Automated Detection of Electric Energy Consumption Load Profile Patterns

[EN] Load profiles of energy consumption from smart meters are becoming more and more available, and the amount of data to analyse is huge. In order to automate this analysis, the application of state-of-the-art data mining techniques for time series analysis is reviewed. In particular, the use of dynamic clustering techniques to obtain and visualise temporal patterns characterising the users of electrical energy is deeply studied. The performed review can be used as a guide for those interested in the automatic analysis and groups of behaviour detection within load profile databases. Additionally, a selection of dynamic clustering algorithms have been implemented and the performances compared using an available electric energy consumption load profile database. The results allow experts to easily evaluate how users consume energy, to assess trends and to predict future scenarios.The data analysed has been facilitated by the Spanish Distributor Iberdrola Electrical Distribution S.A. as part of the research project GAD (Active Management of the Demand), national project by DEVISE 2010 funded by the INGENIIO 2010 program and the CDTI (Centre for Industrial Technology Development), Business Public Entity dependent of the Ministry of Economy and Competitiveness of the Government of Spain.Benítez, I.; Diez, J. (2022). Automated Detection of Electric Energy Consumption Load Profile Patterns. Energies. 15(6):1-26. https://doi.org/10.3390/en1506217612615

Parallel Control of an artificial pancreas with coordinated insulin, glucagon and rescue carbohydrate control actions

[EN] Background: An artificial pancreas with insulin and glucagon delivery has the potential to reduce the risk of hypo- and hyperglycemia in people with type 1 diabetes. However, a maximum dose of glucagon of 1 mg/d is recommended, potentially still requiring rescue carbohydrates in some situations. This work presents a parallel control structure with intrinsic insulin, glucagon, and rescue carbohydrates coordination to overcome glucagon limitations when needed. Methods: The coordinated controller that combines insulin, glucagon, and rescue carbohydrate suggestions (DH-CC-CHO) was compared with the insulin and glucagon delivery coordinated controller (DH-CC). The impact of carbohydrate quantization for practical delivery was also assessed. An in silico study using the UVA-Padova simulator, extended to include exercise and various sources of variability, was performed. Results: DH-CC and DH-CC-CHO performed similarly with regard to mean glucose (126.25 [123.43; 130.73] vs 127.92 [123.99; 132.97] mg/dL, P = .088), time in range (93.04 [90.00; 95.92] vs 92.91 [90.05; 95.75]%, P = .508), time above 180 mg/dL (4.94 [2.72; 7.53] vs 4.99 [2.93; 7.24]%, P = .966), time below 70 mg/dL (0.61 [0.09; 1.75] vs 0.96 [0.23; 2.17]%, P = .1364), insulin delivery (43.50 [38.68; 51.75] vs 42.86 [38.58; 51.36] U/d, P = .383), and glucagon delivery (0.75 [0.40; 1.83] vs 0.76 [0.43; 0.99] mg/d, P = .407). Time below 54 mg/dL was different (0.00 [0.00; 0.05] vs 0.00 [0.00; 0.16]%, P = .036), although non-clinically significant. This was due to the carbs quantization effect in a specific patient, as no statistical difference was found when carbs were not quantized (0.00 [0.00; 0.05] vs 0.00 [0.00; 0.00]%, P = .265). Conclusions: The new strategy of automatic rescue carbohydrates suggestion in coordination with insulin and glucagon delivery to overcome constraints on daily glucagon delivery was successfully evaluated in an in silico proof of concept.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Spanish Ministry of Economy, Industry and Competitiveness (MINECO) through grant number DPI2016-78831-C2-1-R and the European Union through FEDER funds. Vanessa Moscardó was recipient of an FPU grant, FPU13/04253.Moscardo-Garcia, V.; Diez, J.; Bondía Company, J. (2019). Parallel Control of an artificial pancreas with coordinated insulin, glucagon and rescue carbohydrate control actions. Journal of Diabetes Science and Technology. 13(6):1026-1034. https://doi.org/10.1177/1932296819879093S1026103413

Stochastic Seasonal Models for Glucose Prediction in the Artificial Pancreas

[EN] Background: Linear empirical dynamic models have been widely used for glucose prediction. The extension of the concept of seasonality, characteristic of other domains, is explored here for the improvement of prediction accuracy. Methods: Twenty time series of 8-hour postprandial periods (PP) for a same 60g-carbohydrate meal were collected from a closed-loop controller validation study. A single concatenated time series was produced representing a collection of data from similar scenarios, resulting in seasonality. Variability in the resulting time series was representative of worstcase intrasubject variability. Following a leave-one-out cross-validation, seasonal and nonseasonal autoregressive integrated moving average models (SARIMA and ARIMA) were built to analyze the effect of seasonality in the model prediction accuracy. Further improvement achieved from the inclusion of insulin infusion rate as exogenous variable was also analyzed. Prediction horizons (PHs) from 30 to 300 min were considered. Results: SARIMA outperformed ARIMA revealing a significant role of seasonality. For a 5-h PH, average MAPE was reduced in 26.62%. Considering individual runs, the improvement ranged from 6.3% to 54.52%. In the best-performing case this reduction amounted to 29.45%. The benefit of seasonality was consistent among different PHs, although lower PHs benefited more, with MAPE reduction over 50% for PHs of 60 and 120 minutes, and over 40% for 180 min. Consideration of insulin infusion rate into the seasonal model further improved performance, with a 61.89% reduction in MAPE for 30-min PH and reductions over 20% for PHs over 180 min. Conclusions: Seasonality improved model accuracy allowing for the extension of the PH significantly.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was funded by the Spanish Ministry of Economy and Competitiveness, grants DPI2013-46982-C2-1-R and DPI2016-78831-C2-1-R, and the European Union through FEDER funds.Montaser Roushdi Ali, E.; Diez, J.; Bondía Company, J. (2017). Stochastic Seasonal Models for Glucose Prediction in the Artificial Pancreas. Journal of Diabetes Science and Technology. 11(6):1124-1131. https://doi.org/10.1177/1932296817736074S1124113111

Artificial Pancreas System With Unannounced Meals Based on a Disturbance Observer and Feedforward Compensation

© 2021 IEEE. Personal use of this material is permitted. Permissíon from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertisíng or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] This brief is focused on the closed-loop control of postprandial glucose levels of patients with type 1 diabetes mellitus after unannounced meals, which is still a major challenge toward a fully autonomous artificial pancreas. The main limitations are the delays introduced by the subcutaneous insulin pharmacokinetics and the glucose sensor, which typically leads to insulin overdelivery. Current solutions reported in the literature typically resort to meal announcement, which requires patient intervention. In this brief, a disturbance observer (DOB) is used to estimate the effect of unannounced meals, and the insulin pharmacokinetics is taken into account by means of a feedforward compensator. The proposed strategy is validated in silico with the UVa/Padova metabolic simulator. It is demonstrated how the DOB successfully estimates and counteracts not only the effect of meals but also the sudden drops in the glucose levels that may lead to hypoglycemia. For unannounced meals, results show a median time-in-range of 80% in a 30-day scenario with high carbohydrate content and large intrasubject variability. Optionally, users may decide to announce meals. In this case, considering severe bolus mismatch due to carbohydrate counting errors, the median time-in-range is increased up to 88%. In every case, hypoglycemia is avoided.This work was supported in part by the Ministerio de Economia y Competitividad under Grant DPI2016-78831-C2-1-R and in part by the European Union through FEDER Funds.Sanz Diaz, R.; García Gil, PJ.; Diez, J.; Bondía Company, J. (2021). Artificial Pancreas System With Unannounced Meals Based on a Disturbance Observer and Feedforward Compensation. IEEE Transactions on Control Systems Technology. 29(1):454-460. https://doi.org/10.1109/TCST.2020.2975147S45446029

Control por Planificación de Ganancia con Modelos Borrosos

[ES] En este artículo se presentan los tipos de modelos borrosos y metodologías de identificación (por agrupamiento borroso) más adecuados para obtener modelos locales de sistemas no lineales. En particular, se muestra qué técnicas de control por planificación de ganancia son aplicables a los modelos así identificados. Estas técnicas, basándose en el diseño de controladores lineales para los modelos locales identificados, consiguen obtener de forma sencilla controladores para un sistema borroso.Trabajo parcialmente financiado por el proyecto DPI2002-0525 del Ministerio de Ciencia y Tecnología.Diez Ruano, JL.; Navarro Herrero, JL.; Sala Piqueras, A. (2010). Control por Planificación de Ganancia con Modelos Borrosos. Revista Iberoamericana de Automática e Informática industrial. 1(1):32-43. https://doi.org/10.4995/riai.2004.8022OJS32431

Algoritmos de Agrupamiento en la Identificación de Modelos Borrosos

[ES] La aplicación de las técnicas de agrupamiento borroso para la identificación de modelos borrosos se está extendiendo cada vez más. Sin embargo, y dado que su origen es bien distinto a la ingeniería de control, aparecen numerosos problemas en su aplicación en la identificación de modelos locales de sistemas no lineales para control. En este trabajo se revisan las principales técnicas de agrupamiento para la identificación de modelos borrosos, incluyendo propuestas propias que permiten desarrollar modelos que mejoran (respecto a algoritmos previamente existentes) la interpretabilidad y el descubrimiento de estructuras afines locales en los modelos borrosos obtenidos.Parcialmente financiado por el proyecto CICYT DPI2002-0525 (Ministerio Ciencia y Tecnología).Diez Ruano, JL.; Navarro Herrero, JL.; Sala Piqueras, A. (2010). Algoritmos de Agrupamiento en la Identificación de Modelos Borrosos. Revista Iberoamericana de Automática e Informática industrial. 1(2):32-41. http://hdl.handle.net/10251/146622OJS32411

Insulin Estimation and Prediction A REVIEW OF THE ESTIMATION AND PREDICTION OF SUBCUTANEOUS INSULIN PHARMACOKINETICS IN CLOSED-LOOP GLUCOSE CONTROL

This work was supported by the Spanish Ministry of Economy and Competitiveness (MINECO) through grant DPI2013-46982-C2-1-R and the EU through FEDER funds.Bondía Company, J.; Romero Vivó, S.; Ricarte Benedito, B.; Diez, J. (2018). Insulin Estimation and Prediction A REVIEW OF THE ESTIMATION AND PREDICTION OF SUBCUTANEOUS INSULIN PHARMACOKINETICS IN CLOSED-LOOP GLUCOSE CONTROL. IEEE Control Systems. 38(1):47-66. https://doi.org/10.1109/MCS.2017.2766312S476638

Modelling and control of a continuous distillation tower through fuzzy techniques

This paper presents a methodology for the design of a fuzzy controller applicable to continuous processes based on local fuzzy models and velocity linearizations. It has been applied to the implementation of a fuzzy controller for a continuous distillation tower. Continuous distillation towers can be subjected to variations in feed characteristics that cause loss of product quality or excessive energy consumption. Therefore, the use of a fuzzy controller is interesting to control process performance.A dynamic model for continuous distillation was implemented and used to obtain data to develop the fuzzy controller at different operating points. The fuzzy controller was built by integration of linear controllers obtained for each linearization of the system. Simulation of the model with controller was used to validate the controller effectiveness under different scenarios, including a study of the sensibility of some parameters to the control.The results showed that the fuzzy controller was able to keep the target output in the desired range for different inputs disturbances, changing smoothly from a predefined target output to another. The developed techniques are applicable to more complex distillation systems including more operating variablesThe authors acknowledge the partial funding of this work by the projects: Regional Government Project GVPRE/2008/108, and National Projects DPI2007-66728-C02-01 and DPI2008-06737-C02-01.Barceló Rico, F.; Gozálvez Zafrilla, JM.; Diez Ruano, JL.; Santafé Moros, MA. (2011). Modelling and control of a continuous distillation tower through fuzzy techniques. Chemical Engineering Research and Design. 89(1):107-115. https://doi.org/10.1016/j.cherd.2010.04.015S10711589

Sliding-mode disturbance observers for an artificial pancreas without meal announcement

[EN] Carbohydrate counting is not only a burden for patients with type 1 diabetes, but estimation errors in meal announcement could also degrade the outcomes of the current hybrid closed-loop systems. Therefore, removing meal announcement is desirable. A novel control system is addressed here to face postprandial control without meal announcement. The proposed system grounds on two applications of the sliding mode observers in dealing with disturbances: first, the equivalent output technique is used to reconstruct the meal rate of glucose appearance via a first order sliding mode observer; second, a super-twisting -based residual generator is used to detect the meals. Subsequently, a bolusing algorithm uses the information of the two observers to trigger a series of boluses based on a proportional-derivative-like strategy. An in silico validation with 30 patients in a 30-day scenario reveals that the meal detector algorithm achieves a low rate of false positives per day (0.1 (0.1), mean (SD)) and a detection time of 28.5(6.2) min. Additionally, the bolusing algorithm fulfills a non-statistically different mean glucose than the hybrid counterpart with bolus misestimation (146.69 (12.20) mg/dLvs. 144.28 (11.01) mg/dL,p>0.05), without increasing hypoglycemia (0.029 (0.077) vs. 0.004 (0.014)%, p > 0,05), although at the expense of a slightly higher time in hyperglycemia (22.51(8.72) % vs. 18.65 (7.89)%, p <0.05) due to the conservative tuning of the bolusing algorithm for the sake of safety.This work was supported by the Spanish Ministry of Economy, Industry and Competitiveness (MINECO) [grant number DPI2016-78831-C2-1-12]; the European Union [FEDER funds]: and Generalitat Valenciana [grant number ACIF/2017/021]Sala-Mira, I.; Diez, J.; Ricarte Benedito, B.; Bondía Company, J. (2019). Sliding-mode disturbance observers for an artificial pancreas without meal announcement. Journal of Process Control. 78:68-77. https://doi.org/10.1016/j.jprocont.2019.03.00868777

Adaptive calibration algorithm for plasma glucose estimation in continuous glucose monitoring

[EN] Minimally or noninvasive continuous glucose monitors estimate plasma glucose from compartments alternative to blood, and may revolutionize the management of diabetes. However, the accuracy of current devices is still poor and it may partly depend on low performance of the implemented calibration algorithm. Here, a new adaptive calibration algorithm based on a population local-model-based intercompartmental glucose dynamic model is proposed. The novelty consists in the adaptation of data normalization parameters in real time to estimate and compensate patient's sensitivity variations. Adaptation is performed to minimize mean absolute relative deviation at the calibration points with a time window forgetting strategy. Four calibrations are used: preprandial and 1.5 h postprandial at two different meals. Two databases are used for validation: 1) a 9-hCGMSGold (Medtronic, Northridge, USA) time series with paired reference glucose values from a clinical study in 17 subjects with type 1 diabetes; 2) data from 30 virtual patients (UVa simulator, Virginia, USA), where inter-and intrasubject variability of sensor's sensitivity were simulated. Results show how the adaptation of the normalization parameters improves the performance of the calibration algorithm since it counteracts sensor sensitivity variations. This improvement is more evident in one-week simulations.Manuscript received April 17, 2012; revised September 10, 2012 and January 21, 2013; accepted March 11, 2013. Date of publication March 19, 2013; date of current version May 1, 2013. This work was supported in part by the Spanish Ministry of Science and Innovation under Project DPI2010-20764-C02 and in part by the European Union under Grant FP7-PEOPLE-2009-IEF, Ref 252085. The work of F. Barcelo-Rico was supported by the Spanish Ministry of Education (FPU AP2008-02967).Barceló-Rico, F.; Diez, J.; Rossetti, P.; Vehi, J.; Bondía Company, J. (2013). Adaptive calibration algorithm for plasma glucose estimation in continuous glucose monitoring. IEEE Journal of Biomedical and Health Informatics. 17(3):530-538. https://doi.org/10.1109/JBHI.2013.2253325S53053817