Lighting Consumption Optimization in a SCADA Model of Office Building Considering User Comfort Level

Due to the high penetration of the buildings in energy consumption, the use of optimization algorithms plays a key role. Therefore, all the producers and prosumers should be equipped with the automation infrastructures as well as intelligent decision algorithms, in order to perform the management programs, like demand response. This paper proposes a multi-period optimization algorithm implemented in a multi-agent Supervisory Control and Data Acquisition system of an office building. The algorithm optimizes the lighting power consumption of the building considering the user comfort constraints. A case study is implemented in order to validate and survey the performance of the implemented optimization algorithm using real consumption data of the building. The outcomes of the case study show the great impact of the user comfort constraints in the optimization level by respect to the office user’s preferences.The present work was done and funded in the scope of the following projects: COLORS Project PTDC/EEI-EEE/28967/2017 and UID/EEA/00760/2019 funded by FEDER Funds through COMPETE program and by National Funds through FCT.info:eu-repo/semantics/publishedVersio

Automated in situ consolidation process for pre-impregnated carbon fibers: a cyber phisycal approach

The concept known as the fourth industrial revolution (Industry 4.0) has developed a wide field of study, such as the Internet of Things (IoT), Cyber-Physical Systems (CPS) and Information and Communication Technology (ICT), with the objective to achieve enhancing the processes performance of automated systems. This work focuses on the field of CPS for an in situ consolidation machine, which handles preimpregnated, in this case unidirectional carbon fiber tapes with polyamide 6, in order to create composite laminates. The physical model involves an unwinding system that feeds the tape into a turning table through a pressing mechanism, which has a radiation heater before and refrigeration after the presser to guarantee the composite temperature. The objective is to model and simulate the heating process before, during and after the pressing mechanism to feed an optimization algorithm in order to calculate the optimal working parameters such as heaters power, pressing force and process speed taking into account the temperatures of the tape, to avoid overheating and to improve process time. The results are processed, so that they can send and receive data constantly, to an optimization system to alter parameters, without making programing code changes and avoiding the need to stop the consolidation. The CPS can handle different classes of pre-impregnated composites, by learning during the consolidation process, in order to improve the overall result, without the need of fully or partially reprograming the code, which a main characteristic of CPS

Energy Resource Scheduling in an Agriculture System Using a Decision Tree Approach

The present work was done and funded in the scope of project: Eco Rural IoT (TETRAMAX-VALUECHAIN-TTX-1), CEECIND/02887/2017, and UID/EEA/00760/2019 funded by FEDER Funds through COMPETE and by National Funds through FCT.Agriculture sector is backbone of each country. Nowadays the energy efficiency in this sector is at a very low level, which shows the necessity of more investments in this regard. By appearance of smart grid technologies, some new concepts were also appeared in the agriculture sector, such as smart farm, and smart agriculture. This paper provides an energy management system for an agriculture field equipped with renewable energy resources and a river turbine. A decision tree is developed in this paper to schedule and optimize the use of energy resources for reducing the electricity costs. Decision tree method enables the system to obtain optimal scheduling of energy resources in offline mode, without using any external server/machine or internet access. A case study validates the performance of developed decision tree, and the errors and accuracy of all gained results are discussed.The present work was done and funded in the scope of project: Eco Rural IoT (TETRAMAX-VALUECHAIN-TTX-1), CEECIND/02887/2017, and UID/EEA/00760/2019 funded by FEDER Funds through COMPETE and by National Funds through FCTinfo:eu-repo/semantics/publishedVersio

Clustering Direct Load Control Appliances in the Context of Demand Response Programs in Energy Communities

The demand response program explained in this article is designed to be implemented in communities seeking to achieve a self-sustaining system, namely through renewable energy such as photovoltaic energy. This article, through concepts such as prosumer and clustering, aims to make the most efficient management of the resources provided by the energy community. The developed demand response clusters the different consumers who have the same type of consumption throughout the day. That is, it brings together those whose behavior of the respective loads resemble each other and can be viewed from the perspective of an individual load or even clustered with one or more loads. The study comprises three villages with different numbers of consumers and charges, where, through their participation, it is estimated that there are reductions in electricity bills and, for those who collaborated for the study, it is attributed a remuneration according to their performance.This work has received funding from Portugal 2020 under SPEAR project (NORTE - 01 - 0247 - FEDER - 040224) and from FEDER Funds through COMPETE program and from National Funds through (FCT) under the project UIDB/00760/2020, CEECIND/02887/2017, and SFRH/BD/144200/2019N/

Scalable Model-Based Management of Massive High Frequency Wind Turbine Data with ModelarDB

Modern wind turbines are monitored by sensors that generate massive amounts of time series data that is ingested on the edge before it is transferred to the cloud where it is stored and queried. This results in at least four challenges: 1) High frequency time series data must be ingested on limited hardware fast enough to keep up with the generation; 2) Limited bandwidth makes it impossible to transfer the data without compression; 3) High storage costs when data is stored; and 4) Low data quality to unbounded lossy compression methods commonly used by practitioners. Practitioners currently use solutions that only solve some of these challenges. In this paper, we evaluate a solution for the entire pipeline based on the Time Series Management System ModelarDB that addresses all four challenges efficiently. With ModelarDB, the user can exploit both lossless and error-bounded lossy compression. We evaluate the solution in a realistic edge-to-cloud scenario with real-world data under different aspects. For lossless compression, ModelarDB achieves up to 1.5x better compression and 1.2x better transfer efficiency than lossless solutions commonly used by practitioners. For lossy compression, ModelarDB offers significant compression comparable to a lossy method commonly used by practitioners today. However, ModelarDB has orders of magnitude smaller errors

Demand Response in Energy Communities Considering the Share of Photovoltaic Generation from Public Buildings

This paper has as ambit to promote the importance of the prosumer and the sustainable development of a community's energy systems through the aid of the incorporation of renewable energy sources in the market and the concept demand response. Moreover, it is intended to efficiently use the energy surplus produced by the photovoltaic panels of the prosumers for self-consumption, distributed by the remaining members of the community. It is estimated that participants, through the energy management of the community, will be able to verify reductions in electricity bills, as well as be compensated for their contribution to demand response through remuneration. Thus, the proposed methodology contributes in an efficient and sustainable way to be implemented in a community, promoting the use of renewable energy.This work has received funding from the European Union's Horizon 2020 research and innovation programme under project DOMINOES (grant agreement No 771066) and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2019.info:eu-repo/semantics/publishedVersio

An agent-based industrial cyber-physical system deployed in an automobile multi-stage production system

Industrial Cyber-Physical Systems (CPS) are promoting the development of smart machines and products, leading to the next generation of intelligent production systems. In this context, Artificial Intelligence (AI) is posed as a key enabler for the realization of CPS requirements, supporting the data analysis and the system dynamic adaptation. However, the centralized Cloud-based AI approaches are not suitable to handle many industrial scenarios, constrained by responsiveness and data sensitivity. Edge Computing can address the new challenges, enabling the decentralization of data analysis along the cyber-physical components. In this context, distributed AI approaches such as those based on Multi-agent Systems (MAS) are essential to handle the distribution and interaction of the components. Based on that, this work uses a MAS approach to design cyber-physical agents that can embed different data analysis capabilities, supporting the decentralization of intelligence. These concepts were applied to an industrial automobile multi-stage production system, where different kinds of data analysis were performed in autonomous and cooperative agents disposed along Edge, Fog and Cloud computing layers. © 2020, Springer Nature Switzerland AG.info:eu-repo/semantics/publishedVersio

NOVA mobility assistive system: Developed and remotely controlled with IOPT-tools

UID/EEA/00066/2020In this paper, a Mobility Assistive System (NOVA-MAS) and a model-driven development approach are proposed to support the acquisition and analysis of data, infrastructures control, and dissemination of information along public roads. A literature review showed that the work related to mobility assistance of pedestrians in wheelchairs has a gap in ensuring their safety on road. The problem is that pedestrians in wheelchairs and scooters often do not enjoy adequate and safe lanes for their circulation on public roads, having to travel sometimes side by side with vehicles and cars moving at high speed. With NOVA-MAS, city infrastructures can obtain information regarding the environment and provide it to their users/vehicles, increasing road safety in an inclusive way, contributing to the decrease of the accidents of pedestrians in wheelchairs. NOVA-MAS not only supports information dissemination, but also data acquisition from sensors and infrastructures control, such as traffic light signs. For that, it proposed a development approach that supports the acquisition of data from the environment and its control while using a tool framework, named IOPT-Tools (Input-Output Place-Transition Tools). IOPT-Tools support controllers’ specification, validation, and implementation, with remote operation capabilities. The infrastructures’ controllers are specified through IOPT Petri net models, which are then simulated using computational tools and verified using state-space-based model-checking tools. In addition, an automatic code generator tool generates the C code, which supports the controllers’ implementation, avoiding manual codification errors. A set of prototypes were developed and tested to validate and conclude on the feasibility of the proposals.publishersversionpublishe