Abstract

Abstract
Agent-based simulation tools have found many applications in the field of Power and Energy Systems, as they can model and analyze the complex synergies of dynamic and continuously evolving systems. While some studies have been done w.r.t. simulation and decision support for electricity markets and smart grids, there is still a generalized limitation referring to the significant lack of interoperability between independently developed systems, hindering the task of addressing all the relevant existing interrelationships. This work presents the Semantic Services Catalog (SSC), developed and implemented for the automatic registry, discovery, composition, and invocation of web and agent-based services. By adding a semantic layer to the description of different types of services, this tool supports the interaction between heterogeneous multiagent systems and web services with distinct capabilities that complement each other. The case study confirms the applicability of the developed work, wherein multiple simulation and decision-support tools work together managing a microgrid of residential and office buildings. Using SSC, besides discovering each other, agents also learn about the ontologies and languages to use to communicate with each other effectively.

Abstract
The increasing penetration of renewable energy sources and the need to adjust to the future demand requires adopting measures to improve energy resources management, especially in buildings. In this context, PV generation forecast has an essential role in the energy management entities by preventing problems related to intermittent weather conditions and allowing participation in incentive programs to reduce energy consumption. This paper proposes an automatic model for the day-ahead PV generation forecast, combining several forecasting algorithms with the expected weather conditions. To this end, this model communicates with a SCADA system, which is responsible for the cyberphysical energy management of an actual building.

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
Northwest Iberia can be considered as one of the main areas where tin was exploited in antiquity. However, the location of ancient tin mining and metallurgy, their date and the intensity of tin production are still largely uncertain. The scale of mining activity and its socio-economical context have not been truly assessed, nor its evolution over time. With the present study, we intend to present an integrated, multiscale, multisensor and interdisciplinary methodology to tackle this problem. The integration of airborne LiDAR and historic aerial imagery has enabled us to identify and map ancient tin mining remains on the Tinto valley (Viana do Castelo, northern Portugal). The combination with historic mining documentation and literature review allowed us to confirm the impact of modern mining and define the best-preserved ancient mining areas for further archaeological research. After data processing and mapping, subsequent ground-truthing involved field survey and geological sampling that confirmed cassiterite exploitation as the key feature of the mining works. This non-invasive approach is of importance for informing future research and management of these landscapes.

Abstract
Networking equipment that connects households to an operator network, such as home gateways and routers, are major victims of cyber-attacks, being exposed to a number of threats, from misappropriation of user accounts by malicious agents to access to personal information and data, threatening users’ privacy and security. The exposure surface to threats is even wider when the growing ecosystem of Internet-of-Things devices is considered. Thus, it is beneficial for the operator and customer that a security service is provided to protect this ecosystem. The service should be tailored to the particular needs and Internet usage profile of the customer network. For this purpose, Machine Learning methods can be explored to learn typical behaviours and identify anomalies. In this paper, we present preliminary insights into the architecture and mechanisms of a security service offered by an Internet Service Provider. We focus on Distributed Denial-of-Service kind of attacks and define the system requirements. Finally, we analyse the trade-offs of distributing the service between operator equipment deployed at the customer premises and cloud-hosted servers.