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- A Comparative Analysis of Machine Learning Techniques for IoT Intrusion DetectionPublication . Vitorino, João; Andrade, Rui; Praça, Isabel; Sousa, Orlando Jorge Coelho Moura; Maia, EvaThe digital transformation faces tremendous security challenges. In particular, the growing number of cyber-attacks targeting Internet of Things (IoT) systems restates the need for a reliable detection of malicious network activity. This paper presents a comparative analysis of supervised, unsupervised and reinforcement learning techniques on nine malware captures of the IoT-23 dataset, considering both binary and multi-class classification scenarios. The developed models consisted of Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Isolation Forest (iForest), Local Outlier Factor (LOF) and a Deep Reinforcement Learning (DRL) model based on a Double Deep Q-Network (DDQIN), adapted to the intrusion detection context. The most reliable performance was achieved by LightGBM. Nonetheless, iForest displayed good anomaly detection results and the DRL model demonstrated the possible benefits of employing this methodology to continuously improve the detection. Overall, the obtained results indicate that the analyzed techniques are well suited for IoT intrusion detection.
- Managing Smart City Power Network by Shifting Electricity Consumers DemandPublication . Silva, Cátia; Faria, Pedro; Vale, ZitaDemand Response (DR) concept, introduced by the Smart Grid paradigm, is presented as one of the main solutions to mitigate the effects of the intermittency of Distributed Generation sources in the network. With this, the consumer’s role in the energy market is empowered and their flexibility is crucial. The technology advances allow bidirectional communication providing signals to the active consumers, given by the entity manager, participating in DR events to alleviate problems in the grid. The authors proposed a methodology, resorting to load shifting, to lessen voltage limit violations in some points of the grid. After the detention, the DR event is triggered and the small resources are scheduled, requesting a reduction to the active consumers. The authors believe that uncertainty in the response must be considered so, the willingness and availability from the active community are contemplated on the problem. The results show the necessity of pondering consumer behavior.
- Trends in Practical Applications of Scalable Multi-Agent Systems, the PAAMS CollectionPublication . Prieta, Fernando de la; Escalona, María J.; Corchuelo, Rafael; Mathieu, Philippe; Vale, Zita; Campbell, Andrew T.; Rossi, Silvia; Adam, Emmanuel; Jiménez-López, María D.; Navarro, Elena M.; Moreno, María N.PAAMS, the International Conference on Practical Applications of Agents and Multi-Agent Systems is an evolution of the International Workshop on Practical Applications of Agents and Multi-Agent Systems. PAAMS is an international yearly tribune to present, to discuss, and to disseminate the latest developments and the most important outcomes related to real-world applications. It provides a unique opportunity to bring multi-disciplinary experts, academics and practitioners together to exchange their experience in the development of Agents and Multi-Agent Systems. This volume presents the papers that have been accepted for the 2016 in the special sessions: Agents Behaviours and Artificial Markets (ABAM); Advances on Demand Response and Renewable Energy Sources in Agent Based Smart Grids (ADRESS); Agents and Mobile Devices (AM); Agent Methodologies for Intelligent Robotics Applications (AMIRA); Learning, Agents and Formal Languages (LAFLang); Multi-Agent Systems and Ambient Intelligence (MASMAI); Web Mining and Recommender systems (WebMiRes). The volume also includes the paper accepted for the Doctoral Consortium in PAAMS 2016 and Collocated Events.
- Trends in Cyber-Physical Multi-Agent Systems. The PAAMS Collection - 15th International Conference, PAAMS 2017Publication . Prieta, Fernando De la; Vale, Zita; Antunes, Luís; Pinto, Tiago; Campbell, Andrew T.; Julián, Vicente; Neves, Antonio J.R.; Moreno, María N.PAAMS, the International Conference on Practical Applications of Agents and Multi-Agent Systems is an evolution of the International Workshop on Practical Applications of Agents and Multi-Agent Systems. PAAMS is an international yearly tribune to present, to discuss, and to disseminate the latest developments and the most important outcomes related to real-world applications. It provides a unique opportunity to bring multi-disciplinary experts, academics and practitioners together to exchange their experience in the development of Agents and Multi-Agent Systems. This volume presents the papers that have been accepted for the 2017 in the special sessions: Agent-Based Social Simulation, Modelling and Big-Data Analytics (ABM); Advances on Demand Response and Renewable Energy Sources in Agent Based Smart Grids (ADRESS); Agents and Mobile Devices (AM); Computer vision in Multi-Agent Robotics (RV); Persuasive Technologies (PT); Web and Social Media Mining (WASMM). The volume also includes the papers accepted for publication in the Doctoral Consortium (DCAI, DCAI-DECON, ISAMI, MIS4TEL, PAAMS, PACBB 2017 conferences).
- Long-Term Smart Grid Planning Under Uncertainty Considering Reliability IndexesPublication . Canizes, Bruno; Soares, João; Fotouhi Ghazvini, Mohammad Ali; Silva, Cátia; Vale, Zita; Corchado, Juan M.The electricity sector is fast moving towards a new era of clean generation devices dispersed along the network. On one hand, this will largely contribute to achieve the multi-national environment goals agreed via political means. On the other hand, network operators face new complexities and challenges regarding network planning due to the large uncertainties associated with renewable generation and electric vehicles integration. In addition, due to new technologies such as combined heat and power (CHP), the district heat demand is considered in the long-term planning problem. The 13-bus medium voltage network is evaluated considering the possibility of CHP units but also without. Results demonstrate that CHP, together with heat-only boiler units, can supply the district heat demand and contribute to network reliability. They can also reduce the expected energy not supplied and the power losses cost, avoiding the need to invest in new power lines for the considered lifetime project.
- Multi-agent Electricity Markets and Smart Grids Simulation with Connection to Real Physical ResourcesPublication . Pinto, Tiago; Vale, Zita; Praça, Isabel; Gomes, Luis; Faria, PedroThe increasing penetration of distributed energy sources, mainly based on renewable generation, calls for an urgent emergence of novel advanced methods to deal with the associated problems. The consensus behind smart grids (SGs) as one of the most promising solutions for the massive integration of renewable energy sources in power systems has led to the development of several prototypes that aim at testing and validating SG methodologies. The urgent need to accommodate such resources require alternative solutions. This chapter presents a multi-agent based SG simulation platform connected to physical resources, so that realistic scenarios can be simulated. The SG simulator is also connected to the Multi-Agent Simulator of Competitive Electricity Markets, which provides a solid framework for the simulation of electricity markets. The cooperation between the two simulation platforms provides huge studying opportunities under different perspectives, resulting in an important contribution to the fields of transactive energy, electricity markets, and SGs. A case study is presented, showing the potentialities for interaction between players of the two ecosystems: a SG operator, which manages the internal resources of a SG, is able to participate in electricity market negotiations to trade the necessary amounts of power to fulfill the needs of SG consumers.
- Demonstration of an Energy Consumption Forecasting System for Energy Management in BuildingsPublication . Jozi, Aria; Ramos, Daniel; Gomes, Luis; Faria, Pedro; Pinto, Tiago; Vale, ZitaDue to the increment of the energy consumption and dependency of the nowadays lifestyle to the electrical appliances, the essential role of an energy management system in the buildings is realized more than ever. With this motivation, predicting energy consumption is very relevant to support the energy management in buildings. In this paper, the use of an energy management system supported by forecasting models applied to energy consumption prediction is demonstrated. The real-time automatic forecasting system is running separately but integrated with the existing SCADA system. Nine different forecasting approaches to obtain the most reliable estimated energy consumption of the building during the following hours are implemented.
- Identifying Most Probable Negotiation Scenario in Bilateral Contracts with Reinforcement LearningPublication . Silva, Francisco; Pinto, Tiago; Praça, Isabel; Vale, ZitaThis paper proposes an adaptation of the Q-Learning reinforcement learning algorithm, for the identification of the most probable scenario that a player may face, under different contexts, when negotiating bilateral contracts. For that purpose, the proposed methodology is integrated in a Decision Support System that is capable to generate several different scenarios for each negotiation context. With this complement, the tool can also identify the most probable scenario for the identified negotiation context. A realistic case study is conducted, based on real contracts data, which confirms the learning capabilities of the proposed methodology. It is possible to identify the most probable scenario for each context over the learned period. Nonetheless, the identified scenario might not always be the real negotiation scenario, given the variable nature of such negotiations. However, this work greatly reduces the frequency of such unexpected scenarios, contributing to a greater success of the supported player over time.
- Contextual Simulated Annealing Q-Learning for Pre-negotiation of Agent-Based Bilateral NegotiationsPublication . Pinto, Tiago; Vale, ZitaElectricity markets are complex environments, which have been suffering continuous transformations due to the increase of renewable based generation and the introduction of new players in the system. In this context, players are forced to re-think their behavior and learn how to act in this dynamic environment in order to get as much benefit as possible from market negotiations. This paper introduces a new learning model to enable players identifying the expected prices of future bilateral agreements, as a way to improve the decision-making process in deciding the opponent players to approach for actual negotiations. The proposed model introduces a con-textual dimension in the well-known Q-Learning algorithm, and includes a simulated annealing process to accelerate the convergence process. The proposed model is integrated in a multi-agent decision support system for electricity market players negotiations, enabling the experimentation of results using real data from the Iberian electricity market.
- Fair Remuneration of Energy Consumption Flexibility Using Shapley ValuePublication . Faia, R.; Pinto, Tiago; Vale, ZitaThis paper proposes a new methodology for fair remuneration of consumers participation in demand response events. With the increasing penetration of renewable energy sources with a high variability; the flexibility from the consumers’ side becomes a crucial asset in power and energy systems. However, determining how to effectively remunerate consumers flexibility in a fair way is a challenging task. Current models tend to apply over-simplistic and non-realistic approaches which do not incentivize the participation of the required players. This paper proposes a novel methodology to remunerate consumers flexibility, in a fair way. The proposed model considers different aggregators, which manage the demand response requests within their coalition. After player provide their flexibility, the remuneration is calculated based on the flexibility amount provided by the players, the previous participation in demand response programs, the localization of the players, the type of consumer, the effort put in the provided flexibility amount, and the contribution to the stability of the coalition structure using the Shapley value. Results show that by assigning different weights to the distinct factors that compose the calculation formulation, players remuneration can be adapted to the needs and goals of both the players and the aggregators.