Forecasting: theory and practice

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases

Large-scale unit commitment under uncertainty: an updated literature survey

The Unit Commitment problem in energy management aims at finding the optimal production schedule of a set of generation units, while meeting various system-wide constraints. It has always been a large-scale, non-convex, difficult problem, especially in view of the fact that, due to operational requirements, it has to be solved in an unreasonably small time for its size. Recently, growing renewable energy shares have strongly increased the level of uncertainty in the system, making the (ideal) Unit Commitment model a large-scale, non-convex and uncertain (stochastic, robust, chance-constrained) program. We provide a survey of the literature on methods for the Uncertain Unit Commitment problem, in all its variants. We start with a review of the main contributions on solution methods for the deterministic versions of the problem, focussing on those based on mathematical programming techniques that are more relevant for the uncertain versions of the problem. We then present and categorize the approaches to the latter, while providing entry points to the relevant literature on optimization under uncertainty. This is an updated version of the paper "Large-scale Unit Commitment under uncertainty: a literature survey" that appeared in 4OR 13(2), 115--171 (2015); this version has over 170 more citations, most of which appeared in the last three years, proving how fast the literature on uncertain Unit Commitment evolves, and therefore the interest in this subject

Closing the Lifecycle Loop with Installed Base Products

Part 5: Education in the Field of Industry 4.0International audienceIndustry trends indicate that in the future, more systems will be rented then sold. The customer rents production capacity and demands a guaranteed high operational readiness, which is hard to achieve with conventional maintenance. Downtimes can never be completely ruled out. In order to solve this problem and guarantee high operational reliability, the predictive maintenance approach is widely discussed: By means of indicators, measured by sensors, a potential problem can be identified before it actually occurs. The application of this concept to new products gets a lot of attention in many areas. However, industrial products such as machines or plants are long living objects. It seems interesting to extend these new technologies and eventually new services business models to the installed base, too.This paper explores and demonstrates, what it takes to upgrade an operating product in its mid-of-life stage to a smart, connected products with predictive maintenance capabilities. The showcase consists of a jointed-arm industrial robot with six axes. The robot’s motions will be retraced in order to determine the state and position of the robot and finally predict, what the robot is about to do. To achieve this, the robot was made IoT-capable by attachment of sensors which communicate directly to a cloud database. Finally, a trained machine learning model allows predication on the robots’ behavior. On the way to the final result, many little lessons about sensing, protocols, the right place to process or tag data in the IoT stack had to learnt and will be shared in this publication

Smart Farming: Intelligent Management Approach for Crop Inspection and Evaluation Employing Unmanned Aerial Vehicles

This work presents an unmanned aerial vehicle management platform encompassed in the concept of smart farming. Automates inspections of different crops and monitors the status of the plantation is done by IoT, analyzing an area on an online map that provides air and weather restrictions. Intelligent route management algorithms are employed to generate the optimal inspection route and waypoints, maximizing the multispectral images capture. These multispectral images can be subsequently processed according to algorithms based on phytosanitary index formulas and regressions obtained with artificial neural networks. Reports are generated with analysis of the results by this approach, for example: optimal collection time, water stress, maturity index, etc.Dirección General de Universidades, Investigación e Innovación of Castilla-La Mancha, under Research Grant (Ref.: SBPLY/19/180501/000102).No data JCR 20190.184 SJR (2019), Q3 242/393 Computer Science (miscellaneous)No data IDR 2019UE