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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...

引用

2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

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

ISBN: (纸本)9798350396782

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. © 2023 IEEE.

关键词： Battery storage systems Minimize Costs Shift appliances

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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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2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

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

ISBN: (纸本)9798350396782

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 CO2 emissions and the energy cost by 50% and 73%, respectively, as well increasing the energy community sustainability by 10%. © 2023 IEEE.

关键词： Active Participation Clustering Demand Response Energy Community Energy Flexibility

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Detection of Human Falls via Computer Vision for Elderly Care – An I3D/RNN Approach 1

引用

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

作者： Leal, João Moayyed, Hamed Vale, Zita Porto Portugal GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI - Intelligent Systems Associate Laboratory Polytechnic of Porto Porto Portugal

ISBN: (数字)9783031383335

ISBN: (纸本)9783031383328

As the population continues to age, the number of elderly individuals is increasing at an unprecedented rate as the active age group in developed countries continues to shrink. This demographic shift has resulted in a shortage of resources and limitations in the provision of adequate elderly care. Among the many health risks faced by the elderly, falls are a common and serious problem, which can lead to injuries, hospitalizations, and even death. Despite the prevalence of falls, traditional manual detection methods are often unreliable and inadequate. In response to this, this paper proposes an automatic fall detection system based on video cameras that uses a unique combination of an inflated 3D convolutional neural network (I3D) and a recurrent neural network (RNN). This model was evaluated using multiple fall detection datasets, including a newly developed dataset of simulated falls. The results of this study demonstrate that this hybrid model is highly effective at detecting falls and is on par with other state-of-the-art systems, boasting 94% F1 score and 96% recall values. This technology has the potential to significantly improve the quality of life and safety of the elderly population by providing timely and accurate detection of falls. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Computer vision

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A Smart Contract-Based Peer-to-Peer Energy Trading System 6th

A Smart Contract-Based Peer-to-Peer Energy Trading System

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6th International Congress on Blockchain and Applications, BLOCKCHAIN 2024

作者： Abbasi, Mahmoud Faria, Pedro Gomes, Luis Prieto, Javier Vale, Zita BISITE Research Group University of Salamanca Salamanca37007 Spain 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 Porto4249-015 Portugal

ISBN: (纸本)9783031819278

The transition toward sustainable and decentralized energy systems has prompted the emergence of Peer-to-Peer (P2P) energy trading systems, challenging traditional centralized models. This paper proposes a smart contract-based P2P energy trading system to foster a resilient and sustainable energy ecosystem. By integrating blockchain technology with P2P energy trading, we introduce trust, security, and transparency into energy transactions. Our solution employs an auction-based mechanism and blockchain oracles to address standardization challenges and enhance the accuracy of energy-related data. Additionally, an advanced identity management system ensures secure access to the trading system. Through smart contracts, energy producers and consumers engage directly in transparent and decentralized transactions. Despite challenges such as regulatory uncertainties and scalability concerns, our solution offers a promising alternative for building a more inclusive and sustainable energy future. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Smart contract

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Retrieval-Augmented Generation Based Assistant: A Smart Home Case Study 23rd

Retrieval-Augmented Generation Based Assistant: A Smart Home...

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23rd EPIA Conference on Artificial Intelligence, EPIA 2024

作者： Ribeiro, Bruno 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 Porto4249-015 Portugal

ISBN: (纸本)9783031734991

Smart homes are starting to become more present in our daily lives. Smart homes are useful for enabling energy management services and reducing monetary costs of the consumers. Despite the increasing research in these aspects of the smart home, few actually have in mind the interactivity of the system they propose. Therefore, this paper, an extension of a previous work, proposes a new human-machine interface based on multi-agent systems and natural language models with retrieval-augmented generation techniques for smart homes to facilitate the consumers adaptation to this type of systems. The assistant enables the user to control the house and provides him with the information he needs, even when a problem within the system occurs. Through the experiment the assistant showed to be flexible, able to provide answers to questions outside of the smart home system domain and it increases the scalability of the system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Smart homes

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Benchmarking Large Language Models for Multi-agent systems: A Comparative Analysis of AutoGen, CrewAI, and TaskWeaver 22nd

Benchmarking Large Language Models for Multi-agent Systems: ...

引用

22nd International Conference on Practical Applications of Agents and Multi-Agent systems, PAAMS 2024

作者： Barbarroxa, Rafael 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 R. Dr. António Bernardino de Almeida 431 Porto4249-015 Portugal

ISBN: (纸本)9783031704147

This paper presents the benchmarking of three multi-agent systems powered by large language models. The paper presents a comparative analysis of AutoGen, CrewAI, and TaskWeaver. Nowadays, large language models have emerged as powerful tools able to assist users in various areas. The integration of large language models into multi-agent systems increases their potential for collaborative problem-solving. This study focuses on a case study involving a machine learning code generation task which is used to evaluate the framework’s performance. To assess the performance of the solutions, it is requested to create energy forecasting models using the same dataset as the base. After producing the code, a new dataset is used to test the model performance using the root mean square error. The three solutions were able to provide results using multiple large language models. The best result was achieved by TaskWeaver using GPT-3.5, with an error of 25.04. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Multi agent systems

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DR Participants’ Actual Response Prediction Using Artificial Neural Networks 17th

DR Participants’ Actual Response Prediction Using Artificia...

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17th International Conference on Soft computing Models in Industrial and Environmental Applications, SOCO 2022

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

ISBN: (纸本)9783031180491

Empowering the consumers will increase the complexity of local communities’ management. Enabling bidirectional communication and appliances to become smarter can be a huge step toward implementing demand response. However, a solution capable of providing the right knowledge and tools must be developed. The authors thereby propose a methodology to manage the active consumers on Demand Response (DR) events optimally, considering the context in which it is triggered. The distribution system operator detects a voltage violation and requests a load reduction to the aggregators. In this study, to test a performance rate designed by the authors to deal with response uncertainty, a comparison between requested and actual reduction is done. The proposed methodology was applied to three scenarios where the goal is predicting the response from the consumers using artificial neural networks, by changing the features used in the input. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Active learning

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Caravels: A Decentralized Container-Based Infrastructure for Sustainable Human-Centric Intelligent Energy Communities 19th

Caravels: A Decentralized Container-Based Infrastructure for...

引用

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

作者： Silva, Rafael 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 R. Dr. António Bernardino de Almeida 431 Porto4200-072 Portugal

ISBN: (纸本)9783031750120

As the European Union sets a target to increase renewable energy consumption by 2030, progressing towards a decentralized energy production becomes more a (greater?) priority. This can be achieved with initiatives such as energy communities, which when combined with active consumer participation and the integration of IoT devices and Cloud computing technologies in households can enhance energy management, however, it also introduces privacy concerns. Single-board computers are cost-effective computing solutions, but in turn face challenges with scalability and reliability. To address these issues, we propose Caravels, a distributed solution that leverages container applications for intelligent energy communities. Caravels promotes direct peer-to-peer communication among users, enabling real-time applications and collaborative deployments while ensuring user data privacy and system scalability. We conducted a case study that implements Caravels across Raspberry Pi nodes, demonstrating its feasibility and adaptability. The results indicate Caravels’ promising potential to empower households towards sustainable energy management and overcoming technological constraints and privacy concerns. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Scalability

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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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CPU Computation Influence on Energy Consumption Forecasting Activities of a Building 17th

CPU Computation Influence on Energy Consumption Forecasting ...

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17th International Conference on Soft computing Models in Industrial and Environmental Applications, SOCO 2022

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

ISBN: (纸本)9783031180491

Scheduling forecasting activities and improving the forecasting accuracy is important to deliver energy efficiency to the customers. However, it is also important to reduce the computational effort dedicated to these forecasting activities to ensure more effective environment sustainability. This paper proposes two forecasting algorithms known as artificial neural networks and k-nearest neighbors to anticipate energy patterns of a building monitoring data from five-to-five minutes. Using a case study with an annual historic and one week test, different scenarios are defined to test the forecasting activities with both higher and lower computational effort. It is achieved to ensure energy predictions with above reasonable accuracies evaluations while decreasing the computational effort, and the respective energy consumption, dedicated to forecasting activities. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： k-nearest neighbors

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