In this paper a new method for heat load prediction in district energy systems is proposed. The method uses a nominal model for the prediction of the outdoor temperature dependent space heating load, and a data driven latent variable model to predict the time dependent residual heat load. The residual heat load arises mainly from time dependent operation of space heating and ventilation, and domestic hot water production. The resulting model is recursively updated on the basis of a hyper-parameter free implementation that results in a parsimonious model allowing for high computational performance. The approach is applied to a single multi-dwelling building in Lulea, Sweden, predicting the heat load using a relatively small number of model parameters and easily obtained measurements. The results are compared with predictions using an artificial neural network, showing that the proposed method achieves better prediction accuracy for the validation case. Additionally, the proposed methods exhibits explainable behavior through the use of an interpretable physical model.

Much work on social media opinion polarization focuses on identifying separate or orthogonal beliefs from media traces, thereby missing points of agreement among different communities. This paper develops a new class of Non-negative Matrix Factorization (NMF) algorithms that allow identification of both agreement and disagreement points when beliefs of different communities partially overlap. Specifically, we propose a novel Belief Structured Matrix Factorization algorithm (BSMF) to identify partially overlapping beliefs in polarized public social media. BSMF is totally unsupervised and considers three types of information: (i) who posted which opinion, (ii) keyword-level message similarity, and (iii) empirically observed social dependency graphs (e.g., retweet graphs), to improve belief separation. In the space of unsupervised belief separation algorithms, the emphasis was mostly given to the problem of identifying disjoint (e.g., conflicting) beliefs. The case when individuals with different beliefs agree on some subset of points was less explored. We observe that social beliefs overlap even in polarized scenarios. Our proposed unsupervised algorithm captures both the latent belief intersections and dissimilarities. We discuss the properties of the algorithm and conduct extensive experiments on both synthetic data and real-world datasets. The results show that our model outperforms all compared baselines by a great margin.

Meet Spot, the first robot to get its own employee number at Norwegian oil producer Aker BP. Developed by Boston Dynamics, the robot is set to start patrolling Aker BP's oil and gas production vessel at the Skarv field in the Norwegian Sea this year, testing its ability to run inspections, detect hydrocarbon leaks, gather data and generate reports. The upshot for Aker BP, which is seeking to be a front-runner in the digitalization of the oil industry, is to make offshore operations safer and more efficient, the company said as it presented the robot at its capital markets day in Oslo on Tuesday. Aker BP will run the tests with Cognite, the software venture controlled by the oil company's main owner, Aker ASA. "These things never get tired, they have a larger ability to adapt and to gather data," Kjetel Digre, Aker BP's senior vice president for operations, said in an interview.

Everyone seems to be discussing machine learning and artificial intelligence, but few people really know how to take advantage of the new technology. Did you know that according to the MIT Sloan Management Review 76% of companies say they're using machine learning to increase their sales growth? So why does almost every BI and analytics professional I talk to think that machine learning is the domain of a few statisticians or data scientists trained to use algorithms or advanced analytics technologies? Make no mistake, machine learning, artificial intelligence, and the newer offshoot deep learning are complex topics, but you don't need to understand them in depth to take advantage of them. In fact, your analytics strategy should focus on these technologies.

China's best known AI companies are Sensetime, Megvii, Cloudwalk, Yitu, ByteDance, and the BAT companies -- China's first generation of internet giant: Baidu, Alibaba and Tencent. But there's another giant of artificial intelligence that is rarely discussed in the same breath as the companies mentioned above. The state-owned electric utility monopoly State Grid Corporation of China (hereafter State Grid) is the largest utility company in the world, ranking second on the 2018 Fortune Global 500 List. Less celebrated is that State Grid was the only Chinese company ranked in the top 20 in artificial intelligence (AI) patent applicants, per the World Intellectual Property Organization. In an article (in Chinese) published last year titled "State Grid Corporation of China: A hidden giant in AI," Lǐ Shāng 李熵 gives a portrait of a company whose AI initiatives could change the world.

The Gaussian process (GP) model, which has been extensively applied as priors of functions, has demonstrated excellent performance. The specification of a large number of parameters affects the computational efficiency and the feasibility of implementation of a control strategy. We propose a linear model to approximate GPs; this model expands the GP model by a series of basis functions. Several examples and simulation studies are presented to demonstrate the advantages of the proposed method. A control strategy is provided with the proposed linear model. Keywords: Data mining, forecasting, stochastic processes, control strategies INTRODUCTION The Gaussian process (GP) is a powerful modeling tool that has many applications in research and practice. It provides a practical and probabilistic approach to learning in kernel machines. The GP is extensively applied as a prior of a true function.

The Norwegian oil company Aker BP ASA has announced it will bring aboard the infamous robotic watchdog Spot on the company's ships in the Skarv region of the Norwegian Sea. According to Aker, Spot will be charged with sniffing out hydrocarbon leaks, inspecting ship equipment, taking mechanical readings, generating reports, and completing inspections in areas that might be too dangerous for human workers. Spot was developed by the Massachusetts-based robotics company Boston Dynamics, which specializes in developing autonomous and humanoid machines. The Norwegian oil company Aker BP ASA announced it will begin using Boston Dynamics' robotic watchdog on Spot (pictured above) to help monitor equipment on its ships in the Norwegian Sea'These things never get tired, they have a larger ability to adapt and to gather data,' Aker BP ASA's Kjetel Digre told Bloomberg. The announcement is part of the Aker's new emphasis on'digitalization,' which it hopes will make its ships safer and more productive.

Demand flexibility can be described as the capacity for end users of electricity (think both business and homes) to change their electricity consumption patterns in response to market signals, such as time variable electricity prices. Electricity prices follow daily, weekly and seasonal patterns. Above we can see the daily pattern. In the morning, everyone wakes up, turns all their devices on and prices rise. As the population goes off to work, demand and prices fall (and solar generation comes online).

A fleet of 11 uncrewed vessels will traverse the world's oceans over the next ten years in a bid to map the sea floor. The bottom of the world's oceans remains a mystery, with around 80 per cent either poorly imaged or not visualised at all. Ocean Infinity launched in 2016 and has pledged its support to an international collaboration to try and map every inch of the ocean floor within the next decade. It has also attempted to use its technology to try and locate the missing Malaysian Airlines MH370 flight that tragically went missing with 239 people on board nearly six years ago. It has announced it has bought a fleet of 11 uncrewed vessels will traverse the world's oceans over the next ten years in a bid to map the sea floor Uncrewed Surface Vessels (USV) are the latest technology which open up the possibility for long-term marine missions. They have no humans on board and are controlled by computers via a satellite link and a central computer base.

After Hurricane Irma tore through South Florida in 2017 and cut power to more than six million people, it took 10 days for Florida Power & Light --the state's largest electrical utility--to get the lights back on. That was a big improvement from 2005, when recovery from Hurricane Wilma took 18 days. Investments in technology paid off. Now, FPL is looking to reduce recovery time further, by harnessing artificial intelligence, sensors and drones to better pinpoint outages and decide how best to fix them. "After a storm, we want data," says Michael Putt, the company's smart grid and innovation director.