Abstract
This paper proposes a novel case-based reasoning (CBR) recommender system for intelligent energy management in buildings. The proposed approach recommends the amount of energy reduction that should be applied in a building in each moment, by learning from previous similar cases. The k-nearest neighbor clustering algorithm is applied to identify the most similar past cases, and an approach based on support vector machines is used to optimize the weight of different parameters that characterize each case. An expert system composed by a set of ad hoc rules guarantees that the solution is adequate and applicable to the new case scenario. The proposed CBR methodology is modeled through a dedicated software agent, thus enabling its integration in a multi-agent systems society for the study of energy systems. Results show that the proposed approach is able to provide suitable recommendations on energy reduction, by comparing its results with a previous approach based on particle swarm optimization and with the real reduction in past cases. The applicability of the proposed approach in real scenarios is also assessed through the application of the results provided by the proposed approach on a house energy resources management system.

Abstract
Increasing penetration of distributed energy resources in power distribution systems and appearing the flexible behavior of end-users based on demand response programs make the distribution layer of the power systems more active. In this way, energy transaction management through a decentralized manner could be an appropriate solution to improve the efficiency of energy trading in the distribution power networks. This paper proposes a decentralized method to manage energy flexibility by consumers based on a bottom-up approach in distributed power systems. Also, a 33-bus distribution network is considered to assess the performance of our proposed decentralized energy flexibility management model based on impacts of flexible behaviors of end-user and uncertainty of distribution lines to flow energy in the power network.

Abstract
Triggered by the increased fluctuations of renewable energy sources, the European Commission stated the need for integrated short-term energy markets (e.g., intraday), and recognized the facilitating role that local energy communities could play. In particular, microgrids and energy communities are expected to play a crucial part in guaranteeing the balance between generation and consumption on a local level. Local energy markets empower small players and provide a stepping stone toward fully transactive energy systems. In this paper, we evaluate such a fully integrated transactive system by, first, modeling the energy resource management problem of a microgrid under uncertainty considering flexible loads and market participation (solved via two-stage stochastic programming), second, modeling a wholesale market and a local market, and, third, coupling these elements into an integrated transactive energy simulation. Results under a realistic case study (varying prices and competitiveness of local markets) show the effectiveness of the transactive system resulting in a reduction of up to 75% of the expected costs when local markets and flexibility are considered. This illustrates that how local markets can facilitate the trade of energy, thereby increasing the tolerable penetration of renewable resources and facilitating the energy transition.

Abstract
The environmental impact and the scarcity of limited fossil fuels led to the need of investment in energy based on renewable sources. This has driven Europe to implement several policies that changed the energy market's paradigm, namely the incentive to microgeneration. The penetration of energy sources from intermittent nature has increased the unpredictability of the system, which makes simulation and analysis tools essential in order to provide decision support to entities in this sector. This paper presents the Tools Control Center (TOOCC) as a solution to increase the interoperability between heterogeneous agent-based systems, in the energy field. The proposed approach acts as a facilitator in the interaction between different systems through the usage of ontologies, allowing them to communicate in the same language. To understand the real applicability of this tool, a case study is presented concerning the interaction between several systems, with the purpose of enabling the energy resource scheduling of a microgrid, and the reaction of a house managed by a house management system.

Abstract
This paper proposes a predictive dispatch model to manage energy flexibility in the domestic energy system. Electric Vehicles (EV), batteries and shiftable loads are devices that provide energy flexibility in the proposed system. The proposed energy management problem consists of two stages: day-Ahead and real time. A hybrid method is defined for the first time in this paper to model the uncertainty of the PV power generation based on its power prediction. In the day-Ahead stage, the uncertainty is modeled by interval bands. On the other hand, the uncertainty of PV power generation is modeled through a stochastic scenario-based method in the real-Time stage. The performance of the proposed hybrid Interval-Stochastic (InterStoch) method is compared with the Modified Stochastic Predicted Band (MSPB) method. Moreover, the impacts of energy flexibility and the demand response program on the expected profit and transacted electrical energy of the system are assessed in the case study presented in this paper.

Abstract
In recent years, we have been witnessing a real explosion of information, due in large part to the development in Information and Knowledge Technologies (ICTs). As in-formation is the raw material for the discovery of knowledge, there has been a rapid growth, both in the scientific community and in ICT itself, in the approach and study of the phenomenon called Big Data (BD) [1]. The concept of Smart Grids (SG) has emerged as a way of rethinking how to produce and consume energy imposed by economic, political and ecological issues [2]. To become a reality, SGs must be sup-ported by intelligent and autonomous IT systems, to make the right decisions in real time. Knowledge needed for real-time decision-making can only be achieved if SGs are equipped with systems capable of efficiently managing all the information sur-rounding their ecosystem. Multi-Agent systems have been increasingly used from this purpose. This work proposes a system for the management of information in the context of agent based SG to enable the monitoring, in real time, of the events that occur in the ecosystem and to predict upcoming events.