Abstract
In recent years, the energy landscape has undergone significant transformations, characterized by the integration of renewable energy sources, smart grids, and the proliferation of IoT-enabled devices. As a result, the efficient management of energy resources has become paramount, requiring advanced methodologies in load forecasting and clustering. This article presents an enhanced methodology for short-term load forecasting that focuses on load consumption profile recognition within a smart building environment. The methodology is designed to analyze and identify recurring load consumption profiles and measures of sensors, thereby enhancing load consumption profile recognition capabilities within the smart building context. The interaction between single and grouped datasets is explored to enhance the accuracy and interpretability of predictions, contributing to optimized energy consumption and providing valuable information for demand response programs. The default forecasting methods used in the methodology are artificial neural networks and k-nearest neighbors. For comparing results and evaluating the proposed approach, XGBoost is also employed. The dataset is selected from a specific database, and the clustering method, partitioning type, is applied with k-means. The results, validated with error evaluation models and statistics, reveal the advantages of the proposed approach, especially with three clusters, where the results achieved by the Artificial Neural Network are the best. The clustering process, particularly the partitioning type, demonstrates a strong capability in improving load forecasting in smart buildings and helps understand load consumption patterns and achieve energy savings. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract
In collaborative energy communities, optimizing energy costs is a critical aspect of sustainable management. This article explores the potential benefits of applying clustering algorithms to vary retail tariffs monthly, aiming to reduce energy bills for the community as a whole. The article compares a traditional approach of applying the same tariff to all community members throughout the year with a novel approach of dynamically changing tariffs based on monthly clustering results. A case study is conducted, wherein energy bill costs per month are analyzed under different tariff scenarios utilizing k -means clustering. Results indicate that the proposed approach yields promising reductions in energy costs, up to 8.76% (1170.18 EUR) improvement compared to the traditional method. The study contributes valuable insights into the practical application of clustering in energy community management and highlights the potential for significant cost savings through dynamic tariff adjustments.

Abstract
The transition from the current energy architecture to a new model is evident and inevitable. The coming future promises innovative and increasingly rigorous projects and challenges for everyone involved in this value chain. Technological developments have allowed the emergence of new concepts, such as renewable energy communities, decentralized renewable energy production, and even energy storage. These factors have incited consumers to play a more active role in the electricity sector and contribute considerably to the achievement of environmental objectives. With the introduction of renewable energy communities, the need to develop new management and optimization tools, mainly in generation and load management, arises. Thus, this paper proposes a platform capable of clustering consumers and prosumers according to their energy and geographical characteristics to create renewable energy communities. Thus, this paper proposes a platform capable of clustering consumers and prosumers according to their energy and geographical characteristics to create renewable energy communities. Moreover, through this platform, the identification (homogeneous energy communities, mixed energy communities, and self-sufficient energy communities) and the size of each community are also obtained. Three algorithms are considered to achieve this purpose: K-means, density-based spatial clustering of applications with noise, and linkage algorithms (single-link, complete-link, average-link, and Wards' method). With this work, it is possible to verify each algorithm's behavior and effectiveness in clustering the players into communities. A total of 233 members from 9 cities in the northern region of Portugal (Porto District) were considered to demonstrate the application of the proposed platform. The results demonstrate that the linkage algorithms presented the best classification performance, achieving 0.631 by complete-ink in the Silhouette score, 2124.174 by Ward's method in the Calinski-Harabasz index, and 0.329 by single-link on the Davies-Bouldin index. Additionally, the developed platform demonstrated adequacy, versatility, and robustness concerning the classification and sizing of renewable energy communities.

Abstract
Hydrogen is a promising commodity, a renewable secondary energy source, and feedstock alike, to meet greenhouse gas emissions targets and promote economic decarbonization. A common goal pursued by many countries, the hydrogen economy receives a blending of public and private capital. After European Green Deal, state members created national policies focused on green hydrogen. This paper presents a study of energy transition considering green hydrogen production to identify Portugal’s current state and prospects. The analysis uses energy generation data, hydrogen production aspects, CO (Formula presented.) emissions indicators and based costs. A comprehensive simulation estimates the total production of green hydrogen related to the ratio of renewable generation in two different scenarios. Then a comparison between EGP goals and Portugal’s transport and energy generation prospects is made. Portugal has an essential renewable energy matrix that supports green hydrogen production and allows for meeting European green hydrogen 2030–2050 goals. Results suggest that promoting the conversion of buses and trucks into H (Formula presented.) -based fuel is better for CO (Formula presented.) reduction. On the other hand, given energy security, thermoelectric plants fueled by H (Formula presented.) are the best option. The aggressive scenario implies at least 5% more costs than the moderate scenario, considering economic aspects. © 2023 by the authors.

Abstract
During the past few years the transport matrix received many policies to push for the sector decarbonization. The electric vehicles and charging infrastructure increased a lot motivated by European Union directives and countries legislations, becoming national policies framework. Considering the electricity market dynamics, the electrification of transport created a new challenge going forward. In this context, this paper presents a multivariate analysis of electricity commercialization and charging infrastructure to evaluate the real state of electricity mobility and design future opportunities. The analysis uses tariffs, commercialization models, charging services and economic indicators of four countries. A comprehensive simulation model estimates the total electric mobility bill per country and the portion of the average salary spent with the car charging. Even considering the best scenario, consumers from Portugal commit almost four percent of its average wage while Norway commit only one percent. The results reveal that long-term commitment with energy planning, generation and energy matrix expansion, implies on lower energy costs; better economic actions also imply on lower energy expenditure for costumer. The hourly tariffs are important alternatives to reduce energy costs and manage demand helping network operators to plan and manage the energy system. © 2022 Elsevier B.V.. All rights reserved.