Abstract
Power and energy systems are very complex, and several tools are available to assist operators in their planning and operation. However, these tools do not allow a sensitive analysis of the impact of the interaction between the different sub-domains and, consequently, in obtaining more realistic and reliable results. One of the key challenges in this area is the development of decision support tools to address the problem as a whole. Tools Control Center & x2013; TOOCC & x2013; proposed and developed by the authors, enables the co-simulation of heterogeneous systems to study the electricity markets, the operation of the smart grids, and the energy management of the final consumer, among others. To this end, it uses an application ontology that supports the definition of scenarios and results comparison, while easing the interoperability among the several systems. This paper presents the application ontology developed. The paper addresses the methodology used for its development, its purpose and requirements, and its concepts, relations, facets and instances. The ontology application is illustrated through a case study, where different requirements are tested and demonstrated. It is concluded that the proposed application ontology accomplishes its goals, as it is suitable to represent the required knowledge to support the interoperability among the different considered systems.

Abstract
This paper explores the concept of the Local Energy Market and, in particular, the need for Trust in the negotiations necessary for this type of market. A multi-agent system is implemented to simulate the Local Energy Market, and a Trust model is proposed to evaluate the proposals sent by the participants, based on forecasting mechanisms that try to predict the expected behavior of the participant. A case study is carried out with several participants who submit false negotiation proposals to assess the ability of the proposed Trust model to correctly evaluate these participants. The results obtained demonstrate that such an approach has the potential to meet the needs of the local market. © Springer Nature Switzerland AG 2020.

Abstract
The increase of renewable energy sources of intermittent nature has brought several new challenges for power and energy systems. In order to deal with the variability from the generation side, there is the need to balance it by managing consumption appropriately. Forecasting energy consumption becomes, therefore, more relevant than ever. This paper presents and compares three different ensemble learning methods, namely random forests, gradient boosted regression trees and Adaboost. Hour-ahead electricity load forecasts are presented for the building N of GECAD at ISEP campus. The performance of the forecasting models is assessed, and results show that the Adaboost model is superior to the other considered models for the one-hour ahead forecasts. The results of this study compared to previous works indicates that ensemble learning methods are a viable choice for short-term load forecast. © 2020, Springer Nature Switzerland AG.

Abstract
This research proposes a novel methodology for adaptive learning in electricity markets negotiations, based on the principles of the determinism theory. The determinism theory states that all events are predetermined due to the cause-effect rule. At the same time, it is unmanageable to consider all causes to a certain effect, making it impossible to predict future events. However, in a controlled simulation environment, it is possible to access and analyze all involved variables; thus, making the application of this theory promising in such environments. This research applies the principles of the determinism theory to a new learning methodology, which optimizes players' actions, considering the predicted behavior of all involved players, with the objective of maximizing market gains. A case-based reasoning approach is used, providing adaptive context-aware decision support. Results show that the proposed approach is able to achieve better market results than all reference market strategies.

Abstract
This paper discusses the integration of emergent ICTs, such as the Internet of Things (IoT), the Arrowhead Framework, and the best practices from the area of condition monitoring and maintenance. These technologies are applied, for instance, for roller element bearing fault diagnostics and analysis by simulating faults. The authors first undertook the leading industry standards for condition-based maintenance (CBM), i.e., open system architecture-condition-based maintenance (OSA-CBM) and Machinery Information Management Open System Alliance (MIMOSA), which has been working towards standardizing the integration and interchangeability between systems. In addition, this paper highlights the predictive health monitoring methods that are needed for an effective CBM approach. The monitoring of industrial machines is discussed as well as the necessary details are provided regarding a demonstrator built on a metal sheet bending machine of the Greenbender family. Lastly, the authors discuss the benefits of the integration of the developed prototypes into a service-oriented platform, namely the Arrowhead Framework, which can be instrumental for the remotization of maintenance activities, such as the analysis of various equipment that are geographically distributed, to push forward the grand vision of the servitization of predictive health monitoring methods for large-scale interoperability.

Abstract
Collecting complex information on the status of machinery is the enabler for advanced maintenance activities, and one of the main players in this process is the sensor. This paper describes modern maintenance strategies that lead to Condition-Based Maintenance. This paper discusses the sensors that can be used to support maintenance, as of different categories, spanning from common off-the-shelf sensors, to specialized sensors monitoring very specific characteristics, and to virtual sensors. This paper also presents four different real-world examples of project pilots that make use of the described sensors and draws a comparison between them. In particular, each scenario has unique characteristics requiring different families of sensors, but on the other hand provides similar characteristics on other aspects.