--We present a comparative study of different probabilistic forecasting techniques on the task of predicting the electrical load of secondary substations and cabinets located in a low voltage distribution grid, as well as their aggregated power profile. The methods are evaluated using standard KPIs for deterministic and probabilistic forecasts. We also compare the ability of different hierarchical techniques in improving the bottom level forecasters' performances. Both the raw and cleaned datasets, including meteorological data, are made publicly available to provide a standard benchmark for evaluating forecasting algorithms for demand-side management applications. The increasing monitoring capacity in low voltage (L V) and medium voltage (MV) distribution systems allows operators to gather power measurements from different levels of aggregation within the power grid. For instance, smart meters provide measurements from single households or buildings, dedicated power meters or phasor measurement units from secondary substations, and remote terminal units from primary substations at the interface between distribution and (sub)transmission systems. E.g., in a radial distribution system, the power flow at the grid connection point is, at the net of grid losses, the sum of the downstream elements. In the case of forecasts, however, the forecasted top-level series computed by using the information at that level of aggregation does not necessarily correspond to the sum of the bottom-level forecasts, thus invalidating the principle of hierarchy.

Scenario discovery is the process of finding areas of interest, commonly referred to as scenarios, in data spaces resulting from simulations. For instance, one might search for conditions - which are inputs of the simulation model - where the system under investigation is unstable. A commonly used algorithm for scenario discovery is PRIM. It yields scenarios in the form of hyper-rectangles which are human-comprehensible. When the simulation model has many inputs, and the simulations are computationally expensive, PRIM may not produce good results, given the affordable volume of data. So we propose a new procedure for scenario discovery - we train an intermediate statistical model which generalizes fast, and use it to label (a lot of) data for PRIM. We provide the statistical intuition behind our idea. Our experimental study shows that this method is much better than PRIM itself. Specifically, our method reduces the number of simulations runs necessary by 75% on average.

Creating accurate maps today is a painstaking, time-consuming manual process, even with access to satellite imagery and mapping software. Many regions -- particularly in the developing world -- remain largely unmapped. To help close this gap, Facebook AI researchers and engineers have developed a new method that uses deep learning and weakly supervised training to predict road networks from commercially available high-resolution satellite imagery. The resulting model sets a new bar for the state of the art for accuracy, and because it is able to accommodate regional differences in road networks, it can effectively predict roads around the globe. We are now sharing the details of our model and making data available to the global mapping community through Map With AI, a new set of specialized map-editing services and tools. Map With AI includes an editor interface, RapiD, which allows mapping experts to easily review, verify, and adjust the map as needed.

The following article is part of a series on Argonne National Laboratory's efforts to use the predictive power of artificial intelligence, specifically machine learning, to advance discoveries in a broad range of scientific disciplines. High-energy physics and cosmology seem worlds apart in terms of sheer scale, but the invisible components that comprise the field of one inform the composition and dynamics of the other -- collapsing stars, star-birthing nebulae and, perhaps, dark matter. For decades, the techniques by which researchers in both fields studied their domains seemed almost incompatible, as well. High-energy physics relied on accelerators and detectors to glean some insight from the energetic interactions of particles, while cosmologists gazed through all manner of telescopes to unveil the secrets of the universe. " … it would be interesting to know if image classification techniques from machine learning that have been used successfully by Google and Facebook can simplify or shorten the development of algorithms that identify particle signatures in our 3D detectors."

Two Department of Energy Computational Science Graduate Fellowship recipients are leading an effort to address global climate change effects with machine-learning techniques. Priya Donti, a third-year fellow in computer science and public policy at Carnegie Mellon University, and Kelly Kochanski, a fourth-year fellow in Earth surface processes at the University of Colorado Boulder, are on the steering committee (Donti is co-chair) for Climate Change AI. The group's website says it is a coalition of "volunteers from academia and industry who believe in using machine learning, where it is relevant, to help tackle the climate crisis." Machine learning algorithms identify patterns in known data and use that information to make predictions or to classify previously unseen data. Machine learning is a key component of artificial intelligence (AI).

Introduced by Conner Prochaska, Chief Commercialization Officer, U.S. Department of Energy This panel highlights the breadth of research in AI across the national laboratory complex and discusses how this research fits into the recently announced National AI Initiative; several notable collaborations in AI between companies and national labs are discussed. Applications of AI to solving core problems in energy grid optimization are discussed. In particular, AI approaches to energy grid security, flexibility, and reliability in light of future energy demands are highlighted. Panel participants include representatives from major utilities. State of applications of AI to transformative problems in drug discovery are discussed; ATOM Partnership is highlighted; future opportunities across disease areas are discussed with participants from life sciences companies.

CALGARY, Alberta--(BUSINESS WIRE)--The Alberta Machine Intelligence Institute (Amii) and Imperial have announced a two-year agreement to collaborate on the development of Imperial's in-house machine learning capabilities, which will enable a range of applied artificial intelligence (AI) projects. Through these projects, Imperial will work to develop more effective ways to recover oil and gas resources, reduce environmental impacts and improve the safety of its workforce. "At Imperial, we are taking action to be a leader in advancing digital and AI technology across the value chain," said John Whelan, Imperial's senior vice-president, upstream. "Amii is not only a leader in the AI space globally, but based locally in Alberta. We believe the institute is a perfect partner to help us showcase Alberta's leadership in technology and digital solutions for responsibly-produced oil and gas."

On 10th of June, 2019, twenty-two AI researchers, including Andrew Ng and Yoshua Bengio, published a paper on how climate change can be tackled with machine learning. I really enjoyed reading it and I am convinced that the paper as well as the climatechange.ai For that reason i created a series of blog posts and videos which provide a dense summary, listing many of the proposed solutions and linking research work as well as ongoing projects. In the big picture, all solutions aim to reduce greenhouse gas emissions. As my contribution to the global #ClimateStrike week from September 20th to 27th, i will post one chapter (video and blog post) on every working day.

Metron has developed an AI energy platform to help industrial factories become energy transparent. By harnessing multiple sources of data generated by industrial systems, METRON Energy Virtual Assistant leverages the latest machine learning capabilities and dedicated knowledge bases to proactively identify energy savings opportunities, connect to decentralized energy resources and turn energy into a profit centre.

PROVIDENCE, RHODE ISLAND – Employee activism and outside pressure have pushed big tech companies like Amazon, Microsoft and Google to make promises to slash their carbon emissions. When Microsoft held an all-staff meeting in September, an employee asked CEO Satya Nadella if it was ethical for the company to be selling its cloud computing services to fossil fuel companies, according to two other Microsoft employees who described the exchange on condition they not be named. Such partnerships, the worker told Nadella, were accelerating the oil companies' greenhouse gas emissions. Microsoft and other tech giants have been competing with one another to strike lucrative partnerships with ExxonMobil, Chevron, Shell, BP and other energy firms, in many cases supplying them not just with remote data storage but also artificial intelligence tools for pinpointing better drilling spots or speeding up refinery production. The oil and gas industry is spending roughly $20 billion each year on cloud services, which accounts for about 10 percent of the total cloud market, according to Vivek Chidambaram, a managing director of Accenture's energy consultancy.