@article{DBLP:journals/tii/BousbiatFKPE23,

title = {Unlocking the Full Potential of Neural NILM: On Automation, Hyperparameters, 

 and Modular Pipelines},

author = {Hafsa Bousbiat and Anthony Faustine and Christoph Klemenjak and Lucas Pereira and Wilfried Elmenreich},

year  = {2023},

date = {2023-01-01},

journal = {IEEE Trans. Ind. Informatics},

volume = {19},

number = {5},

pages = {7002--7010},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{velosa2023procsim,

title = {PROCSIM: An Open-Source Simulator to Generate Energy Community Power Demand and Generation Scenarios},

author = {Nuno Velosa and Eduardo Gomes and Hugo Morais and Lucas Pereira},

year  = {2023},

date = {2023-01-01},

journal = {Energies},

volume = {16},

number = {4},

pages = {1611},

publisher = {MDPI},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Sensors2022a,

title = {A Data-Centric Analysis of the Impact of Non-Electric Data on the 

 Performance of Load Disaggregation Algorithms},

author = {João Gois and Lucas Pereira and Nuno Nunes},

url = {https://doi.org/10.3390/s22186914},

doi = {10.3390/s22186914},

year  = {2022},

date = {2022-12-01},

journal = {Sensors},

volume = {22},

number = {18},

pages = {6914},

abstract = {Recent research on non-intrusive load monitoring, or load disaggregation, suggests that the performance of algorithms can be affected by factors beyond energy data. In particular, by incorporating non-electric data in load disaggregation analysis, such as building and consumer characteristics, the estimation accuracy of consumption data may be improved. However, this association has rarely been explored in the literature. This work proposes a data-centric methodology for measuring the effect of non-electric characteristics on load disaggregation performance. A real-world dataset is considered for evaluating the proposed methodology, using various appliances and sample rates. The methodology results indicate that the non-electric characteristics may have varying effects on the performances of different building appliances. Therefore, the proposed methodology can be relevant for complementing load disaggregation analysis.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/tsg/FaustineP22,

title = {FPSeq2Q: Fully Parameterized Sequence to Quantile Regression for Net-Load 

 Forecasting With Uncertainty Estimates},

author = {Anthony Faustine and Lucas Pereira},

year  = {2022},

date = {2022-01-01},

journal = {IEEE Trans. Smart Grid},

volume = {13},

number = {3},

pages = {2440--2451},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/widm/AbreuP22,

title = {Privacy protection in smart meters using homomorphic encryption: An 

 overview},

author = {Zita Abreu and Lucas Pereira},

year  = {2022},

date = {2022-01-01},

journal = {WIREs Data Mining Knowl. Discov.},

volume = {12},

number = {4},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{gomes2022impact,

title = {Impact of forecasting models errors in a peer-to-peer energy sharing market},

author = {Luis Gomes and Hugo Morais and Calvin Gonçalves and Eduardo Gomes and Lucas Pereira and Zita Vale},

year  = {2022},

date = {2022-01-01},

journal = {Energies},

volume = {15},

number = {10},

pages = {3543},

publisher = {MDPI},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/data/PereiraAV21,

title = {FIKWaste: A Waste Generation Dataset from Three Restaurant Kitchens 

 in Portugal},

author = {Lucas Pereira and Vitor Aguiar and Fábio Vasconcelos},

year  = {2021},

date = {2021-01-01},

journal = {Data},

volume = {6},

number = {3},

pages = {25},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/data/PereiraAV21a,

title = {FIKWater: A Water Consumption Dataset from Three Restaurant Kitchens 

 in Portugal},

author = {Lucas Pereira and Vitor Aguiar and Fábio Vasconcelos},

year  = {2021},

date = {2021-01-01},

journal = {Data},

volume = {6},

number = {3},

pages = {26},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/tsg/FaustinePK21,

title = {Adaptive Weighted Recurrence Graphs for Appliance Recognition in Non-Intrusive 

 Load Monitoring},

author = {Anthony Faustine and Lucas Pereira and Christoph Klemenjak},

year  = {2021},

date = {2021-01-01},

journal = {IEEE Trans. Smart Grid},

volume = {12},

number = {1},

pages = {398--406},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/access/GomesP20,

title = {PB-NILM: Pinball Guided Deep Non-Intrusive Load Monitoring},

author = {Eduardo Gomes and Lucas Pereira},

year  = {2020},

date = {2020-01-01},

journal = {IEEE Access},

volume = {8},

pages = {48386--48398},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/corr/abs-2002-04177,

title = {Towards Phase Balancing using Energy Storage},

author = {Md Umar Hashmi and José Horta and Lucas Pereira and Zachary Lee and Ana Busic and Daniel Kofman},

year  = {2020},

date = {2020-01-01},

journal = {CoRR},

volume = {abs/2002.04177},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/corr/abs-2004-08770,

title = {Energy Storage Optimization for Grid Reliability},

author = {Md Umar Hashmi and Deepjyoti Deka and Lucas Pereira and Ana Busic},

year  = {2020},

date = {2020-01-01},

journal = {CoRR},

volume = {abs/2004.08770},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/nca/TorabiRCPQND20,

title = {A global monitoring system for electricity consumption and production 

 of household roof-top PV systems in Madeira},

author = {Roham Torabi and Sandy Rodrigues and Nuno Cafôfo and Lucas Pereira and Filipe Quintal and Nuno Nunes and Fernando Morgado Dias},

year  = {2020},

date = {2020-01-01},

journal = {Neural Comput. Appl.},

volume = {32},

number = {20},

pages = {15835--15844},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Pereira2019,

title = {Understanding the practical issues of deploying energy monitoring and eco-feedback technology in the wild: Lesson learned from three long-term deployments},

author = {Lucas Pereira and Nuno Nunes},

url = {http://www.sciencedirect.com/science/article/pii/S2352484719302689},

doi = {10.1016/j.egyr.2019.11.025},

issn = {2352-4847},

year  = {2019},

date = {2019-12-25},

journal = {Energy Reports},

volume = {6},

pages = {41-56},

abstract = {This paper reports on the different engineering, social and financial challenges behind the building and deploying electric energy monitoring and eco-feedback technology in real-world scenarios, which despite being relevant to the research community are seldom reported in the literature. The objectives of this paper are two-fold: First, discuss the technical and social constraints of real-world deployments. This includes, for example, hardware and software requirements, and issues related to security and intrusiveness of the monitoring solutions. Second, identify and understand the costs associated with developing and deploying such systems. These include hardware costs and consumed energy. To this end, we rely on over five years of experience developing and improving a non-intrusive energy monitoring research platform to enable the deployment of long and short-term studies of eco-feedback technology. During this time, two versions of that platform were deployed in 50 homes for periods that lasted between 6 and 18 consecutive months. By iteratively developing and deploying our sensing and eco-feedback infrastructures, we managed to build upon previous findings and lessons learned to understand how to create, deploy, and maintain such systems. Concurrently, we gained insights regarding what are some of the most relevant costs associated with running such experiments.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/data/PereiraVP19,

title = {dsCleaner: A Python Library to Clean, Preprocess and Convert Non-Instrusive 

 Load Monitoring Datasets},

author = {Manuel Pereira and Nuno Velosa and Lucas Pereira},

year  = {2019},

date = {2019-01-01},

journal = {Data},

volume = {4},

number = {3},

pages = {123},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/data/Pereira19,

title = {NILMPEds: A Performance Evaluation Dataset for Event Detection Algorithms 

 in Non-Intrusive Load Monitoring},

author = {Lucas Pereira},

year  = {2019},

date = {2019-01-01},

journal = {Data},

volume = {4},

number = {3},

pages = {127},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/corr/abs-1903-06132,

title = {Arbitrage with Power Factor Correction using Energy Storage},

author = {Md Umar Hashmi and Deepjyoti Deka and Ana Busic and Lucas Pereira and Scott Backhaus},

year  = {2019},

date = {2019-01-01},

journal = {CoRR},

volume = {abs/1903.06132},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/corr/abs-1904-00463,

title = {Energy Storage in Madeira, Portugal: Co-optimizing for Arbitrage, 

 Self-Sufficiency, Peak Shaving and Energy Backup},

author = {Md Umar Hashmi and Lucas Pereira and Ana Busic},

year  = {2019},

date = {2019-01-01},

journal = {CoRR},

volume = {abs/1904.00463},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10645,

title = {Implementation Strategy of Convolution Neural Networks on Field Programmable Gate Arrays for Appliance Classification Using the Voltage and Current (V-I) Trajectory},

author = {Dario Baptista and Sheikh Mostafa and Lucas Pereira and Leonel Sousa and Dias F. Morgado},

url = {http://www.mdpi.com/1996-1073/11/9/2460},

doi = {10.3390/en11092460},

issn = {1996-1073},

year  = {2018},

date = {2018-09-01},

journal = {Energies},

volume = {11},

pages = {2460},

abstract = {
Specific information about types of appliances and their use in a specific time window could help determining in details the electrical energy consumption information. However, conventional main power meters fail to provide any specific information. One of the best ways to solve these problems is through non-intrusive load monitoring, which is cheaper and easier to implement than other methods. However, developing a classifier for deducing what kind of appliances are used at home is a difficult assignment, because the system should identify the appliance as fast as possible with a higher degree of certainty. To achieve all these requirements, a convolution neural network implemented on hardware was used to identify the appliance through the voltage and current (V-I) trajectory. For the implementation on hardware, a field programmable gate array (FPGA) was used to exploit processing parallelism in order to achieve optimal performance. To validate the design, a publicly available Plug Load Appliance Identification Dataset (PLAID), constituted by 11 different appliances, has been used. The overall average F-score achieved using this classifier is 78.16% for the PLAID 1 dataset. The convolution neural network implemented on hardware has a processing time of approximately 5.7 ms and a power consumption of 1.868 W.
},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10640,

title = {Performance evaluation in non-intrusive load monitoring: Datasets, metrics, and tools—A review},

author = {Lucas Pereira and Nuno Nunes},

url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/widm.1265},

doi = {10.1002/widm.1265},

issn = {1942-4795},

year  = {2018},

date = {2018-05-01},

journal = {Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery},

abstract = {
Non-intrusive load monitoring (also known as NILM or energy disaggregation) is the process of estimating the energy consumption of individual appliances from electric power measurements taken at a limited number of locations in the electric distribution of a building. This approach reduces sensing infrastructure costs by relying on machine learning techniques to monitor electric loads. However, the ability to evaluate and benchmark the proposed approaches across different datasets is key for enabling the generalization of research findings and consequently contributes to the large-scale adoption of this technology. Still, only recently researchers have focused on creating and standardizing the existing datasets in order to deliver a single interface to run NILM evaluations. Furthermore, there is still no consensus regarding, which performance metrics should be used to measure and report the performance of NILM systems and their underlying algorithms. This paper provides a review of the main datasets, metrics, and tools for evaluating the performance of NILM systems and technologies. Specifically, we review three main topics: (a) publicly available datasets, (b) performance metrics, and (c) frameworks and toolkits. The review suggests future research directions in NILM systems and technologies, including cross-datasets, performance metrics for evaluation and generalizable frameworks for benchmarking NILM technology. This article is categorized under: Application Areas > Science and Technology Application Areas > Data Mining Software Tools Technologies > Computational Intelligence Technologies > Machine Learning
},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10647,

title = {Highlights of ES2DE and IWOBI 2017: extended versions of selected best papers},

author = {Lucas Pereira and A. Ravelo-Garcia},

url = {https://rdcu.be/7UPR},

doi = {https://doi.org/10.1007/s00607-018-0660-3},

year  = {2018},

date = {2018-01-01},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/computing/PereiraR18,

title = {Highlights of ES2DE and IWOBI 2017: extended versions of selected 

 best papers},

author = {Lucas Pereira and Antonio G. Ravelo-García},

year  = {2018},

date = {2018-01-01},

journal = {Computing},

volume = {100},

number = {11},

pages = {1133--1135},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/widm/PereiraN18,

title = {Performance evaluation in non-intrusive load monitoring: Datasets, 

 metrics, and tools - A review},

author = {Lucas Pereira and Nuno Nunes},

year  = {2018},

date = {2018-01-01},

journal = {WIREs Data Mining Knowl. Discov.},

volume = {8},

number = {6},

keywords = {},

pubstate = {published},

tppubtype = {article}

}