Over the past decade, Virginia Natural Gas (VNG) has continuously worked to modernize its pipeline infrastructure and has coordinated efforts with the Virginia State Corporation Commission, local governments, excavators and Virginia 811 (VA811) to promote safe digging and build awareness of the damage prevention laws that keep customers and communities safe. The investments VNG has made in infrastructure upgrades have created some of the safest, most modern pipelines that customers depend on for the safe and reliable delivery of natural gas. By modernizing more than 400 miles of aging, older pipes through the Steps to Advance Virginia's Energy (SAVE) program, VNG is enhancing the safety and reliability of systems to meet current and future energy needs for generations to come. VNG is now incorporating innovative technology to help predict and prevent damages to its critical infrastructure. The technology uses artificial intelligence to predict which third-party dig requests are most at risk for potential excavation damage.

Dr. Barry Po is the EVP & Chief Marketing Officer at mCloud, using AI to unlock the untapped potential of energy-intensive assets. A San Francisco auditorium is spellbound. The audience is catching a 90-minute glimpse into the future of work. Teams edit documents together seamlessly in real time. They collaborate virtually over video around the world with a single click as they effortlessly navigate the reams of data and information needed to make important decisions.

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Surrogate models are data-based approximations of computationally expensive simulations that enable efficient exploration of the model's design space and informed decision-making in many physical domains. The usage of surrogate models in the vibroacoustic domain, however, is challenging due to the non-smooth, complex behavior of wave phenomena. This paper investigates four Machine Learning (ML) approaches in the modelling of surrogates of Sound Transmission Loss (STL). Feature importance and feature engineering are used to improve the models' accuracy while increasing their interpretability and physical consistency. The transfer of the proposed techniques to other problems in the vibroacoustic domain and possible limitations of the models are discussed.

Forecasting electricity prices is a challenging task and an active area of research since the 1990s and the deregulation of the traditionally monopolistic and government-controlled power sectors. Although it aims at predicting both spot and forward prices, the vast majority of research is focused on short-term horizons which exhibit dynamics unlike in any other market. The reason is that power system stability calls for a constant balance between production and consumption, while being weather (both demand and supply) and business activity (demand only) dependent. The recent market innovations do not help in this respect. The rapid expansion of intermittent renewable energy sources is not offset by the costly increase of electricity storage capacities and modernization of the grid infrastructure. On the methodological side, this leads to three visible trends in electricity price forecasting research as of 2022. Firstly, there is a slow, but more noticeable with every year, tendency to consider not only point but also probabilistic (interval, density) or even path (also called ensemble) forecasts. Secondly, there is a clear shift from the relatively parsimonious econometric (or statistical) models towards more complex and harder to comprehend, but more versatile and eventually more accurate statistical/machine learning approaches. Thirdly, statistical error measures are nowadays regarded as only the first evaluation step. Since they may not necessarily reflect the economic value of reducing prediction errors, more and more often, they are complemented by case studies comparing profits from scheduling or trading strategies based on price forecasts obtained from different models.

Asia's urban population has steadily increased over the last several years, bringing added environmental and infrastructure growing pains. Technology will be a key leveler to mitigate these issues and ensure that the region is well placed to capture the emerging opportunities. Already, communication networks serve as the backbone for smart grids conveying information as well as data transmission from the use of artificial intelligence (AI) and machine learning (ML). These technologies not only improve the overall quality of life for growing cities, but also overcome constraints on productivity. We've seen a plethora of examples where new tech has helped address these growing pains.

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Israeli space weather researchers report that they used artificial intelligence (AI) to predict powerful radiation outbreaks up to 96 hours before they occurred. Harmful radiation from these eruptions – also known as solar storms -- can have a significant impact on our lives here on Earth. They produce disturbances in atmospheric layers through which communication and GPS signals pass. That leads to disruption of satellite activity, navigation systems, communications and electric grids. Intense radiation bursts also can result in dangerous and costly spacecraft failures.

Robotized warehouses are deployed to automatically distribute millions of items brought by the massive logistic orders from e-commerce. A key to automated item distribution is to plan paths for robots, also known as task planning, where each task is to deliver racks with items to pickers for processing and then return the rack back. Prior solutions are unfit for large-scale robotized warehouses due to the inflexibility to time-varying item arrivals and the low efficiency for high throughput. In this paper, we propose a new task planning problem called TPRW, which aims to minimize the end-to-end makespan that incorporates the entire item distribution pipeline, known as a fulfilment cycle. Direct extensions from state-of-the-art path finding methods are ineffective to solve the TPRW problem because they fail to adapt to the bottleneck variations of fulfillment cycles. In response, we propose Efficient Adaptive Task Planning, a framework for large-scale robotized warehouses with time-varying item arrivals. It adaptively selects racks to fulfill at each timestamp via reinforcement learning, accounting for the time-varying bottleneck of the fulfillment cycles. Then it finds paths for robots to transport the selected racks. The framework adopts a series of efficient optimizations on both time and memory to handle large-scale item throughput. Evaluations on both synthesized and real data show an improvement of $37.1\%$ in effectiveness and $75.5\%$ in efficiency over the state-of-the-arts.

Data assimilation presents computational challenges because many high-fidelity models must be simulated. Various deep-learning-based surrogate modeling techniques have been developed to reduce the simulation costs associated with these applications. However, to construct data-driven surrogate models, several thousand high-fidelity simulation runs may be required to provide training samples, and these computations can make training prohibitively expensive. To address this issue, in this work we present a framework where most of the training simulations are performed on coarsened geomodels. These models are constructed using a flow-based upscaling method. The framework entails the use of a transfer-learning procedure, incorporated within an existing recurrent residual U-Net architecture, in which network training is accomplished in three steps. In the first step. where the bulk of the training is performed, only low-fidelity simulation results are used. The second and third steps, in which the output layer is trained and the overall network is fine-tuned, require a relatively small number of high-fidelity simulations. Here we use 2500 low-fidelity runs and 200 high-fidelity runs, which leads to about a 90% reduction in training simulation costs. The method is applied for two-phase subsurface flow in 3D channelized systems, with flow driven by wells. The surrogate model trained with multifidelity data is shown to be nearly as accurate as a reference surrogate trained with only high-fidelity data in predicting dynamic pressure and saturation fields in new geomodels. Importantly, the network provides results that are significantly more accurate than the low-fidelity simulations used for most of the training. The multifidelity surrogate is also applied for history matching using an ensemble-based procedure, where accuracy relative to reference results is again demonstrated.