In response to a growing backlash, Microsoft said it would take steps to ensure that data centers don't raise utility bills in surrounding areas and address other public concerns. A Microsoft data center in Aldie, Virginia.Photograph: Bloomberg/Getty Images Microsoft said on Tuesday that it would be taking a series of steps toward becoming a "good neighbor" in communities where it is building data centers--including promising to ask public utilities to set higher electricity rates for data centers. Speaking onstage at an event in Great Falls, Virginia, Microsoft vice chair and president Brad Smith directly referenced a growing national pushback to data centers, describing it as creating "a moment in time when we need to listen, and we need to address these concerns head-on." "When I visit communities around the country, people have questions--pointed questions. They even have concerns," Smith said, as a slide showed headlines from various news outlets about opposition to data centers.

The influential progressive third party announced Thursday that it was putting out a recruitment call for candidates specifically opposed to data centers. The Working Families Party said Thursday that it is putting out a specific recruitment call for people who are organizing against data centers in their communities to run for office. The announcement comes amid a period of heightened political turmoil around data centers, as some high-profile Democrats wade into the fight. Earlier this week, three Democrats in the Senate sent letters seeking information from Big Tech companies about how data centers impact electricity bills, while senator Bernie Sanders, the independent from Vermont, became the first national politician to call for a moratorium on data center construction. "We see our role as responding to what working families and working people are concerned about, what issues are keeping them up at night," says Ravi Mangla, the national press secretary for the Working Families Party. "We would be ignoring the needs of our constituents if we were not responding to the issue of data centers and their impacts on communities."

Fox News anchor Bret Baier examines the U.S. power supply on'Special Report.' If your electricity bill seems shockingly high, you're not imagining it. A big part of the spike is being driven by rising artificial intelligence electricity demand. PJM Interconnection, the largest power grid operator in the United States, says electricity usage is climbing sharply this summer. Some areas may see bills increase by as much as 20%.

Model predictive control (MPC) controller is considered for temperature management in buildings but its performance heavily depends on hyperparameters. Consequently, MPC necessitates meticulous hyperparameter tuning to attain optimal performance under diverse contracts. However, conventional building controller design is an open-loop process without critical hyperparameter optimization, often leading to suboptimal performance due to unexpected environmental disturbances and modeling errors. Furthermore, these hyperparameters are not adapted to different pricing schemes and may lead to non-economic operations. To address these issues, we propose an efficient performance-oriented building MPC controller tuning method based on a cutting-edge efficient constrained Bayesian optimization algorithm, CONFIG, with global optimality guarantees. We demonstrate that this technique can be applied to efficiently deal with real-world DSM program selection problems under customized black-box constraints and objectives. In this study, a simple MPC controller, which offers the advantages of reduced commissioning costs, enhanced computational efficiency, was optimized to perform on a comparable level to a delicately designed and computationally expensive MPC controller. The results also indicate that with an optimized simple MPC, the monthly electricity cost of a household can be reduced by up to 26.90% compared with the cost when controlled by a basic rule-based controller under the same constraints. Then we compared 12 real electricity contracts in Belgium for a household family with customized black-box occupant comfort constraints. The results indicate a monthly electricity bill saving up to 20.18% when the most economic contract is compared with the worst one, which again illustrates the significance of choosing a proper electricity contract.

ALEX (Autonomous Local Energy eXchange) is an economy-driven, transactive local energy market where each participating building is represented by a rational agent. Relying solely on building-level information, this agent minimizes its electricity bill by automating distributed energy resource utilization and trading. This study examines ALEX's capabilities to align participant and grid-stakeholder interests and assesses ALEX's impact on short- and long-term intermittence using a set of community net-load metrics, such as ramping rate, load factor, and peak load. The policies for ALEX's rational agents are generated using dynamic programming through value iteration in conjunction with iterative best response. This facilitates comparing ALEX and a benchmark energy management system, which optimizes building-level self-consumption, ramping rate, and peak net load. Simulations are performed using the open-source CityLearn2022 dataset to provide a pathway for benchmarking by future studies. The experiments demonstrate that ALEX enables the coordination of distributed energy resources across the community. Remarkably, this community-level coordination occurs even though the system is populated by agents who only access building-level information and selfishly maximize their own relative profit. Compared to the benchmark energy management system, ALEX improves across all metrics.

Global transformations are taking place to get the most out of the data because of the widespread deployment of smart meters, which present more than 16 million in the United Kingdom. Aim of researchers and utilities are Timely and accurate billing, a better understanding of home energy use, easing the transition to renewable energy and electric vehicles, and improved management of electricity generation and distribution. By reducing unnecessary energy use, households and utilities can cut costs and achieve goals related to energy efficiency and climate change. Artificial intelligence is the solution. Emerging technologies like Artificial Intelligence have a role in industries.

For all the modern conveniences technology brings to the home – Wi-Fi-enabled washing machines, powerful gaming systems and enormous smart televisions – one of the downsides is paying to power it all. In fact, home utility costs are continuing to spike for many parts of the country, with 2021 electricity prices rising at the fastest rate since 2008, says the U.S. Energy Information Administration (EIA) – already hitting Americans facing skyrocketing inflation, resulting in higher costs for many goods and services. Not only does the average household have dozens of consumer electronics products plugged into power outlets at any given time, most consume electricity when not in use. "Vampire power" – also referred to as "phantom power" or "standby power" – can account for as much as 10% of a household's electricity bill, says the Environmental Protection Agency (EPA). This can really add up.

With each passing year, models are getting more complex and bigger. A lot of AI models developed in research labs never see the light of the day. We can see that from 2012 to 2015, state-of-the-art Image recognition models went from being 8 layers to 152 layers. This begs the question is there a way to keep the model lighter and still able to process more complex data. The big challenge that we face with a bigger network is the training times.

Is Artificial Intelligence (AI) here to stay? Will it bring a complete end to human interventions? Such questions set the context in which people generally think about AI. Whether they have come to a conclusion or not is a different matter altogether. But it can't be denied that today, AI is integral to various organizations' growth.

SunPower has launched a web application that can design rooftop solar projects in seconds. SunPower's Design Studio combines SunPowers's Instant Design technology, Google Cloud and Google Sunroof to deliver customizable residential solar designs that take into account roof size, shading and energy potential. Homeowners can modify their design based on their energy needs, resulting in more accurate predictions of electricity bill and energy savings. "With SunPower Design Studio, we've created a new solar buying experience," said Jake Wachman, digital VP for SunPower. "We're making solar accessible by enabling homeowners nationwide to envision solar on their home and understand savings at lightning-fast speeds. With SunPower Design Studio, we're changing how homeowners go solar."