Suchergebnisse

People with type 1 diabetes must control their blood glucose level through insulin infusion either with several daily injections or with an insulin pump. However, estimating the required insulin dose is not easy. Recommender systems, mainly based on Case-Based Reasoning (CBR), are being developed to provide recommendations to users. These systems are designed to keep the experiences or cases of the user in a case-base, which requires maintenance to keep system's response accurate and efficient. This paper proposes a case-base maintenance methodology that combines case-base redundancy reduction and attribute weight learning. Contrary to previous approaches designed for classification problems, the maintenance methodology presented in this paper deals with numerical recommendations. It can manage a potentially huge case-base due to the combinatorial derived from the number of attributes used to represent a case. The proposed approach has been tested using the UVA/PADOVA type 1 diabetes simulator and the results demonstrate that it can accomplish better levels of accuracy than other insulin recommender systems mentioned in the literature, when a large number of attributes is considered ; This project has received funding from the European Union Horizon 2020 research and innovation programme under grant agreement No. 689810, www.pepper.eu.com/, PEPPER, and the grant of the University of Girona 20162018 (MPCUdG2016). The work has been developed with the support of the research group SITES awarded with distinction by the Generalitat de Catalunya (SGR 20142016)

The paper describes an ongoing work to embed several services in a Smart City architecture with the aim of achieving a sustainable city. In particular, the main goal is to identify services required in such framework to define the requirements and features of a reference architecture to support the data-driven methods for energy efficiency monitoring or load prediction. With this object in mind, a use case of short-term load forecasting in non-residential buildings in the University of Girona is provided, in order to practically explain the services embedded in the described general layers architecture. In the work, classic data-driven models for load forecasting in buildings are used as an example ; This research project has been partially funded through BR-UdGScholarship of the University of Girona granted to Joaquim MassanaRaurich. Work developed with the support of the research groupSITES awarded with distinction by the Generalitat de Catalunya(SGR 2014–2016), the MESC project funded by the Spanish MINECO (Ref. DPI2013-47450-C2-1-R) and the European Union's Horizon2020 Research and Innovation Programme under grant agreementNo 680708

The electric grid is evolving. Smart grids and demand response systems will increase the performance of the grid in terms of cost efficiency, resilience and safety. Accurate load forecasting is an important issue in the daily operation and control of a power system. A suitable short term load forecasting will enable a utility provider to plan the resources and also to take control measures to balance the supply and demand of electricity. The aim of this paper is to create a method to forecast the electric load in a non-residential building. Another goal is to analyse what kind of data, as weather, indoor ambient, calendar and building occupancy, is the most relevant in building load forecasting. A simple method, tested with three different models, such as MLR, MLP and SVR, is proposed. The results, from a real case study in the University of Girona, show that the proposed forecast method has high accuracy and low computational cost ; This research has been partially supported by the Spanish Government project MESC (Ref. DPI2013-47450-C2-1-R). Also we would like to thank the Department of Physics and acknowledge the technical assistance and maintenance service of the UdG (SOTIM) which provided the weather and consumption data respectively. The authors belong to the 'Smart IT Engineering and Solutions' accredited research group (Generalitat de Catalunya, 2014 SGR 1052)

The electric grid is evolving. Smart grids and demand response systems will increase the performance of the grid in terms of cost efficiency, resilience and safety. Accurate load forecasting is an important issue in the daily operation and control of a power system. A suitable short term load forecasting will enable a utility provider to plan the resources and also to take control measures to balance the supply and demand of electricity. The aim of this paper is to create a method to forecast the electric load in a non-residential building. Another goal is to analyse what kind of data, as weather, indoor ambient, calendar and building occupancy, is the most relevant in building load forecasting. A simple method, tested with three different models, such as MLR, MLP and SVR, is proposed. The results, from a real case study in the University of Girona, show that the proposed forecast method has high accuracy and low computational cost ; This research has been partially supported by the Spanish Government project MESC (Ref. DPI2013-47450-C2-1-R). Also we would like to thank the Department of Physics and acknowledge the technical assistance and maintenance service of the UdG (SOTIM) which provided the weather and consumption data respectively. The authors belong to the 'Smart IT Engineering and Solutions' accredited research group (Generalitat de Catalunya, 2014 SGR 1052)

The electric grid is evolving. Smart grids and demand response systems will increase the performance of the grid in terms of cost efficiency, resilience and safety. Accurate load forecasting is an important issue in the daily operation and control of a power system. A suitable short term load forecasting will enable a utility provider to plan the resources and also to take control measures to balance the supply and demand of electricity. The aim of this paper is to create a method to forecast the electric load in a non-residential building. Another goal is to analyse what kind of data, as weather, indoor ambient, calendar and building occupancy, is the most relevant in building load forecasting. A simple method, tested with three different models, such as MLR, MLP and SVR, is proposed. The results, from a real case study in the University of Girona, show that the proposed forecast method has high accuracy and low computational cost ; This research has been partially supported by the Spanish Government project MESC (Ref. DPI2013-47450-C2-1-R). Also we would like to thank the Department of Physics and acknowledge the technical assistance and maintenance service of the UdG (SOTIM) which provided the weather and consumption data respectively. The authors belong to the 'Smart IT Engineering and Solutions' accredited research group (Generalitat de Catalunya, 2014 SGR 1052)

Pòster de congrés presentat a: ATTD 2020 - 13th International Conference on Advanced Technologies & Treatments for Diabetes", celebrat a Madrid del 19 al 22 de febrer de 2020 ; PEPPER (Patient Empowerment through Predictive PERsonalised decision support) is an EU-funded H2020 project that provides a personalized decision support system for type 1 diabetes selfmanagement. This work describes the application program interface (API) developed for this purpose ; This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement 689810

Pòster de congrés presentat a: ATTD 2020 - 13th International Conference on Advanced Technologies & Treatments for Diabetes", celebrat a Madrid del 19 al 22 de febrer de 2020 ; PEPPER (Patient Empowerment through Predictive PERsonalised decision support) is an EU-funded H2020 project that provides personalised insulin bolus advice for people with Type 1 diabetes (T1D). Here, we evaluate the safety, feasibility and usability of the PEPPER system compared to a standard bolus calculator ; This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement 689810

Pòster de congrés presentat a: International Conference on Advanced Technologies & Treatments for Diabetes (ATTD) (12th: 20-23 Febrer 2019: Berlin) ; PEPPER (Patient Empowerment through Predictive PERsonalised decision support) is an EU-funded research project which aims to improve the self-management behaviour of adults with type 1 diabetes (T1D). Human factors and ergonomics play a key role in the development of this system ; This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement 689810