In this article we present an approach that enables joint wind speed and wind power forecasts for a wind park. We combine a multivariate seasonal time varying threshold autoregressive moving average (TVARMA) model with a power threshold generalized autoregressive conditional heteroscedastic (power-TGARCH) model. The modeling framework incorporates diurnal and annual periodicity modeling by periodic B-splines, conditional heteroscedasticity and a complex autoregressive structure with nonlinear impacts. In contrast to usually time-consuming estimation approaches as likelihood estimation, we apply a high-dimensional shrinkage technique. We utilize an iteratively re-weighted least absolute shrinkage and selection operator (lasso) technique. It allows for conditional heteroscedasticity, provides fast computing times and guarantees a parsimonious and regularized specification, even though the parameter space may be vast. We are able to show that our approach provides accurate forecasts of wind power at a turbine-specific level for forecasting horizons of up to 48 hours (short-to medium-term forecasts).

In this paper we propose a framework for automated forecasting of energy-related time series using open access data from European Network of Transmission System Operators for Electricity (ENTSO-E). The framework provides forecasts for various European countries using publicly available historical data only. Our solution was benchmarked using the actual load data and the country provided estimates (where available). We conclude that the proposed system can produce timely forecasts with comparable prediction accuracy in a number of cases. We also investigate the probabilistic case of forecasting - that is, providing a probability distribution rather than a simple point forecast - and incorporate it into a web based API that provides quick and easy access to reliable forecasts.

Why do we get nervous when we think about robots working among us instead of tethered to the factory floor? We're already dependent on hundreds if not thousands of automated systems and processes. We fly on planes that fly themselves. Our electrical grid can redirect itself to avoid power outages. We expect these systems to be reliable and safe, and to do what they've been programmed to do and nothing more.

When Sputnik 1 reached low Earth orbit in 1957, it did more than kick-start America's space program and send American schoolchildren scurrying for cover under their desks. It launched the satellite age. The orbiting platforms, which now number in the thousands, revolutionized communication, navigation, and watching football. Satellites, though, are expensive to build, expensive to launch, and difficult to update once in orbit. They're relatively cheap, easily launched, and readily updated.

Black-box alpha (BB-$\alpha$) is a new approximate inference method based on the minimization of $\alpha$-divergences. BB-$\alpha$ scales to large datasets because it can be implemented using stochastic gradient descent. BB-$\alpha$ can be applied to complex probabilistic models with little effort since it only requires as input the likelihood function and its gradients. These gradients can be easily obtained using automatic differentiation. By changing the divergence parameter $\alpha$, the method is able to interpolate between variational Bayes (VB) ($\alpha \rightarrow 0$) and an algorithm similar to expectation propagation (EP) ($\alpha = 1$). Experiments on probit regression and neural network regression and classification problems show that BB-$\alpha$ with non-standard settings of $\alpha$, such as $\alpha = 0.5$, usually produces better predictions than with $\alpha \rightarrow 0$ (VB) or $\alpha = 1$ (EP).

TechCrunch is pleased to bring you Microsoft Accelerator's Machine Learning Demo Day this Thursday, June 2 from the Showbox SoDo in Seattle. The Microsoft Accelerator is an immersive three- to six-month program aimed at helping entrepreneurs get through the challenges of building a company, finding customers and scaling to global markets. There are seven accelerators located around the world, from Bangalore to Beijing, from Berlin to Tel-Aviv. While most of their programs have a focus on enterprise startups, this demo day in Seattle is for companies specifically leveraging machine learning. Investors and press will hear pitches from nine companies solving problems ranging from natural gas pipelines to on-demand medicine.

Classical collaborative filtering, and content-based filtering methods try to learn a static recommendation model given training data. These approaches are far from ideal in highly dynamic recommendation domains such as news recommendation and computational advertisement, where the set of items and users is very fluid. In this work, we investigate an adaptive clustering technique for content recommendation based on exploration-exploitation strategies in contextual multi-armed bandit settings. Our algorithm takes into account the collaborative effects that arise due to the interaction of the users with the items, by dynamically grouping users based on the items under consideration and, at the same time, grouping items based on the similarity of the clusterings induced over the users. The resulting algorithm thus takes advantage of preference patterns in the data in a way akin to collaborative filtering methods. We provide an empirical analysis on medium-size real-world datasets, showing scalability and increased prediction performance (as measured by click-through rate) over state-of-the-art methods for clustering bandits. We also provide a regret analysis within a standard linear stochastic noise setting.

The latest robotic cockroach can jump more than five feet in the air, and flip itself over to continue scurrying. Using a new method for storing energy and a height-adjustable trigger, the robo-roach can achieve more ground than those which rely solely on crawling. Though the enhanced jumping capabilities have been built into a small package for the project, the concept has potential to be scaled up for much larger robotics systems. The bug crawls across a desk before opening its'wings' to jump high in the air. The JumpRoACH features a height-adjustable trigger, allowing it to jump between 1.1 and 1.62 meters (3.6 – 5.2 feet) JumpRoACH has six feet for crawling and can move at a speed of up to .62 meters (2 feet) per second.

But whether one is optimistic or pessimistic, or feels prepared or not, a revolution is inevitable. And indeed, the fourth industrial revolution appears to be upon us. With the advent of 5G mobile Internet, smaller and more powerful sensors, artificial intelligence and machine learning, this revolution will be as transformational, if not more so, as anything mankind has experienced before. It will change the way we live, work and relate to each other. Artificial intelligence, robotics, the Internet of Things, 3D printing, nanotechnology, biotechnology, renewable energy, and quantum computing: such advances are transforming the world faster than we realise.

Motivated by a real-world problem, we study a novel budgeted optimization problem where the goal is to optimize an unknown function f(.) given a budget by requesting a sequence of samples from the function. In our setting, however, evaluating the function at precisely specified points is not practically possible due to prohibitive costs. Instead, we can only request constrained experiments. A constrained experiment, denoted by Q, specifies a subset of the input space for the experimenter to sample the function from. The outcome of Q includes a sampled experiment x, and its function output f(x). Importantly, as the constraints of Q become looser, the cost of fulfilling the request decreases, but the uncertainty about the location x increases. Our goal is to manage this trade-off by selecting a set of constrained experiments that best optimize f(.) within the budget. We study this problem in two different settings, the non-sequential (or batch) setting where a set of constrained experiments is selected at once, and the sequential setting where experiments are selected one at a time. We evaluate our proposed methods for both settings using synthetic and real functions. The experimental results demonstrate the efficacy of the proposed methods.