Ben & Jerry's row deepens as three board members removed Three members of Ben & Jerry's independent board will no longer be eligible to serve in their roles, after the ice cream company introduced a new set of governance practices. These include a nine-year limit set on board members' terms. Chair Anuradha Mittal, who earlier said she had no plans to resign under pressure, is among those affected. The move was criticised by the company's co-founder Ben Cohen, who called it a blatant power grab designed to strip the board of legal authority and independence. His remarks are the latest in a long-running row between Ben and Jerry's and its owner over the Cherry Garcia maker's social activism and the continued independence of its board.

UK launches taskforce to'break down barriers' for women in technology The government has launched a new taskforce it says will help women enter, stay and lead in the UK tech sector. Led by technology secretary Liz Kendall, it will see female leaders from tech companies and organisations advise the government on how to boost diversity and economic growth in the industry. BCS, the Chartered Institute for IT, recently suggested women accounted for only 22% of those working in IT specialist roles in the UK. Ms Kendall said the Women in Tech group would break down the barriers that still hold too many people back. When women are inspired to take on a role in tech and have a seat at the table, the sector can make more representative decisions, build products that serve everyone, she said.

With the global energy transition and rapid development of renewable energy, the scheduling optimization challenge for combined power-heat systems under new energy integration and multiple uncertainties has become increasingly prominent. Addressing this challenge, this study proposes an intelligent scheduling method based on the improved Dual-Delay Deep Deterministic Policy Gradient (PVTD3) algorithm. System optimization is achieved by introducing a penalty term for grid power purchase variations. Simulation results demonstrate that under three typical scenarios (10%, 20%, and 30% renewable penetration), the PVTD3 algorithm reduces the system's comprehensive cost by 6.93%, 12.68%, and 13.59% respectively compared to the traditional TD3 algorithm. Concurrently, it reduces the average fluctuation amplitude of grid power purchases by 12.8%. Regarding energy storage management, the PVTD3 algorithm reduces the end-time state values of low-temperature thermal storage tanks by 7.67-17.67 units while maintaining high-temperature tanks within the 3.59-4.25 safety operating range. Multi-scenario comparative validation demonstrates that the proposed algorithm not only excels in economic efficiency and grid stability but also exhibits superior sustainable scheduling capabilities in energy storage device management.

A key element to support the increased amounts of renewable energy in the energy system is flexibility, i.e., the possibility of changing energy loads in time and amount. Many flexibility models have been designed; however, exact models fail to scale for long time horizons or many devices. Because of this, the FlexOffers model has been designed, to provide device-independent approximations of flexibility with good accuracy, and much better scaling for long time horizons and many devices. An important aspect of the real-life implementation of energy flexibility is enabling flexible data exchange with many smart energy appliances and market systems, e.g., in smart buildings. For this, ontologies standardizing data formats are required. However, the current industry standard ontology for integrating smart devices for energy purposes, SAREF for Energy Flexibility (SAREF4ENER), only has limited support for flexibility and thus cannot support important use cases. In this paper, we propose an extension of SAREF4ENER that integrates full support for the complete FlexOffer model, including advanced use cases, while maintaining backward compatibility. This novel ontology module can accurately describe flexibility for advanced devices such as electric vehicles, batteries, and heat pumps. It can also capture the inherent uncertainty associated with many flexible load types.

They gathered together on a sunny July evening, between the churchyard's trees and leaning tombstones, to give thanks for the heat pump. Facing the newly installed system, in its large green metal box, they sang hymns and said prayers. "To thank God, really, for being able to work His wonders in mysterious ways," says Karen Crowhurst, who is part of a committee that helps to run St. The previous month, a flatbed truck carrying a hefty new heat pump system had eased itself onto the church grounds. By late July, the device was fully installed, and soon followed an outdoor thanksgiving service .

Heat pumps can save you about $370 per year and are good for the planet. Heat pumps date back to the 1850s and are more energy efficient than furnaces or boilers. Breakthroughs, discoveries, and DIY tips sent every weekday. Colder weather is quickly approaching, which means it's time for many folks to start cranking up the heat in their homes and apartments. But for many Americans, heating up their homes is a costly affair-and it's only getting more expensive.

Short-term forecasting of residential electricity demand is an important task for utilities. Yet, many small and medium-sized utilities still use simple forecasting approaches such as Synthesized Load Profiles, which treat residential households similarly and neither account for renewable energy installations nor novel large consumers (e.g., heat pumps, electric vehicles). The effectiveness of such "one-fits-all" approaches in future grid states--where decentral generation and sector coupling increases--are questionable. Our study challenges these forecasting practices and investigates whether Machine Learning (ML) approaches are suited to predict electricity demand in today's and in future grid states. We use real smart meter data from 3,511 households in Germany over 34 months. We extrapolate this data with future grid states (i.e., increased decentral generation and storage) based on a digital twin of a local energy system. Our results show that Long Short-Term Memory (LSTM) approaches outperform SLPs as well as simple benchmark estimators with up to 68.5% lower Root Mean Squared Error for a day-ahead forecast, especially in future grid states. Nevertheless, all prediction approaches perform worse in future grid states. Our findings therefore reinforce the need (a) for utilities and grid operators to employ ML approaches instead of traditional demand prediction methods in future grid states and (b) to prepare current ML methods for future grid states.

Constrained Reinforcement Learning (RL) has emerged as a significant research area within RL, where integrating constraints with rewards is crucial for enhancing safety and performance across diverse control tasks. In the context of heating systems in the buildings, optimizing the energy efficiency while maintaining the residents' thermal comfort can be intuitively formulated as a constrained optimization problem. However, to solve it with RL may require large amount of data. Therefore, an accurate and versatile simulator is favored. In this paper, we propose a novel building simulator I4B which provides interfaces for different usages and apply a model-free constrained RL algorithm named constrained Soft Actor-Critic with Linear Smoothed Log Barrier function (CSAC-LB) to the heating optimization problem. Benchmarking against baseline algorithms demonstrates CSAC-LB's efficiency in data exploration, constraint satisfaction and performance.

While we still don't know all the details, there have been reports that researchers at OpenAI had made a "breakthrough" in AI that alarmed staff members. The claim is that they came up with a new way to make powerful AI systems and had created a new model, called Q* (pronounced Q star), that was able to perform grade-school level math. Some at OpenAI reportedly believe this could be a breakthrough in the company's quest to build artificial general intelligence, a much-hyped concept of an AI system that is smarter than humans. And why is grade-school math such a big deal? Our senior AI reporter Melissa Heikkilä called some experts to find out how big of a deal any such breakthrough would really be.

This paper outlines a natural conversational approach to solving personalized energy-related problems using large language models (LLMs). We focus on customizable optimization problems that necessitate repeated solving with slight variations in modeling and are user-specific, hence posing a challenge to devising a one-size-fits-all model. We put forward a strategy that augments an LLM with an optimization solver, enhancing its proficiency in understanding and responding to user specifications and preferences while providing nonlinear reasoning capabilities. Our approach pioneers the novel concept of human-guided optimization autoformalism, translating a natural language task specification automatically into an optimization instance. This enables LLMs to analyze, explain, and tackle a variety of instance-specific energy-related problems, pushing beyond the limits of current prompt-based techniques. Our research encompasses various commonplace tasks in the energy sector, from electric vehicle charging and Heating, Ventilation, and Air Conditioning (HVAC) control to long-term planning problems such as cost-benefit evaluations for installing rooftop solar photovoltaics (PVs) or heat pumps. This pilot study marks an essential stride towards the context-based formulation of optimization using LLMs, with the potential to democratize optimization processes. As a result, stakeholders are empowered to optimize their energy consumption, promoting sustainable energy practices customized to personal needs and preferences.