Artificial Intelligence (AI) techniques continue to broaden across governmental and public sectors, such as power and energy - which serve as critical infrastructures for most societal operations. However, due to the requirements of reliability, accountability, and explainability, it is risky to directly apply AI-based methods to power systems because society cannot afford cascading failures and large-scale blackouts, which easily cost billions of dollars. To meet society requirements, this paper proposes a methodology to develop, deploy, and evaluate AI systems in the energy sector by: (1) understanding the power system measurements with physics, (2) designing AI algorithms to forecast the need, (3) developing robust and accountable AI methods, and (4) creating reliable measures to evaluate the performance of the AI model. The goal is to provide a high level of confidence to energy utility users. For illustration purposes, the paper uses power system event forecasting (PEF) as an example, which carefully analyzes synchrophasor patterns measured by the Phasor Measurement Units (PMUs). Such a physical understanding leads to a data-driven framework that reduces the dimensionality with physics and forecasts the event with high credibility. Specifically, for dimensionality reduction, machine learning arranges physical information from different dimensions, resulting inefficient information extraction. For event forecasting, the supervised learning model fuses the results of different models to increase the confidence. Finally, comprehensive experiments demonstrate the high accuracy, efficiency, and reliability as compared to other state-of-the-art machine learning methods.

The Special Economic Zone (SEZ) at Duqm is being prepped for investments related to artificial intelligence (AI) as well as 3D-printing projects, a senior official announced on Saturday. Eng Saleh bin Rashid al Hashmi, Director General of Planning and Engineering Affairs and Head of the Modern Building and 3D Printing Techniques Team at SEZAD, said the zone can provide a suitable and attractive environment for experimenting such technologies offered by the corporates specialised in modern building techniques. Successful technologies can then be promoted within and outside the Sultanate, he noted. "The Zone welcomes the initiatives of local and international private sector companies and research institutions to expand their services and activities and develop their business in SEZAD. Accordingly, investors will be stimulated to use modern technologies that serve reducing the building cost and duration of projects as well as providing environment-friendly buildings", Al Hashmi commented.

Volvo is eager to bring self-driving technology to construction crews, but it's taking a decidedly unusual route to get there. The automaker has unveiled an autonomous wheel loader prototype, the LX03, that's based on a Lego model -- 42081 Lego Technic Concept Wheel Loader Zeux, if you're looking for it. The machine can haul 5 tons and can make its own decisions in a wide variety of situations, including team-ups with human workers. The LX03 is also uniquely modular. Volvo can make "just one or two changes" to produce a larger or smaller loader to meet a customer's demands.

The growing popularity of 3D printing for manufacturing all sorts of items, from customized medical devices to affordable homes, has created more demand for new 3D printing materials designed for very specific uses. To cut down on the time it takes to discover these new materials, researchers at MIT have developed a data-driven process that uses machine learning to optimize new 3D printing materials with multiple characteristics, like toughness and compression strength. By streamlining materials development, the system lowers costs and lessens the environmental impact by reducing the amount of chemical waste. The machine learning algorithm could also spur innovation by suggesting unique chemical formulations that human intuition might miss. "Materials development is still very much a manual process. A chemist goes into a lab, mixes ingredients by hand, makes samples, tests them, and comes to a final formulation. But rather than having a chemist who can only do a couple of iterations over a span of days, our system can do hundreds of iterations over the same time span," says Mike Foshey, a mechanical engineer and project manager in the Computational Design and Fabrication Group (CDFG) of the Computer Science and Artificial Intelligence Laboratory (CSAIL), and co-lead author of the paper.

Plans have been unveiled for a 3D-printed community of 100 new homes in the Austin area, Texas -- which would become the largest development of its kind in the United States to date when construction begins next year. The project is the result of a collaboration between real estate and homebuilding firm Lennar and ICON, a construction engineering company specialising in the development of large-scale 3D-printing technology. They are joined by the architectural firm BIG-Bjarke Ingels Group, who will be designing the houses which will be produced using so-called'additive manufacturing' -- in which objects are printed up one single layer at a time. According to ICON, their'Vulcan construction system' can produce resilient, energy-efficient homes both faster and with less waste than conventional building approaches, while also offering more freedom of design. Buildings and other structures can be built as large as 3,000 square feet using the system, they added.

The time-domain simulation is the fundamental tool for power system transient stability analysis. Accurate and reliable simulations rely on accurate dynamic component modeling. In practical power systems, dynamic component modeling has long faced the challenges of model determination and model calibration, especially with the rapid development of renewable generation and power electronics. In this paper, based on the general framework of neural ordinary differential equations (ODEs), a modified neural ODE module and a neural differential-algebraic equations (DAEs) module for power system dynamic component modeling are proposed. The modules adopt an autoencoder to raise the dimension of state variables, model the dynamics of components with artificial neural networks (ANNs), and keep the numerical integration structure. In the neural DAE module, an additional ANN is used to calculate injection currents. The neural models can be easily integrated into time-domain simulations. With datasets consisting of sampled curves of input variables and output variables, the proposed modules can be used to fulfill the tasks of parameter inference, physics-data-integrated modeling, black-box modeling, etc., and can be easily integrated into power system dynamic simulations. Some simple numerical tests are carried out in the IEEE-39 system and prove the validity and potentiality of the proposed modules.

A partnership between the Massachusetts Institute of Technology and the chemical giant BASF has managed to successfully create an AI-driven process to speed up the discovery of custom 3D printing materials. Chemists usually develop a few iterations of a material candidate over a couple of days and test them in the lab. The new machine-learning algorithm can churn out hundreds of those iterations with the desired characteristics in the same timeframe. This would save time and raw material costs, as well as lessen the environmental impact of the discarded chemicals. Not only that, but the algorithm may also come up with ideas that the material's engineer could have overlooked for various reasons.

Researchers at MIT and BASF have developed a data-driven system that accelerates the process of discovering new 3D printing materials that have multiple mechanical properties. A new machine-learning system costs less, generates less waste, and can be more innovative than manual discovery methods. The growing popularity of 3D printing for manufacturing all sorts of items, from customized medical devices to affordable homes, has created more demand for new 3D printing materials designed for very specific uses. To cut down on the time it takes to discover these new materials, researchers at MIT have developed a data-driven process that uses machine learning to optimize new 3D printing materials with multiple characteristics, like toughness and compression strength. By streamlining materials development, the system lowers costs and lessens the environmental impact by reducing the amount of chemical waste.

The growing popularity of 3D printing for manufacturing all sorts of items, from customized medical devices to affordable homes, has created more demand for new 3D printing materials designed for very specific uses. To cut down on the time it takes to discover these new materials, researchers at MIT have developed a data-driven process that uses machine learning to optimize new 3D printing materials with multiple characteristics, like toughness and compression strength. By streamlining materials development, the system lowers costs and lessens the environmental impact by reducing the amount of chemical waste. The machine learning algorithm could also spur innovation by suggesting unique chemical formulations that human intuition might miss. "Materials development is still very much a manual process. A chemist goes into a lab, mixes ingredients by hand, makes samples, tests them, and comes to a final formulation. But rather than having a chemist who can only do a couple of iterations over a span of days, our system can do hundreds of iterations over the same time span," says Mike Foshey, a mechanical engineer and project manager in the Computational Design and Fabrication Group (CDFG) of the Computer Science and Artificial Intelligence Laboratory (CSAIL), and co-lead author of the paper.

Author and futurist Tom Goodwin goes to London in episode three of The Edge to explore how new technologies like 3D printing, artificial intelligence and algorithms are enabling the next revolution in design. Tom meets Benjamin Hubert, the founder of Layer, a studio that combines the latest technologies with "human-centred design". He sees an example of that vision in their 3D-printed wheelchair, where each seat is custom-designed using algorithms to ensure optimal comfort and performance. Next Tom talks to Mollie Claypool, an academic turned practitioner who is working on a sustainable, modular building method, like Lego for houses. The idea is to build a whole system that can be used by communities to design and build the homes and spaces they need.