Abstract
Entropy models the added information associated to data uncertainty, proving that stochasticity is not purely random. This paper explores the potential improvement of machine learning methodologies through the incorporation of entropy analysis in the learning process. A multi-layer perceptron is applied to identify patterns in previous forecasting errors achieved by a machine learning methodology. The proposed learning approach is adaptive to the training data through a re-training process that includes only the most recent and relevant data, thus excluding misleading information from the training process. The learnt error patterns are then combined with the original forecasting results in order to improve forecasting accuracy, using the Rényi entropy to determine the amount in which the original forecasted value should be adapted considering the learnt error patterns. The proposed approach is combined with eleven different machine learning methodologies, and applied to the forecasting of electricity market prices using real data from the Iberian electricity market operator – OMIE. Results show that through the identification of patterns in the forecasting error, the proposed methodology is able to improve the learning algorithms’ forecasting accuracy and reduce the variability of their forecasting errors.

Abstract
This paper proposes a decision support model to optimize small players' negotiations in multiple alternative/complementary market opportunities. The proposed model endows players with the ability to maximize their gains in electricity market negotiations. The proposed approach is integrated in a multi-agent simulation platform, which enables experimenting different market configurations, thus facilitating the assessment of the impact of negotiation outcomes in distinct electricity markets. The proposed model is directed to supporting the actions of small players in a transactive energy environment. Therefore, the experimental findings include negotiations in local markets, negotiations through bilateral contracts, and the participation in wholesale markets (through aggregators). The validation is performed using real data from the Iberian market, and results show that by planning market actions considering the expected prices in different market opportunities, small players are able to improve their benefits from market negotiations.

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
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 ability to predict future energy consumption is very important for energy distribution companies because it allows them to estimate energy needs and supply them accordingly. Consumption prediction makes it possible for those companies to optimize their processes by, for example, providing them with knowledge about future periods of high energy demand or by enabling them to adapt their tariffs to customer consumption. Machine Learning techniques allow to predict future energy consumption on the basis of the customers' historical consumption and several other parameters. This article reviews some of the main machine learning models capable of predicting energy consumption, in our case study we use a specific set of data extracted from a two-year-period of a shoe store. Among the evaluated methods, Gradient Boosting has obtained an 86.3% success rate in predicting consumption. © 2019 IEEE.

Abstract
The necessity of end-user engagement in power systems have become a reality in recent times. One of the solutions to this engagement is the creation of local energy markets. The distribution systems operators are compelled to investigate and optimize their asset investment cost in reinforcement of grids by introducing smart grid functionalities in order to avoid investments. The congestion management is the one of the most promising strategies to deal with the network issues. This paper presents a local electricity market or flexibility negotiation as a strategy in order to help the distribution system operator in congestion management. The local market is performed considering an asymmetric action model and is coordinated by an aggregator. A case study is presented considering a simulation that uses a low voltage network with 17 buses, which includes 9 consumers and 3 prosumers, all domestic users. Results show that using the proposed market model, the network congestion is avoided by taking advantage from the trading of consumers flexibility. © 2019 IEEE.