Big data is big news, and large companies in all sectors are making significant advances in their customer relations, product selection and development and consequent profitability through using this valuable commodity. Small and medium enterprises (SMEs) have proved themselves to be slow adopters of the new technology of big data analytics and are in danger of being left behind. In Europe, SMEs are a vital part of the economy, and the challenges they encounter need to be addressed as a matter of urgency. This paper identifies barriers to SME uptake of big data analytics and recognises their complex challenge to all stakeholders, including national and international policy makers, IT, business management and data science communities.The paper proposes a big data maturity model for SMEs as a first step towards an SME roadmap to data analytics. It considers the ‘state‐of‐the‐art’ of IT with respect to usability and usefulness for SMEs and discusses how SMEs can overcome the barriers preventing them from adopting existing solutions. The paper then considers management perspectives and the role of maturity models in enhancing and structuring the adoption of data analytics in an organisation. The history of total quality management is reviewed to inform the core aspects of implanting a new paradigm. The paper concludes with recommendations to help SMEs develop their big data capability and enable them to continue as the engines of European industrial and business success. Copyright © 2016 John Wiley & Sons, Ltd.

During overloads in the mains, the load curtailment applied to interruptible loads is often the only solution to keep the network in operation. Normally, in contingencies, the difference between the power absorbed and the power produced is very low, often less than 1% of the latter. Therefore if all the loads participated in the load shedding program, the discomfort would be minimal, considering its usually short duration. According to this point of view, we present a new approach to the load shedding program to guarantee the correct electrical system operation by increasing the number of participants. This new load control strategy is named Distributed Interruptible Load Shedding (DILS). Indeed, it is possible to split every user's load into interruptible and uninterruptible parts, and to operate on the interruptible part only. The optimal load reduction request is found by minimizing the expected value of an appropriate cost function, thus taking the uncertainty about the power absorbed by each customer into account. Presently, several users such as hospitals, data centres, supermarkets, universities, industries, etc. might be very interested in typical shedding programs as a way to spare money in their electrical account. However, in the future, when the domotic power plants are likely to be used widely, the distributors could interest the end users in participating in DILS programs for either economic or social reasons. By adopting the DILS program, the distributors can resort to the interruptible loads not only in case of emergency conditions but also during normal and alert operations.

The paper presents a method based on Markov decision processes to optimally schedule energy storage devices in power distribution networks with renewable generation. The time series of renewable generation is modeled as a Markov chain which allows for the implementation of a stochastic dynamic programming algorithm. The output of this algorithm is an optimal scheduling policy for the storage device achieving the minimization of an objective function including cost of energy and network losses. Besides this, other properties, such as energy storage placement and size, can be assessed and compared in optimized systems with different layouts.

We present a novel decision-making framework for accelerated degradation tests and predictive maintenance that exploits prior knowledge and experimental data on the system’s state. As a framework for sequential decision making in these areas, dynamic programming and reinforcement learning are considered, along with data-driven degradation learning when necessary. Furthermore, we illustrate both stochastic and machine learning degradation models, which are integrated in the framework, using data-driven methods. These methods are presented as a valuable tool for designing life-testing experiments and for maintaining lithium-ion batteries.