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Open-access tools for the modelling and simulation of electricity markets

Energy Informatics 2022年第4期5卷 43页

作者： Carvalho, Rui Santos, Gabriel Vale, Zita GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI - Intelligent Systems Associate Laboratory Institute of Engineering Polytechnic of Porto Rua Dr. Antonio Bernardino de Almeida 431 Porto4249-015 Portugal

Globally, the amount of renewable energy generation is increasing, which raises the complexity of operating electrical grids to maintain stability and balance and boosts the need for developing new electricity market (EM) models fitting this new reality. To test, study, and validate the possible effects of novel EM designs, simulation techniques are frequently employed. This work proposes the use of two open-access tools for the modeling and simulation of complex EMs. These are the Electricity Markets Service (EMS) and the Spine Toolbox. EMS enables the simulation of two commonly used auction-based algorithms and the execution of three European wholesale EMs. Being published as a web service facilitates its integration with other services, systems, or software agents, such as the Spine Toolbox. The Spine Toolbox, in turn, is an open-source software for complex energy systems modeling. Combining them allows the modeling and simulation of complex EMs from the wholesale to local markets, as well as testing and validating new market designs. This work’s case study demonstrates how to use these tools to simulate the operation of the Iberian EM – MIBEL – for a month, using public data available from the market operator’s website. The results are analyzed from the perspective of the market operator and two players, i.e., a selected buyer and seller, for a specific day and the whole month. © 2022, The Author(s).

关键词： Web services

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Bridging Fashion and Technology: Synthetic Human Models for an Enhanced E-Commerce Experience 23rd

Bridging Fashion and Technology: Synthetic Human Models for...

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23rd EPIA Conference on Artificial Intelligence, EPIA 2024

作者： Duarte, Ana Rita Conceição, Luís Department of Informatics Engineering School of Engineering Polytechnic of Porto Porto Portugal Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development School of Engineering Polytechnic of Porto Porto Portugal

ISBN: (纸本)9783031734960

The fashion industry is undergoing a significant transformation, driven by advancements in digitalization and artificial intelligence (AI). This paper explores the integration of Stable Diffusion Models (SDMs) and AI to create high-quality images of synthetic models wearing a target cloth in multiple poses, enhancing the users’ experience on e-commerce websites and addressing the fast-paced demands of fashion trends. The proposed pipeline includes multiple steps: face generation, pose estimation, cloth warping, human synthesis, and refinement networks, each designed to enhance the realism and quality of the final images for e-commerce platforms. Experimental results on a small VITON-HD dataset demonstrate this approach’s overall success. BRISQUE, NIQE, and entropy were used for objective evaluation, scoring 14.1449, 4.1354, and 7.1238 respectively, indicating a high level of detail, naturalness, and complexity on the generated images. Future work should focus on enhancing the representation of clothing with complex patterns and lower and full garments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Clothes

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Learning-Based Models for intelligent Control Over Air Conditioning Units in a Smart Building 19th

Learning-Based Models for Intelligent Control Over Air Condi...

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19th International Conference on Soft computing Models in Industrial and Environmental Applications, SOCO 2024

作者： Ribeiro, Bruno Silva, Rafael Mota, Bruno Gomes, Luis Vale, Zita Polytechnic of Porto Porto Portugal GECAD Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI—Intelligent Systems Associate Laboratory Polytechnic of Porto R. Dr. António Bernardino de Almeida 431 Porto4200-072 Portugal

ISBN: (纸本)9783031750120

In power and energy systems, artificial intelligence is commonly used for energy forecasting and decision-making. However, the real deployment of artificial intelligence models for operation in smart buildings is not common, and there is a significant gap between the literature-proposed models and deployed models. In this research, the authors apply multiple machine learning models to learn user-profiles and comfort levels regarding air conditioning usage: decision tree, random forest, support vector machine, and artificial neural network. To validate the models, a real use case with a one-year dataset was used. The dataset preparation is also part of this research. The models were analyzed using simulation and the best solution, in this case support vector machine, was deployed in a real uncontrollable environment using a containerized-based solution. The promising results demonstrate that the proposed methodology can be deployed in real buildings and learn the air conditioning usage profiles within one year of use. The authors also proceed with the deployment of a learning model in a smart building. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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Analyzing the Impact of Pricing Strategies on Economic Fairness in Energy Communities

Analyzing the Impact of Pricing Strategies on Economic Fairn...

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IEEE General Meeting Power& Energy Society

作者： Calvin Goncalves Fernando Lezama Zita Vale Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD) LASI - Polytechnic of Porto Porto Portugal

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

This article proposes a comprehensive framework encompassing energy sharing optimization, pricing schemes for energy transactions, and fairness metrics to validate the profit distribution of members in an energy community. First, a mixed-integer linear programming model is introduced to optimize energy sharing among the members of an energy community. Second, the study provides insights into the impact of four pricing mechanisms on economic fairness within the energy community. Finally, to validate the profit distribution, two fairness metrics assessing the quality of experience and the profit gaps are integrated to ensure economic equity within the energy community. The results shed light on the achieved level of fairness, evaluating the effectiveness of diverse pricing methods and the collaborative energy-sharing dynamics within the energy community.

关键词： Measurement Costs Scalability Buildings Collaboration Pricing Mixed integer linear programming Quality of experience Sustainable development Optimization

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Optimizing Energy Costs in Energy Communities: A Monthly Tariff Clustering Approach

Optimizing Energy Costs in Energy Communities: A Monthly Tar...

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International Conference on intelligent Systems Applications to Power Systems (ISAP)

作者： Fernando Lezama Diego Bairrao Filipe Doria Zita Vale GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI - Polytechnic of Porto Porto Portugal

ISBN: (数字)9798331531751

ISBN: (纸本)9798331531768

In collaborative energy communities, optimizing energy costs is a critical aspect of sustainable management. This article explores the potential benefits of applying clustering algorithms to vary retail tariffs monthly, aiming to reduce energy bills for the community as a whole. The article compares a traditional approach of applying the same tariff to all community members throughout the year with a novel approach of dynamically changing tariffs based on monthly clustering results. A case study is conducted, wherein energy bill costs per month are analyzed under different tariff scenarios utilizing k-means clustering. Results indicate that the proposed approach yields promising reductions in energy costs, up to 8.76% (1170.18 EUR) improvement compared to the traditional method. The study contributes valuable insights into the practical application of clustering in energy community management and highlights the potential for significant cost savings through dynamic tariff adjustments.

关键词： Costs Accuracy Heuristic algorithms Switching frequency Tariffs Clustering algorithms Switches Power systems intelligent systems Optimization

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Knowledge Retrieval Mechanism for Smart Buildings Based on IoT Devices Data 1

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9th International Conference on Energy and Environment research, ICEER 2022

作者： Teixeira, Nuno Gomes, Luis Vale, Zita 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 Porto4200-072 Portugal

ISBN: (数字)9783031435591

ISBN: (纸本)9783031435584

The use of smart building solutions can bring advantages to both a building and its users. The main motivation of this work is to propose a solution based on internet of things devices to retrieve knowledge from historic data and, using the knowledge gained, contribute to the comfort of the user and the building’s sustainability. The use of internet of things devices enables the easy deployment of the proposed system in today’s buildings, where this kind of devices are commonly found. The knowledge regarding the role of each device and how they interact or impact each other will be studied by the proposed solutions that will deploy a continuous mechanism. The designed software architecture was developed in python, to allow easy implementation of different features. To test the proposed solution, it was used real data from a research building where several types of sensors were correlated to assess relations among data. The results show significant correlations between celling light consumption and the room’s light intensity sensor, and between the operation of air conditioning units and the room’s temperature, for example, data from 2 months, regarding air conditioner unit in room N102, demonstrates a − 0.85 correlation with the room temperature, when operating in cooling mode. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Internet of things

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Using simulation to evaluate a concept drift detector for condition based maintenance 48

Using simulation to evaluate a concept drift detector for co...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Lourenco, Afonso Fernandes, Marta Marreiros, Goreti Corchado, Juan Manuel Gecad-Res. Group on Intelligent Engineering and Computing for Advanced Innovation and Development Porto Portugal Bisite-Research Group on Bioinformatics Intelligent Systems and Educational Technology Salamanca Spain

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

Industry 4.0. has empowered the integration of real-time sensor readings and process knowledge for early identification of faults. In this paper, simulation was used to evaluate the process of classifying data streams as drifting towards defective behavior. Focusing on the challenges imposed by real-word, unlabeled and non-stationary data, the proposed method was assessed by conducting a case-study in the plastic extrusion domain. The knowledge gained from initial tests on generated synthetic data sets was used to isolate symptoms of a clogged screen pack from the concept drifts detected by the Adaptive Windowing algorithm. Experimental results for 40 days of running time of an extruder show that the proposed method helped in identifying a defective screen pack state upon the appearance of defects in the extrudate. © 2022 IEEE.

关键词： Industry 4.0

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A Novel Approach to Network Traffic Analysis: the HERA tool

A Novel Approach to Network Traffic Analysis: the HERA tool

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IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Daniela Pinto Ivone Amorim Eva Maia Isabel Praça Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD) Porto School of Engineering Polytechnic of Porto (ISEP-IPP) Porto Portugal Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD) PORTIC – Porto Research Technology & Innovation Center Polytechnic of Porto (IPP) Porto Portugal

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Cybersecurity threats highlight the need for robust network intrusion detection systems to identify malicious behaviour. These systems rely heavily on large datasets to train machine learning models capable of detecting patterns and predicting threats. In the past two decades, researchers have produced a multitude of datasets, however, some widely utilised recent datasets generated with CICFlowMeter contain inaccuracies. These result in flow generation and feature extraction inconsistencies, leading to skewed results and reduced system effectiveness. Other tools in this context lack ease of use, customizable feature sets, and flow labelling options. In this work, we introduce HERA, a new open-source tool that generates flow files and labelled or unlabelled datasets with user-defined features. Validated and tested with the UNSW-NB15 dataset, HERA demonstrated accurate flow and label generation.

关键词： Data privacy Accuracy Network intrusion detection Telecommunication traffic Machine learning Predictive models Feature extraction Labeling Computer security

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Flexibility Provision from Residential Houses: a Sensitive Analysis on Net Consumption Limits

Flexibility Provision from Residential Houses: a Sensitive A...

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Mediterranean Electrotechnical Conference (MELECON)

作者： Ricardo Faia Pedro Faria Luis Gomes Zita Vale Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD) Intelligent Systems Associate Laboratory (LASI) Porto Portugal

ISBN: (数字)9798350387025

ISBN: (纸本)9798350387032

This paper explores the potential for flexibility provision a residential house equipped with 12 different loads, integrated with a battery storage system and photovoltaic panels. The study employs a sensitive analysis framework to examine the net consumption limits associated with total flexibility provision throughout different periods of the day. By incorporating renewable energy sources such as PV panels and energy storage via batteries, the research addresses the load management in modern households. The use of shifting properties to the loads enhances the potential for flexibility provision. The findings offer valuable insights into optimizing energy consumption, balancing loads, and improving the overall efficiency of residential energy systems. This work contributes to the advancement of smart grid technologies, sustainable energy practices, and the integration of renewable sources in residential settings.

关键词： Photovoltaic systems Renewable energy sources Costs Load management Linear programming Mathematical models Batteries

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Federated Genetic Programming: A Study About the Effects of Non-IID and Federation Size 20th

Federated Genetic Programming: A Study About the Effects of ...

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

作者： Ribeiro, Bruno Gomes, Luis Faia, Ricardo Vale, Zita GECAD Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI—Intelligent Systems Associate Laboratory Polytechnic of Porto R. Dr. António Bernardino de Almeida 431 Porto4200-072 Portugal

ISBN: (纸本)9783031383328

The privacy and security of users’ data have been a concern in the last few years. New techniques like federated learning have appeared to allow the training of machine learning models without sharing the users’ personal data. These systems have a lot of variables that can change the outcome of the models. Studies have been made to explore the effect of these variables and their importance. However, those studies only focus on machine learning models, namely deep learning. This paper explores the impact of the federation size in unbalanced data using a genetic programming model for image classification. The results show that the federation size can have impact on the contributions of each client, and the dataset size can influence the quality of the individuals. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Image classification

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