Abstract
Energy allocation rules are one of the core aspects of collective self-consumption (CSC) regulations. It allows final consumers to share their surplus generation with other CSC members, while keeping their full rights as consumers, i.e., maintaining a supply contract with the retailers of their choice. Some European Union member states regulations use allocation coefficients so that local allocations are integrated with wholesale settlement and directly affect the retailers' billing. Several AC methods have been proposed so far, each one adapted to distribution system operators' settlement procedures with specific rules that can impact the benefits that each CSC member obtain. This paper analyses, assesses and compares two relevant AC methods, namely pre-delivery fixed AC and post-delivery dynamic AC, by developing a settlement formulation for a community with members with flexible assets and different opportunity costs. AC policy recommendations based on findings are provided.

Abstract
Predictive maintenance methods play a crucial role in the early detection of failures and errors in machinery, preventing them from reaching critical stages. This paper presents a comprehensive study on a real-world dataset called MetroPT3, with data from a Metro do Porto train's air production unit (APU) system. The dataset comprises data collected from various analogue and digital sensors installed on the APU system, enabling the analysis of behavioural changes and deviations from normal patterns. We propose a data-driven predictive maintenance framework based on a Long Short-Term Memory Autoencoder (LSTM-AE) network. The LSTM-AE efficiently identifies abnormal data instances, leading to a reduction in false alarm rates. We also implement a Sparse Autoencoder (SAE) approach for comparative analysis. The experimental results demonstrate that the LSTM-AE outperforms the SAE regarding F1 Score, Recall, and Precision. Furthermore, to gain insights into the reasons for anomaly detection, we apply the Shap method to determine the importance of features in the predictive maintenance model. This approach enhances the interpretability of the model to support the decision-making process better.

Abstract
Sustainable development policies trigger a shift in the global development paradigm by aligning economic, social, and ecological goals. Concurrently, the rapid surge in digitalization is transforming business processes and communications across all sectors and levels. As a result, the integration of e-business and e-governance becomes a critical component in achieving Sustainable Development Goals (SDGs). In this context, the aim of this article is to analyze the effects of digitalization, specifically e-governance and e-business, on the attainment of SDGs in European Union (EU) countries. The method used is a panel of corrected standard errors and feasible generalized least squares models to identify the impact and significance of e-governance and e-business on SDG achievement. The e-governance indicators considered by this study were found to significantly impact SDG achievement. Moreover, e-business indicators were also found to positively impact the attainment of SDGs, with some exceptions. The findings suggest that EU countries should continue to intensify digitalization across all sectors as it enhances the transparency accountability of all business processes and communications and increases trust in government services, which are the core drivers of achieving SDGs.

Abstract
This chapter aims to discuss about the potential Return on Investment (ROI) measures from Artificial intelligence (AI) investments that business can leverage. It discusses the concepts and describes the dimensions, features and tools of AI investments in Marketing business, to assist the readers to understand about the topic. The authors also describe the major drivers of ROI measures for business applications and discusses the concerns and limitations of tangible measures. So, this document contributes to the literature on ROI (in)tangibles measures that leverage AI investments and features issues in digital marketing, at large and potentially offers a theoretical grounding for many empirical and theoretical future studies. © 2025 by IGI Global Scientific Publishing. All rights reserved.

Abstract
While the share of renewable energy in interconnected systems has been increasing steadily, in isolated systems it represents a bigger challenge. This paper presents a dispatch algorithm integrating thermal, wind, solar and hydro generation and storage for an isolated network, which allows maximizing renewable energy integration and reducing the share of thermal energy in the mix. The possibility of using the battery to provide spinning reserve is also considered. The algorithm was tested and validated using real data from the island of Madeira, Portugal. Results prove the robustness and flexibility of the algorithm, showing that a significant decrease in the thermal fraction is achievable, and that it is possible to accommodate an increase in renewable generation with minimal or no curtailment at all.

Abstract
The integration of renewable generation requires new sources of flexibility, including the flexibility from distributed resources that can be unlocked via local flexibility markets (LFMs). In these markets, aggregators (AGGs) offer the flexibility from their portfolios to the flexibility requesting parties (FRP), i.e. system operators or other balancing requesting parties. To bid in LFMs and manage market uncertainty, AGGs must compute the flexibility they are willing to offer at each possible flexibility market price, by optimizing their portfolios. This paper proposes a 2-stage methodology to compute the flexibility bidding curve that an energy community can send to a LFM when behaving as an AGG of its members resources. At stage 1, the energy community (EC) manager computes the optimal EC operation without flexibility provision, minimizing the EC energy bill, and serving as the baseline to verify the flexibility provision. Then, at stage 2, for each possible flexibility price, the EC manager computes the optimal flexibility to be offered, minimizing the EC energy bill but including the flexibility provision incomes, to build the flexibility bidding curve.