Abstract
This paper presents a novel feed-forward neural network for wind speed forecasting. The electricity sector accounts for a quarter of the world CO2 emissions. To reduce these emissions, several national, regional and global agreements have been signed, setting ambitious goals to increase the penetration of renewable energy sources (RES). Although achieving those goals is essential for the sector decarbonization and, therefore, to mitigate the global climate crisis, renewable-based generation can depend on highly variable and uncertain resources, such as the wind. Hence, having access to reliable forecasts of those resources availability is essential for the operation of several actors in the power and energy sector, and for the effectiveness of the whole system. This paper contributes to surpass this problem by introducing a new forecasting model based on a feed-forward neural network to forecast wind speed. The proposed model is applied to real data from a wind farm in the south of South America. Results show that the proposed model can achieve lower forecasting errors than the baseline models, which consist of Numerical Weather Predictions.

Abstract
This study proposes a comprehensive framework integrating eXplainable Artificial Intelligence (XAI) techniques with clustering-based context extraction to enhance energy consumption forecasting in modern office buildings. By leveraging explanation vectors derived from state-of-the-art XAI methods such as SHAP and LIME, our framework identifies latent operational contexts from sensor data aggregated at 15-min intervals. These contexts enable the tailoring of predictive models through feature augmentation, context-specific training, and transfer learning strategies, thereby improving forecasting accuracy compared to conventional approaches. To identify the best-performing models for each context, hyperparameter optimization via grid search is employed across multiple algorithmsincluding Gradient Boosting, Random Forest, and K-Nearest Neighbors. Extensive experiments demonstrate that context-aware models significantly outperform baseline methods, achieving up to a 7% improvement in the coefficient of determination (R-2) and a marked reduction in error metrics. Our findings underscore the importance of integrating XAI with data-driven modeling to enhance predictive performance and model interpretability, which are critical for practical energy management and decision-making in complex building environments.

Abstract
The reduction of the greenhouse gas emissions is a priority all around the globe. The investment on renewable energy sources is contributing for new opportunities in the context of the smart grids and microgrids. Recent advances are transforming the consumer into a prosumer, being able to adapt the consumption depending on its own generated power, and selling the surplus or buying the missing power. In this context, home management systems are emerging as an effective means to support the management of energy resources in the context of communication between functions/devices of a smart home. This paper presents a new agent-based home energy management approach, using ontologies to enable semantic communications between heterogeneous multi-agent entities. The main goal is to support an efficient energy management of end consumers in the context of microgrids, obtaining a scheduling for both real and virtual resources. A case study is presented, which simulates a 25-bus microgrid that includes a laboratorial controlled house (with real and simulated resources), which is managed by the proposed energy management system.

Abstract
Reliable consumption forecasts are crucial in several aspects of power and energy systems, e.g. to take advantage of the full potential of flexibility from consumers and to support the management from operators. With this need, several methodologies for electricity forecasting have emerged. However, the study of correlated external variables, such as temperature or luminosity, is still far from adequate. This paper presents the application of the Wang and Mendel's Fuzzy Rule Learning Method (WM) to forecast electricity consumption. The proposed approach includes two distinct strategies, the first one uses only the electricity consumption as the input of the method, and the second strategy considers a combination of the electricity consumption and the environmental temperature as the input, in order to extract value from the correlation between the two variables. A case study that considers the forecast of the energy consumption of a real office building is also presented. Results show that the WM method using the combination of energy consumption data and environmental temperature is able to provide more reliable forecasts for the energy consumption than several other methods experimented before, namely based on artificial neural networks and support vector machines. Additionally, the WM approach that considers the combination of input values achieves better results than the strategy that considers only the consumption history, hence concluding that WM is appropriate to incorporate different information sources.

Abstract
This paper highlights a new learning model that introduces a contextual dimension to the well-known Q-Learning algorithm. Through the identification of different contexts, the learning process is adapted accordingly, thus converging to enhanced results. The proposed learning model includes a simulated annealing (SA) process that accelerates the convergence process. The model is integrated in a multi-agent decision support system for electricity market players negotiations, enabling the experimentation of results using real electricity market data. © 2020 The authors and IOS Press.