Browsing by Author "Rodrigues, Fátima"
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- An Electric Energy Consumer Characterization Framework Based on Data Mining TechniquesPublication . Figueiredo, Vera; Rodrigues, Fátima; Vale, Zita; Gouveia, Joaquim BorgesThis paper presents an electricity consumer characterization framework based on a knowledge discovery in databases (KDD) procedure, supported by data mining (DM) techniques, applied on the different stages of the process. The core of this framework is a data mining model based on a combination of unsupervised and supervised learning techniques. Two main modules compose this framework: the load profiling module and the classification module. The load profiling module creates a set of consumer classes using a clustering operation and the representative load profiles for each class. The classification module uses this knowledge to build a classification model able to assign different consumers to the existing classes. The quality of this framework is illustrated with a case study concerning a real database of LV consumers from the Portuguese distribution company.
- An integrated system to support electricity tariff contract definitionPublication . Rodrigues, Fátima; Figueiredo, Vera; Vale, ZitaThis paper presents an integrated system that helps both retail companies and electricity consumers on the definition of the best retail contracts and tariffs. This integrated system is composed by a Decision Support System (DSS) based on a Consumer Characterization Framework (CCF). The CCF is based on data mining techniques, applied to obtain useful knowledge about electricity consumers from large amounts of consumption data. This knowledge is acquired following an innovative and systematic approach able to identify different consumers’ classes, represented by a load profile, and its characterization using decision trees. The framework generates inputs to use in the knowledge base and in the database of the DSS. The rule sets derived from the decision trees are integrated in the knowledge base of the DSS. The load profiles together with the information about contracts and electricity prices form the database of the DSS. This DSS is able to perform the classification of different consumers, present its load profile and test different electricity tariffs and contracts. The final outputs of the DSS are a comparative economic analysis between different contracts and advice about the most economic contract to each consumer class. The presentation of the DSS is completed with an application example using a real data base of consumers from the Portuguese distribution company.
- An unobtrusive multimodal stress detection model & recommender systemPublication . Ferreira, Simão; Correia, Hugo; Rodrigues, Fátima; Rodrigues, Matilde; Rocha, NunoStudies estimate that about 50% of all lost workdays are related to occupational stress. In recent years, several solutions for mental health management, including biofeedback applications, have emerged as a way to enhance employee mental health. Solutions to mitigate risk factors related to the working settings present an enormous potential and a clear contribution. However, most of the work that has been developed is limited to laboratory environments and does not suit real-life needs. Our study proposes an unobtrusive multimodal approach for detecting work-related stress combining videoplethysmography and self-reported measures for stablishing the ground truth in real-life settings. The study involved 28 volunteers over a two-month period. Various physiological signals were collected through a videopletismography solution, while users were performing daily working, for approximately eight hours a day. In parallel, selfreported measures were collected via a pop-up application (developed by the research team) that periodically retrieved the user's perceived stress (amongst other variables) in order to label the physiological data. In order to develop the stress detection model, we pre-processed the data and performed Heart Rate Variability (HRV) feature extraction. Then, we experimented with several machine learning models, utilizing both individual and combined physiological signals to explore all available alternatives. After rigorous evaluation, the best-trained model achieved an accuracy of over 80% and an F1 Score of over 85%. With the stress detection model in place, we are developing a structured intervention model to help reduce stress. This intervention model integrates two interconnected dimensions through digital coaching, which prioritizes personalized recommendations based on user preferences. Our top priority is to ensure user engagement, and we believe that adherence to and adoption of recommended interventions are more likely when users receive recommendations that align with their preferences. Thus, we prioritize personalized recommendations that are tailored to each individual's unique model. After detecting immediate stress peaks and providing real-time feedback on stress levels, our alarm system goes a step further by offering customized recommendations for brief stress relief. The digital coach (intervention model) offers various recommendations and active lifestyle changes such as exercise, task management, weight management, better sleep habits, structured pauses, and other critical interventions. These critical interventions are also based on user preferences, allowing our system to prevent future stress-related incidents and, most importantly, mitigate long-term stress. This project and its methodology demonstrate that truly unobtrusive stress detection is possible and can be performed within the scope of ethical demands. In future work, we will evaluate the responses and beneficial outcomes of implementing a recommender system.
- Benefits, concerns, and perceptions of knowledge workers regarding a video stress detection softwarePublication . Rodrigues, Matilde; Ferreira, Simão; Figueiredo, Henrique; Rodrigues, Fátima; Moreira, Fernando; Costa, Luís; Rocha, NunoStress is not only highly destructive, causing various mental health disorders (anxiety, insomnia, depression), cardiovascular diseases, poor immune function, and presenteeism, as it is costly. While concerns about occupational stress have increased, new solutions for its management have emerged. Systems based on the use of facial recognition, posture, eye movements, video monitoring, and behavioral stress detection have shown good results. Their drawback has mainly been the recording of the said video feed and privacy threats proceeding. This Focus Groups aimed to raise the opinions, perceptions, and concerns of end users regarding the system under development. With a new solution in mind, we gathered two groups of knowledge workers, one group of team leaders, and a group of consulting psychologists to gather their perceptions. A Focus Groups was conducted online via Microsoft Teams, as COVID-19 restrictions were applied during that period. Against previous reports, knowledge workers showed that privacy threats were not their major concern. Both groups showed that Mental Health was their main focus as the follow-up structure regarding stress detection was the most prevalent topic being close to information sharing and software adaptation. The results highly contribute to the development of future stress detection applications/software and the importance of a detailed and thorough explanation regarding the software framework.
- Bid definition method for electricity markets based on an adaptive multiagent systemPublication . Pinto, Tiago; Vale, Zita; Rodrigues, Fátima; Morais, H.; Praça, IsabelElectricity markets are complex environments with very particular characteristics. MASCEM is a market simulator developed to allow deep studies of the interactions between the players that take part in the electricity market negotiations. This paper presents a new proposal for the definition of MASCEM players’ strategies to negotiate in the market. The proposed methodology is multiagent based, using reinforcement learning algorithms to provide players with the capabilities to perceive the changes in the environment, while adapting their bids formulation according to their needs, using a set of different techniques that are at their disposal. Each agent has the knowledge about a different method for defining a strategy for playing in the market, the main agent chooses the best among all those, and provides it to the market player that requests, to be used in the market. This paper also presents a methodology to manage the efficiency/effectiveness balance of this method, to guarantee that the degradation of the simulator processing times takes the correct measure.
- Cost dependent strategy for electricity markets bidding based on adaptive reinforcement learningPublication . Pinto, Tiago; Vale, Zita; Rodrigues, Fátima; Praça, Isabel; Morais, H.Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simulator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM provides several dynamic strategies for agents’ behavior. This paper presents a method that aims to provide market players with strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses a reinforcement learning algorithm to learn from experience how to choose the best from a set of possible bids. These bids are defined accordingly to the cost function that each producer presents.
- Definition of MV Load Diagrams via Weighted Evidence Accumulation Clustering using SubsamplingPublication . Duarte, Jorge; Fred, Ana; Rodrigues, Fátima; Duarte, João; Ramos, Sérgio; Vale, ZitaA definition of medium voltage (MV) load diagrams was made, based on the data base knowledge discovery process. Clustering techniques were used as support for the agents of the electric power retail markets to obtain specific knowledge of their customers’ consumption habits. Each customer class resulting from the clustering operation is represented by its load diagram. The Two-step clustering algorithm and the WEACS approach based on evidence accumulation (EAC) were applied to an electricity consumption data from a utility client’s database in order to form the customer’s classes and to find a set of representative consumption patterns. The WEACS approach is a clustering ensemble combination approach that uses subsampling and that weights differently the partitions in the co-association matrix. As a complementary step to the WEACS approach, all the final data partitions produced by the different variations of the method are combined and the Ward Link algorithm is used to obtain the final data partition. Experiment results showed that WEACS approach led to better accuracy than many other clustering approaches. In this paper the WEACS approach separates better the customer’s population than Two-step clustering algorithm.
- Determination of electricity consumers’ load profiles via weighted evidence accumulation clustering using subsamplingPublication . Duarte, Jorge; Fred, Ana; Rodrigues, Fátima; Duarte, João; Ramos, Sérgio; Vale, ZitaWith the electricity market liberalization, the distribution and retail companies are looking for better market strategies based on adequate information upon the consumption patterns of its electricity consumers. A fair insight on the consumers’ behavior will permit the definition of specific contract aspects based on the different consumption patterns. In order to form the different consumers’ classes, and find a set of representative consumption patterns we use electricity consumption data from a utility client’s database and two approaches: Two-step clustering algorithm and the WEACS approach based on evidence accumulation (EAC) for combining partitions in a clustering ensemble. While EAC uses a voting mechanism to produce a co-association matrix based on the pairwise associations obtained from N partitions and where each partition has equal weight in the combination process, the WEACS approach uses subsampling and weights differently the partitions. As a complementary step to the WEACS approach, we combine the partitions obtained in the WEACS approach with the ALL clustering ensemble construction method and we use the Ward Link algorithm to obtain the final data partition. The characterization of the obtained consumers’ clusters was performed using the C5.0 classification algorithm. Experiment results showed that the WEACS approach leads to better results than many other clustering approaches.
- ISEM: a multi-agent system that simulates competitive electronic marketplacesPublication . Viamonte, Maria João; Ramos, Carlos; Rodrigues, Fátima; Cardoso, JoséWith the increasing importance of large commerce across the Internet it is becoming increasingly evident that in a few years the Iternet will host a large number of interacting software agents. a vast number of them will be economically motivated, and will negociate a variety of goods and services. It is therefore important to consider the economic incentives and behaviours of economic software agents, and to use all available means to anticipate their collective interactions. This papers addresses this concern by presenting a multi-agent market simulator designed for analysing agent market strategies based on a complete understanding of buyer and seller behaviours, preference models and pricing algorithms, consideting risk preferences. The system includes agents that are capable of increasing their performance with their own experience, by adapting to the market conditions. The results of the negotiations between agents are analysed by data minig algorithms in order to extract rules that give agents feedback to imprive their strategies.
- Knowledge extraction from medium voltage load diagrams to support the definition of electrical tariffsPublication . Ramos, Sérgio; Figueiredo, Vera; Rodrigues, Fátima; Pinheiro, Raul; Vale, ZitaWith the electricity market liberalization, distribution and retail companies are looking for better market strategies based on adequate information upon the consumption patterns of its electricity customers. In this environment all consumers are free to choose their electricity supplier. A fair insight on the customer´s behaviour will permit the definition of specific contract aspects based on the different consumption patterns. In this paper Data Mining (DM) techniques are applied to electricity consumption data from a utility client’s database. To form the different customer´s classes, and find a set of representative consumption patterns, we have used the Two-Step algorithm which is a hierarchical clustering algorithm. Each consumer class will be represented by its load profile resulting from the clustering operation. Next, to characterize each consumer class a classification model will be constructed with the C5.0 classification algorithm.