West Somerset Lagoon would harness renewable energy for UK's AI boom - and create'iconic' arc around Bristol Channel The architect of the London Eye wants to build a vast tidal power station in a 14-mile arc off the coast of Somerset that could help Britain meet surging electricity demand to power artificial intelligence - and create a new race track to let cyclists skim over the Bristol Channel. Julia Barfield, who designed the Eye and the i360 observation tower in Brighton, is part of a team that has drawn up the £11bn proposal. The proposal comes amid growing concern that rapidly rising use of AI in Britain will drive up carbon emissions unless more renewable energy sources are found. The AI boom is expected to add to sharp increases in demand for electricity across the UK, which the government estimated this month could more than double by 2050. "If the decision is to go ahead with adopting more and more AI - which I am surprised is not being questioned more at a time of climate emergency - then it is going to be better with a renewable energy source," said Barfield.

Google is still aiming for its "moonshot" 2030 energy goals The company's electricity demand has doubled since 2020, making its end-of-decade target more of a challenge. Last week, we hosted EmTech MIT, MIT Technology Review's annual flagship conference in Cambridge, Massachusetts. Over the course of three days of main-stage sessions, I learned about innovations in AI, biotech, and robotics. But as you might imagine, some of this climate reporter's favorite moments came in the climate sessions. I was listening especially closely to my colleague James Temple's discussion with Lucia Tian, head of advanced energy technologies at Google. They spoke about the tech giant's growing energy demand and what sort of technologies the company is looking to to help meet it.

This week, Casey Crownhart, senior reporter for energy at MIT Technology Review and Pilita Clark, FT's columnist, consider how China's rapid renewables buildout could help it leapfrog on AI progress. In the age of AI, the biggest barrier to progress isn't money but energy . That should be particularly worrying here in the US, where massive data centers are waiting to come online, and it doesn't look as if the country will build the steady power supply or infrastructure needed to serve them all. For about a decade before 2020, data centers were able to offset increased demand with efficiency improvements . Now, though, electricity demand is ticking up in the US, with billions of queries to popular AI models each day--and efficiency gains aren't keeping pace. With too little new power capacity coming online, the strain is starting to show: Electricity bills are ballooning for people who live in places where data centers place a growing load on the grid.

Forecasting electricity demand is increasingly challenging as energy systems become more decentralized and intertwined with renewable sources. Graph Neural Networks (GNNs) have recently emerged as a powerful paradigm to model spatial dependencies in load data while accommodating complex non-stationarities. This paper introduces a comprehensive framework that integrates graph-based forecasting with attention mechanisms and ensemble aggregation strategies to enhance both predictive accuracy and interpretability. Several GNN architectures -- including Graph Convolutional Networks, GraphSAGE, APPNP, and Graph Attention Networks -- are systematically evaluated on synthetic, regional (France), and fine-grained (UK) datasets. Empirical results demonstrate that graph-aware models consistently outperform conventional baselines such as Feed Forward Neural Networks and foundation models like TiREX. Furthermore, attention layers provide valuable insights into evolving spatial interactions driven by meteorological and seasonal dynamics. Ensemble aggregation, particularly through bottom-up expert combination, further improves robustness under heterogeneous data conditions. Overall, the study highlights the complementarity between structural modeling, interpretability, and robustness, and discusses the trade-offs between accuracy, model complexity, and transparency in graph-based electricity load forecasting.

Why he thinks near-term targets can be a distraction, and what technologies he expects to power our future grid. Bill Gates doesn't shy away or pretend modesty when it comes to his stature in the climate world today. "Well, who's the biggest funder of climate innovation companies?" he asked a handful of journalists at a media roundtable event last week. "If there's someone else, I've never met them." The former Microsoft CEO has spent the last decade investing in climate technology through Breakthrough Energy, which he founded in 2015. Ahead of the UN climate meetings kicking off next week, Gates published a memo outlining what he thinks activists and negotiators should focus on and how he's thinking about the state of climate tech right now.

Countries have fallen behind on emissions goals, but Hannah Ritchie looks at the numbers and sees real gains. Get your news from a source that's not owned and controlled by oligarchs. It has been 10 years since countries signed on to the Paris Agreement, and emissions and temperatures continue to reach new highs, fueling unprecedented weather disasters around the globe. Meanwhile, the shift to clean energy is facing powerful headwinds in the United States, where climate policies are being reversed and support for clean energy is withdrawn. Yet, while the headlines paint a dismal picture of efforts to rein in climate change, the numbers often tell a different story. That is the assessment of data scientist Hannah Ritchie, a researcher at the University of Oxford and deputy editor of the publication .

Nonlinear and delayed effects of covariates often render time series forecasting challenging. To this end, we propose a novel forecasting framework based on ridge regression with signature features calculated on sliding windows. These features capture complex temporal dynamics without relying on learned or hand-crafted representations. Focusing on the discrete-time setting, we establish theoretical guarantees, namely universality of approximation and stationarity of signatures. We introduce an efficient sequential algorithm for computing signatures on sliding windows. The method is evaluated on both synthetic and real electricity demand data. Results show that signature features effectively encode temporal and nonlinear dependencies, yielding accurate forecasts competitive with those based on expert knowledge.

This paper presents a machine learning framework for electricity demand forecasting across diverse geographical regions using the gradient boosting algorithm XGBoost. The model integrates historical electricity demand and comprehensive weather and socioeconomic variables to predict normalized electricity demand profiles. To enable robust training and evaluation, we developed a large-scale dataset spanning multiple years and countries, applying a temporal data-splitting strategy that ensures benchmarking of out-of-sample performance. Our approach delivers accurate and scalable demand forecasts, providing valuable insights for energy system planners and policymakers as they navigate the challenges of the global energy transition.

This study presents the development and optimization of a deep learning model based on Long Short-Term Memory (LSTM) networks to predict short-term hourly electricity demand in Córdoba, Argentina. Integrating historical consumption data with exogenous variables (climatic factors, temporal cycles, and demographic statistics), the model achieved high predictive precision, with a mean absolute percentage error of 3.20\% and a determination coefficient of 0.95. The inclusion of periodic temporal encodings and weather variables proved crucial to capture seasonal patterns and extreme consumption events, enhancing the robustness and generalizability of the model. In addition to the design and hyperparameter optimization of the LSTM architecture, two complementary analyses were carried out: (i) an interpretability study using Random Forest regression to quantify the relative importance of exogenous drivers, and (ii) an evaluation of model performance in predicting the timing of daily demand maxima and minima, achieving exact-hour accuracy in more than two-thirds of the test days and within abs(1) hour in over 90\% of cases. Together, these results highlight both the predictive accuracy and operational relevance of the proposed framework, providing valuable insights for grid operators seeking optimized planning and control strategies under diverse demand scenarios.

Abstract--We develop an energy management system (EMS) for artificial intelligence (AI) data centers with colocate d renewable generation. Under a cost-minimizing framework, th e EMS of renewable-colocated data center (RCDC) co-optimize s AI workload scheduling, on-site renewable utilization, an d electricity market participation. Within both wholesale and re tail market participation models, the economic benefit of the RCD C operation is maximized. Empirical evaluations using real-world traces of electricity prices, data center power consumptio n, and renewable generation demonstrate significant electric ity cost reduction from renewable and AI data center colocations. Index T erms --AI data center power system, energy management system, flexible demand, large load colocation, worklo ad scheduling.