Abstract
This paper proposes an hybrid approach for short term solar intensity forecasting, which combines different forecasting methodologies with a clustering algorithm, which plays the role of data filter, in order to support the selection of the best data for training. A set of methodologies based on Artificial Neural Networks (ANN) and Support Vector Machines (SVM), used for short term solar irradiance forecast, is implemented and compared in order to facilitate the selection of the most appropriate methods and respective parameters according to the available information and needs. Data from the Brazilian city of Florianopolis, in the state of Santa Catarina, has been used to illustrate the methods applicability and conclusions. The dataset comprises the years of 1990 to 1999 and includes four solar irradiance components as well as other meteorological variables, such as temperature, wind speed and humidity. Conclusions about the irradiance components, parameters and the proposed clustering mechanism are presented. The results are studied and analysed considering both efficiency and effectiveness of the results. The experimental findings show that the hybrid model, combining a SVM approach with a clustering mechanism, to filter the data used for training, achieved promising results, outperforming the approaches without clustering.

Abstract
The reliability and safety of industrial machines depends on their timely maintenance. The integration of Cyber Physical Systems within the maintenance process enables both continuous machine monitoring and the application of advanced techniques for predictive and proactive machine maintenance. The building blocks for this revolution-embedded sensors, efficient preprocessing capabilities, ubiquitous connection to the internet, cloud-based analysis of the data, prediction algorithms, and advanced visualization methods- A re already in place, but several hurdles have to be overcome to enable their application in real scenarios, namely: The integration with existing machines and existing maintenance processes. Current research and development efforts are building pilots and prototypes to demonstrate the feasibility and the merits of advanced maintenance techniques, and this paper describes a system for the industrial maintenance of sheet metal working machinery and its evolution towards a full proactive maintenance system. © 2017 IEEE.

Abstract
Introduction In this chapter, we present a number of applications of the Arrowhead Framework with special attention to services related to awareness and optimisation of energy consumption. First, we present the notion of FlexOffers as a general mechanism for describing energy flexibility. FlexOffers can be aggregated into larger flexibility units to be used as an Arrowhead service in the virtual market of energy [1]. This is followed by two examples on how to exploit such a flexibility service in the energy management of heat pumps and a campus building. Then we present two examples on how to exploit renewable energy to provide elevator services. Next, two examples of context aware services are described - smart lighting and smart car heating, and finally it is described how the Arrowhead Framework can play a role in the optimisation of municipal service systems. In the final section, we indicate future work. © 2017 by Taylor & Francis Group, LLC.

Abstract
Introduction In previous chapters local automation clouds and a SOA based architecture supporting the design and implementation of IoT based automation systems were discussed. This chapter is devoted to design and implementation of application services: •Design of an Arrowhead Framework system •Implementation of such a system and its services •Interoperability test 5.2 Application service design This section will discuss the design of an automation application system and associated services. For this purpose, we will make use of the simple control loop example addressing the level in a flotation tank. © 2017 by Taylor & Francis Group, LLC.

Abstract
Introduction In Chapter 2 local clouds were discussed followed by a local cloud automation architecture in Chapter 3. The automation architecture supports the implementation of local automation clouds. Such implementation is supported by the Arrowhead Framework and its core systems and services. © 2017 by Taylor & Francis Group, LLC.

Abstract
Embedded computers such as Raspberry Pi are gaining market as they offer considerable computation power on a flexible platform that can run different operating systems and user level libraries. There are a number of contributions on building middleware for connecting devices based on embedded computers in various ways; however, the temporal behavior of these systems has not been sufficiently covered, despite the fact that this is essential to validate the system design, operation, and timeliness that is needed in domains such as cyber-physical systems (CPS). This paper analyzes the temporal behavior of the connection among embedded computers and servers in the context of time sensitive deployments where some nodes can be virtualized offering mixed criticality execution platforms. We provide a scheme for using the Data Distribution Service standard to connect embedded computers based on Raspberry Pi and servers to analyze the temporal response stability.