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20th International Symposium on Distributed computing and Artificial Intelligence, DCAI 2023

作者： da Veiga, Carlos Ernani Ramos, Carlos Corchado, Juan Manuel Fernandes, Piara Soares, João DAE - Academic Department of Electrotechnics IFSC - Federal Institute of Santa Catarina Florianopolis Brazil GECAD - Research Group On Intelligent Engineering and Computing for Advanced Innovation and Development LASI - Intelligent Systems Associate Laboratory Polytechnic of Port Porto Portugal BISITE Research Group University of Salamanca Edificio I+D+I Salamanca37008 Spain

ISBN: (纸本)9783031383175

Electric vehicle (EV) owners often have electric vehicle charging stations (EVCS) in their homes and leave the EV connected when it remains in the garage. This procedure does not guarantee that EV charging is adequate, and monitoring this period is necessary to identify anomalies. A study was developed where the EV charging curves was analyzed by clustering with K-means. The advantage presented in this work is that the necessary data for the charging analysis come from the EVCS power supply circuit. This proposal allows monitoring and rapidly classifying the data according to the form of the EV charging curves, with no need for the supervision of the input data. The unsupervised machine learning method can classify any situation by collecting data on the electrical power supplied to the EVCS. In this study, the classification was applied with learning performed in the history, identifying the clusters representing abnormal situations to identify anomalies in future charging curves. The results of the proposed approach presented around 27.8% as an anomaly in the EV charging, allowing to identify the moment in which it occurs and to make the necessary adjustments to avoid them. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Learning systems

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Enhance Demand Response Participants Performance Using a Personalized Recommendation Method

Enhance Demand Response Participants Performance Using a Per...

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International Conference on Environment and Electrical eng.neering (EEEIC)

作者： Cátia Silva Pedro Faria Zita Vale Intelligent Systems Associated Laboratory (LASI) Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD) Polytechnic of Porto Porto Portugal

For a successful implementation of Demand Response (DR) in the real markets, the uncertainty from the players' response must be reduced. From the community manager's perspective, the proposed methodology focuses on selecting trustworthy participants according to their availability for the triggered DR event. As innovation from previous works, the authors focus now on the consumer perspective. The present paper introduces a tool developed for performance enhancement by displaying the expected rewards according to consumer participation. In other words, the tool performs a sensitivity analysis to study all the possibilities and achieve the player's goal. For example, the tool might suggest the optimal one according to the frequency and type of DR programs triggered, but the participation is voluntary and depends on player availability. The case study results discuss the perspective of four players with distinct objectives.

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David – A Novel Approach to an Industrial Recommender System 20th

David – A Novel Approach to an Industrial Recommender Syste...

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20th International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2022

作者： Marques, Rui Martins, Constantino Moreno-García, María N. GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development ISEP – Instituto Superior de Engenharia do Porto Porto Portugal USAL - Universidad de Salamanca Salamanca Spain

ISBN: (纸本)9783031186967

A conceptual approach to a recommender system that is industrial oriented and optimized for business-to-business. With particular needs, industrial datasets seek assertiveness and contextualization to capitalize on recommender systems. Muscle memory must be implanted on business users, enabling them to harvest the benefits of such technologies. These types of users, which differ from standard ones, are fuzzy and vague in their choices, avoiding explicit forms of feedback. This paper addresses state-of-the-art of such industrial systems, its users and explores a potential solution. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Recommender systems

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Production and Maintenance Scheduling for Total Cost and Machine Longevity Optimization

Production and Maintenance Scheduling for Total Cost and Mac...

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Future Energy Solutions (FES), International Conference on

作者： Bruno Mota Pedro Faria Bruno Canizes Carlos Ramos LASI - Intelligent Systems Associate Laboratory Polytechnic of Porto GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Porto Portugal

Production and maintenance scheduling for total cost-effective and machine longevity manufacturing is a key aspect in dealing with the ever-increasing energy prices, competitiveness, added maintenance costs, and environmental pressures that the manufacturing sector faces nowadays. This paper addresses these issues by proposing a novel intelligent production scheduling system for joint optimization of production and maintenance for overall cost minimization and machine longevity improvement. To achieve this, it is proposed a Genetic Algorithm (GA) for production and maintenance scheduling of flexible job shop manufacturing environments. The proposed GA takes into account volatile market energy prices, Renewable Energy Resources (RERs), surplus energy selling, maintenance activities, and constraints imposed on the production plan. A case study from the literature is used as a baseline scenario to validate the proposed scheduler. It uses real-production data and considers three unique machines with 275 tasks to be scheduled among them. Using the baseline scenario, it was possible to demonstrate the robustness of the proposed scheduler in reducing total costs, by taking advantage of the volatility of energy prices, as well as utilizing RERs to cover energy expenses or for selling excess energy. Furthermore, it highlights the ability to reduce the overload of single machines. Accordingly, it was possible to achieve cost reductions of up to 11.0% and improvements of 24.4% in machine longevity when compared to the baseline scenario.

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Analysing the Impact of Local Electricity Transactions with EV Penetration on the Electrical Grid

Analysing the Impact of Local Electricity Transactions with ...

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International Conference on European Electricity Market, EEM

作者： Fernando Lezama Ricardo Faia Pedro Faria Zita Vale GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI – Intelligent Systems Associate Laboratory Polytechnic of Porto Porto Portugal

This article analysis, through simulation, the impact that local transactions and the penetration of EVs have on the distribution network and the costs and incomes of local market participants. An auction-based competitive market and mathematical model are provided to simulate end-user transactions with EVs on the low-voltage grid. The proposed framework is validated in a scenario considering 55 end-users (some with PV generation and EVs) and six combined heat and power generators trading energy in the IEEE European Low Voltage Test Feeder system. Furthermore, a distribution system operator is considered by defining network constraints (voltage limits and lines overloading) that ensure the correct operation of the distribution grid.

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Detailed analysis of a deep learning energy forecast model considering different input units and dimensions

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IFAC-PapersOnLine 2024年第13期58卷 308-313页

作者： Bruno Ribeiro Rafael Silva Luis Gomes Zita Vale GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI - Intelligent Systems Associate Laboratory Polytechnic of Porto Rua Dr. António Bernardino de Almeida 431 4249-015 Porto Portugal

The use of smart buildings technologies in residential houses, commonly referred to as smart homes, can benefit the operation and management of the buildings as well and its users. The application of energy related models in smart buildings can improve building sustainability and efficiency, but, usually, requires the existence of energy forecasting models capable of providing accurate and precise information. In this paper, it was studied how different input configurations of an energy forecasting model impact its performance by considering multiple error metrics. The conducted study allowed a detailed analysis of each input configuration to see which one is better suitable to be integrated with the smart grid management platform developed by the authors, which is also presented in this paper. The results demonstrate that the inputs’ units and dimensions have impact on the forecast error, indicating that accumulative Wh values are potentially better for the energy forecast model considered.

关键词： Energy forecasting error analysis forecast evaluation IoT-based smart building smart buildings

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Optimizing House Energy Management with MILP Considering Shifting of Electric Appliances and Battery Storage System

Optimizing House Energy Management with MILP Considering Shi...

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Ricardo Faia Pedro Faria Zita Vale GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI - Intelligent Systems Associate Laboratory Polytechnic of Porto Porto Portugal

The integration of renewable energy sources into the power grid has led to the need for efficient energy management systems that consider the presence of energy storage systems and controllable loads. In this paper, the authors propose a mixed-integer linear programming optimization model for energy management that takes into account the operation of shift electric appliances and battery storage systems. The proposed model aims to minimize the overall electricity cost while ensuring that the energy demand of the system is met, the battery state of charge is maintained within safe operating limits, and the shift electric appliance met with the user requirements. The authors consider the operational constraints of shifting electric appliances, which can be scheduled to operate during periods of low electricity prices. Additionally, they account for the energy storage system, which can be charged during periods of excess renewable energy production and discharged during periods of high demand. Four different scenarios are tested to prove the value of the model.

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Joint Optimization of Production and Maintenance for Effective Manufacturing Using a Genetic Algorithm

Joint Optimization of Production and Maintenance for Effecti...

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International Conference on Environment and Electrical eng.neering (EEEIC)

作者： Bruno Mota Pedro Faria Carlos Ramos GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI - Intelligent Systems Associate Laboratory Polytechnic of Porto Porto Portugal

While energy prices are seen as the major drive for competitiveness in the manufacturing field, intelligent maintenance scheduling is also one of the most widespread and significant issues plaguing the manufacturing industry. Maintenance activities can cost between 15% and 70% of the price of goods sold, thus it is essential to optimize both energy and maintenance costs. The purpose of this paper is to propose a production line optimization system that focuses on reducing the total costs (i.e., energy and maintenance costs) while also taking into account dynamic pricing, Renewable Energy Resource (RER) usage, and constraints applied in the production plan. In the proposed system, tasks and maintenance activities are scheduled by using a Genetic Algorithm. To validate the proposed system, a baseline scenario that uses real production data is considered. The obtained results show that the system is able to comply with imposed maintenance hours while also minimizing costs by shifting tasks to higher RER generation and lower energy price times, while the opposite is done to maintenance activities.

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Demand Response-based Energy Management Model for Energy Communities considering Data Privacy of the Members

Demand Response-based Energy Management Model for Energy Com...

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Ruben Barreto Luis Gomes Pedro Faria Zita Vale GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI - Intelligent Systems Associate Laboratory Polytechnic of Porto Porto Portugal

This paper proposes a demand response-based energy management model for energy communities, considering the respective members’ data privacy. Through forecasting and clustering algorithms, this model can identify demand response opportunities for the next day, rank and select the participants for the event and monitor and evaluate the respective event. The paper’s novelty lies in increasing the energy community members’ active participation by allowing them to submit one or more proposals to participate in a demand response event. In this way, a member can, on the one hand, have more freedom to choose how to participate and, on the other hand, have more opportunities to contribute to the DR event with their energy flexibility. This model was tested with real data from an energy community with 50 buildings, which can provide flexibility through reductions and shifting. The results showed that the model’s efficiency could increase by considering the multiple proposals per member. Moreover, it was verified that with just one event, it is possible to reduce the CO 2 emissions and the energy cost by 50% and 73%, respectively, as well increasing the energy community sustainability by 10%.

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Automated energy management and learning

Automated energy management and learning

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Artificial Intelligence (CAI), IEEE Conference on

作者： Gabriel Santos Brígida Teixeira Tiago Pinto Zita Vale GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI - Intelligent Systems Associate LAboratory Polytechnic of Porto Porto Portugal

Automatic energy management systems allow users’ active participation in flexibility management while assuring their energy demands. We propose a transparent framework for automated energy management to increase trust and improve the learning process, combining machine learning, experts’ knowledge, and semantic reasoning. A practical example of thermal comfort shows the advantages of the framework.

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