure. The piece is opinion commentary rather than a reported news story.
Massachusetts has approved new clean energy requirements for data center operators, making it the third US state to tighten rules on such facilities within a few months. The measures add to a growing set of state-level restrictions on data center growth, driven largely by rising power demand from AI workloads.
Google is committing more than 13 billion euros to build out AI infrastructure in Finland, marking its largest single investment in Europe to date. The arrangement also includes a power purchase agreement covering nuclear-generated electricity for the sites.
Google plans to spend a minimum of €13 billion on Finnish operations across 2027-2028, contingent on a long-term power purchase agreement with utility Fortum. The 22-year contract is linked to extending the Loviisa nuclear plant, giving Google stable low-carbon electricity for its planned data centres in the country.
New analysis from environmental thinktank Verdant estimates that UK datacentres will employ about 10,400 people, far below the roughly 40,000 jobs the tech sector has projected. The research also highlights the large energy demands these facilities place on the grid.