Understanding the heat use of customers is crucial for effective district heating (DH) operations and management. Unfortunately, existing knowledge about customers and their heat load behaviors is quite scarce and very few studies have been focusing on this aspect. The deployment of smart meters offers a unique opportunity for researchers and DH utilities to analyze large-scale data and discover both typical, as well as atypical, patterns in the network. Heat load pattern discovery is a challenging task in DH systems, since a comprehensive analysis needs to involve many customers. Most of the past studies have relied on analysis of a small number of buildings, which are not shown to be picked as the representative examples. Therefore, the knowledge discovered in such studies is not enough to generalize for the entire network. In this work, we propose a data-driven approach that enables automatic discovery of heat load patterns in a complete district heating network. Our method clusters the buildings into different groups based on the characteristics of their load profiles, extracts the representative patterns for each of them, and detects abnormal profiles, i.e., the ones deviating from the expected behavior. We present the first comprehensive analysis of the heat load patterns by conducting a case study on all the buildings, in six customer categories, connected to two district heating networks in the south of Sweden. Our method has captured fifteen typical patterns among the heat load profiles of all buildings in our dataset. It shows that control strategies are not enough to explain the variability in the heat load behaviors. In conclusion, we demonstrate that the proposed approach has a great potential to develop knowledge about customers and their heat use habits in practice by automatically analyzing their typical and atypical profiles in large-scale.

The field of probabilistic numerics (PN), loosely speaking, attempts to provide a statistical treatment of the errors and/or approximations that are made en route to the output of a deterministic numerical method, e.g. the approximation of an integral by quadrature, or the discretised solution of an ordinary or partial differential equation. This decade has seen a surge of activity in this field. In comparison with historical developments that can be traced back over more than a hundred years, the most recent developments are particularly interesting because they have been characterised by simultaneous input from multiple scientific disciplines: mathematics, statistics, machine learning, and computer science. The field has, therefore, advanced on a broad front, with contributions ranging from the building of overarching generaltheory to practical implementations in specific problems of interest. Over the same period of time, and because of increased interaction among researchers coming from different communities, the extent to which these developments were -- or were not -- presaged by twentieth-century researchers has also come to be better appreciated. Thus, the time appears to be ripe for an update of the 2014 Tübingen Manifesto on probabilistic numerics[Hennig, 2014, Osborne, 2014d,c,b,a] and the position paper[Hennig et al., 2015] to take account of the developments between 2014 and 2019, an improved awareness of the history of this field, and a clearer sense of its future directions. In this article, we aim to summarise some of the history of probabilistic perspectives on numerics (Section 2), to place more recent developments into context (Section 3), and to articulate a vision for future research in, and use of, probabilistic numerics (Section 4).

A Portland, Oregon-based company named Global Energy Transmission (GET) is developing a network of wireless charging hotspots for drones. With only six minutes hovering over a grid for a full charge, an electric industrial class drone can repeat the cycle of charging and flying until its battery is drained without ever having to land or connect to a cable using this technology. GET's long-term vision includes a cell-tower like infrastructure comprising numerous charging stations, enabling indefinite flying time for drones in the network. If successful, this technology could reinvent the commercial drone industry, providing 24/7 solutions in dedicated areas for things like deliveries, monitoring, and security. Per GET's website, the drone built for the charging network technology weighs about 18 lbs without the battery, can carry about 15 lbs, and can fly for 28 minutes weighing 30 lbs at takeoff.

The analysis of sensor data of machines, plants or buildings makes it possible to detect anomalous states early and thus to avoid further damage. For this purpose, the monitoring data is searched for anomalies. By means of machine learning, anomaly detection can already be partially automated. Machine learning methods first require a stable learning phase in which they get to know all possible kinds of regular states. For wind turbines or bridges, this is only possible to a very limited extent, as they are, for example, exposed to highly fluctuating weather conditions.

In a 2017 article for Foreign Affairs, Kassia Yanosek and I advanced the hypothesis that the biggest impacts of the information technology (IT) revolution may be felt far outside IT--in the traditional industries of oil, gas, and electricity.1 That's because IT was transforming how those industries function. That logic of transformation may be especially profound when looking at a subset of the IT revolution: artificial intelligence (AI). Other essays in this series explain what's happening with AI and why it is such an important technical revolution.2 In this essay, I'll look at how AI might be affecting the supply and demand for energy and the implications of AI for how modern society uses energy: climate change. In a nutshell, the message is that AI helps make markets more efficient and easier for analysts and market participants to understand highly complex phenomena--from the behavior of electrical power grids to climate change.

We demonstrate high-accuracy tuning of poly(2-oxazoline) cloud point via machine learning. With a design space of four repeating units and a range of molecular masses, we achieve an accuracy of 4 C root mean squared error (RMSE) in a temperature range of 24– 90 C, employing gradient boosting with decision trees. The RMSE is 3x better than linear and polynomial regression. We perform inverse design via particle-swarm optimization, predicting and synthesizing 17 polymers with constrained design at 4 target cloud points from 37 to 80 C. Our approach challenges the status quo in polymer design with a machine learning algorithm, that is capable of fast and systematic discovery of new polymers.

This paper proposes a novel adaptive guidance system developed using reinforcement meta-learning with a recurrent policy and value function approximator. The use of recurrent network layers allows the deployed policy to adapt real time to environmental forces acting on the agent. We compare the performance of the DR/DV guidance law, an RL agent with a non-recurrent policy, and an RL agent with a recurrent policy in four difficult tasks with unknown but highly variable dynamics. These tasks include a safe Mars landing with random engine failure and a landing on an asteroid with unknown environmental dynamics. We also demonstrate the ability of a recurrent policy to navigate using only Doppler radar altimeter returns, thus integrating guidance and navigation. INTRODUCTION Many space missions take place in environments with complex and time-varying dynamics that may be incompletely modeled during the mission design phase. For example, during an orbital refueling mission, the inertia tensor of each of the two spacecraft will change significantly as fuel is transferred from one spacecraft to the other, which can make the combined system difficult to control. The wet mass of an exoatmospheric kill vehicles (EKV) consists largely of fuel, and as this is depleted with divert thrusts, the center of mass changes, and the divert thrusts are no longer orthogonal to the EKV's velocity vector, which wastes fuel and impacts performance. Future missions to asteroids might be undertaken before the asteroid's gravitational field, rotational velocity, and local solar radiation pressure are accurately modeled.

Machine Learning helps make complex systems more efficient. By applying advanced Machine Learning techniques such as Cognitive Fingerprinting, wind project operators can utilize these tools to learn from collected data, detect regular patterns, and optimize their own operations. In his session at 18th Cloud Expo, Stuart Gillen, Director of Business Development at SparkCognition, discussed how research has demonstrated the value of Machine Learning in delivering next generation analytics to improve safety, performance, and reliability in today's modern wind turbines. Speaker Bio Stuart Gillen is the Director of Business Development at SparkCognition. In this role, he is responsible for driving business engagements, partner development, marketing activities, and go-to market strategy.

For every business, data science is the foundation of enabling a successful transformation into an AI-powered enterprise. Streamlining the data science workflow is essential to ensuring that organizations can mine oceans of data for valuable insights and predictions that can power business. Unfortunately, today's enterprise machine learning is built on a legacy architecture that was never designed for the unique demands of ingesting, preparing and ultimately training ML algorithms with speed and efficiency - attributes which are native to the world of GPUs. Data science talent is hard to find, and maybe harder to retain if they're not adequately equipped to do their best work, with the best tools available. If your valued innovators are spending an appreciable amount of their day waiting on ingesting a CSV file, data analysis, data preparation or training a model, they're likely bored or taking too many coffee breaks while they wait.

A new partnership between the UK and Japan will see medical researchers and scientists join forces, in advancing research into chronic conditions - such as diabetes, heart disease and arthritis - green technology and AI. The collaboration, announced by British Prime Minister Theresa May, Business Secretary Greg Clark and Japanese Prime Minister Shinzo Abe, will see £30 million invested into a new partnership aimed at promoting technology and innovation in both Britain and Japan. The partnership includes a £10 million programme led by the UK's Medical Research Council (MRC) and Japan's Agency for Medical Research and Development (AMED) that will advance regenerative medicine. Greg Clark commented: "The UK and Japan are home to some of the most innovative businesses in the world, and we share the same fundamental belief in the power of enterprise to improve the lives of our citizens. This government wants to give older people at least five extra healthy independent years of life by 2035."