Within wind farms, wake effects between turbines can significantly reduce overall energy production. Wind farm flow control encompasses methods designed to mitigate these effects through coordinated turbine control. Wake steering, for example, consists in intentionally misaligning certain turbines with the wind to optimize airflow and increase power output. However, designing a robust wake steering controller remains challenging, and existing machine learning approaches are limited to quasi-static wind conditions or small wind farms. This work presents a new deep reinforcement learning methodology to develop a wake steering policy that overcomes these limitations. Our approach introduces a novel architecture that combines graph attention networks and multi-head self-attention blocks, alongside a novel reward function and training strategy. The resulting model computes the yaw angles of each turbine, optimizing energy production in time-varying wind conditions. An empirical study conducted on steady-state, low-fidelity simulation, shows that our model requires approximately 10 times fewer training steps than a fully connected neural network and achieves more robust performance compared to a strong optimization baseline, increasing energy production by up to 14 %. To the best of our knowledge, this is the first deep reinforcement learning-based wake steering controller to generalize effectively across any time-varying wind conditions in a low-fidelity, steady-state numerical simulation setting.

--This paper explores the implementation of a Deep Reinforcement Learning (DRL)-Optimized energy management system for e-commerce data centers, aimed at enhancing energy efficiency, cost-effectiveness, and environmental sustainability. The proposed system leverages DRL algorithms to dynamically manage the integration of renewable energy sources, energy storage, and grid power, adapting to fluctuating energy availability in real-time. The study demonstrates that the DRL-Optimized system achieves a 38% reduction in energy costs, significantly outperforming traditional Reinforcement Learning (RL) methods (28%) and heuristic approaches (22%). Additionally, it maintains a low SLA violation rate of 1.5%, compared to 3.0% for RL and 4.8% for heuristic methods. The DRL-Optimized approach also results in an 82% improvement in energy efficiency, surpassing other methods, and a 45% reduction in carbon emissions, making it the most environmentally friendly solution. The system's cumulative reward of 950 reflects its superior performance in balancing multiple objectives. As global e-commerce demand continues to surge, data centers have experienced a significant increase in energy consumption, making energy efficiency an ever more pressing issue. Data centers, the backbone of e-commerce operations, must function continuously to support this infrastructure, resulting in high energy costs and a considerable carbon footprint [1]-[4].

Solar vehicles, which simultaneously produce and consume energy, require meticulous energy management. However, potential users often feel uncertain about their operation compared to conventional vehicles. This study presents a simulator designed to help users understand long-distance travel in solar vehicles and recognize the importance of proper energy management. By utilizing Google Maps data and weather information, the simulator replicates real-world driving conditions and provides a dashboard displaying vehicle status, updated hourly based on user-inputted speed. Users can explore various speed policy scenarios and receive recommendations for optimal driving strategies. The simulator's effectiveness was validated using the route of the World Solar Challenge (WSC). This research enables users to monitor energy dynamics before a journey, enhancing their understanding of energy management and informing appropriate speed decisions.

Azure high-performance computing provides a platform for energy industry innovation at scale. Global energy demand has rapidly increased over the last few years and looks set to continue accelerating at such a pace. With a booming middle class, economic growth, digitization, urbanization, and increased mobility of populations, energy suppliers are in a race to leverage the development of new technologies that can more optimally and sustainably generate, store, and transport energy to consumers. With the impact of climate change adding urgency to minimizing energy waste, in addition to optimizing power production leaders in the renewable energy as well as oil and gas industries are accelerating sector-wide innovation initiatives that can drive differentiated impact and outcomes at scale. As the population of developing countries continues to expand, the energy needs of billions of additional people in rural and especially urban areas will need to be catered to.

Technology is central to the country's sustainability agenda. Malaysia's commercial hub, Kuala Lumpur, has rolled out a smart city plan, which includes accelerating digital transformation by focusing on education and promoting cloud technologies and artificial intelligence (AI), among other areas. The Malaysian government has also emphasized technology investment in its Budget 2022, with up to MYR 100 million (US$ 23.7 million) in grants for areas such as smart automation and at least MYR 30 billion (US$ 7 billion) for government-linked companies investing in renewable energy, supply-chain modernization, and 5G infrastructure. In recent years, Kuala Lumpur has also seen an increasing number of "greening" opportunities. For instance, the city governance has employed a smart "City Brain", which uses Alibaba Cloud's computing systems to optimize services like traffic control and even calculate the best routes for emergency services.

AJ Abdallat is CEO of Beyond Limits, a leader in artificial intelligence and cognitive computing. Artificial intelligence (AI), machine learning (ML) and similar digitalization solutions are modifying the way the world's most influential companies and industries -- as well as entire cities -- function every day. When working in harmony with humans, AI and other automation systems have the potential to make huge impacts on economic growth across the globe, going so far as to support solving humanity's most critical roadblocks, from streamlining energy production to improving grid systems and achieving more sustainable operations for nearly every major industry on Earth. As the CEO of an AI company making advanced digitalization software products and solutions, the paradigm of enabling people and AI to work together on achieving more sustainable operations is always top of mind; its importance cannot be curbed. As we move into the future, I'm confident there will be plenty of jobs for both humans and AI so long as they are able to function in conjunction with one another.

Hydropower has been stirring up controversies since the early 2000s. Despite being promoted as a solution to mitigate climate change, the hydropower bubble burst when researchers discovered in 2005 that hydropower dams are responsible for huge amounts of greenhouse gas emissions. Hydropower dams' walls restrict the flow of rivers and turn them into pools of stagnant water. Reservoir surfaces and turbines then release methane into the atmosphere. Methane makes up approximately 80 percent of the greenhouse gases emitted from hydropower dams, peaking in the first decade of the dams lifecycle.

The exponential growth of distributed energy resources is enabling the transformation of traditional consumers in the smart grid into prosumers. Such transition presents a promising opportunity for sustainable energy trading. Yet, the integration of prosumers in the energy market imposes new considerations in designing unified and sustainable frameworks for efficient use of the power and communication infrastructure. Furthermore, several issues need to be tackled to adequately promote the adoption of decentralized renewable-oriented systems, such as communication overhead, data privacy, scalability, and sustainability. In this article, we present the different aspects and challenges to be addressed for building efficient energy trading markets in relation to communication and smart decision-making. Accordingly, we propose a multi-level pro-decision framework for prosumer communities to achieve collective goals. Since the individual decisions of prosumers are mainly driven by individual self-sufficiency goals, the framework prioritizes the individual prosumers' decisions and relies on 5G wireless network for fast coordination among community members. In fact, each prosumer predicts energy production and consumption to make proactive trading decisions as a response to collective-level requests. Moreover, the collaboration of the community is further extended by including the collaborative training of prediction models using Federated Learning, assisted by edge servers and prosumer home-area equipment. In addition to preserving prosumers' privacy, we show through evaluations that training prediction models using Federated Learning yields high accuracy for different energy resources while reducing the communication overhead.

As created for AI4IMPACT's Deep Learning Datathon 2020, TEAM DEFAULT has created a neural-network-based deep learning model used for predicting energy production demand in France. The model was created using Smojo, on AI4IMPACT's innovative cloud-based learning and model deployment system. Our model was able to achieve a 0.131 test loss which beat persistence loss of 0.485 by a quite a fair margin. As the energy market becomes increasingly liberalized across the world, the free and open market has seen an uptick and importance for optimized energy demand. New and existing entrants turn to data and various methods to forecast energy consumption in hopes of turning over a profit.

Today's article is about a particularly inspiring AGI Podcast revolving around decentralized efforts to achieve synoptical systems for social good, and which ties in with a new endeavor undertaken by the SingularityNET team. This week we interviewed a prominent figure in the European blockchain and AI innovation scene: Jan-Peter Doomernik. Jan-Peter is Nature 2.0's Lead Architect and a Senior Business Developer working in one of Holland's leading distribution service operators (DSO) Enexis Netbeheer. In the podcast, we discuss the "demystification of complexity", the upcoming Odyssey hackathon, and the efforts that civil society, academia and industry can make to introduce new autonomous systems imbued with humanitarianism. "In forests, you have big trees and little trees and those trees are connected like a network in which the big trees share resources of sunlight and water to the little trees so that the little trees do not have to become competitors."