An hourly periodic state space model for modelling French national electricity load

We present a model for hourly electricity load forecasting based on stochastically time-varying processes that are designed to account for changes in customer behaviour and in utility production efficiencies. The model is periodic: it consists of different equations and different parameters for each hour of the day. Dependence between the equations is introduced by covariances between disturbances that drive the time-varying processes. The equations are estimated simultaneously. Our model consists of components that represent trends, seasons at different levels (yearly, weekly, daily, special days and holidays), short-term dynamics and weather regression effects, including nonlinear functions for heating effects. The implementation of our forecasting procedure relies on the multivariate linear Gaussian state space framework, and is applied to the national French hourly electricity load. The analysis focuses on two hours, 9 AM and 12 PM, but forecasting results are presented for all twenty-four hours. Given the time series length of nine years of hourly observations, many features of our model can be estimated readily, including yearly patterns and their time-varying nature. The empirical analysis involves an out-of-sample forecasting assessment up to seven days ahead. The one-day ahead forecasts from forty-eight bivariate models are compared with twenty-four univariate models, one for each hour of the day. We find that the implied forecasting function depends strongly on the hour of the day.Kalman filter Maximum likelihood estimation Seemingly Unrelated Regression Equations

An Hourly Periodic State Space Model for Modelling French National Electricity Load

We present a model for hourly electricity load forecasting based on stochastically time-varying processes that are designed to account for changes in customer behaviour and in utility production efficiencies. The model is periodic: it consists of different equations and different parameters for each hour of the day. Dependence between the equations is introduced by covariances between disturbances that drive the time-varying processes. The equations are estimated simultaneously. Our model consists of components that represent trends, seasons at different levels (yearly, weekly, daily, special days and holidays), short-term dynamics and weather regression effects including nonlinear functions for heating effects. The implementation of our forecasting procedure relies on the multivariate linear Gaussian state space framework and is applied to national French hourly electricity load. The analysis focuses on two hours, 9 AM and 12 AM, but forecasting results are presented for all twenty-four hours. Given the time series length of nine years of hourly observations, many features of our model can be readily estimated including yearly patterns and their time-varying nature. The empirical analysis involves an out-of sample forecasting assessment up to seven days ahead. The one-day ahead forecasts from fourty-eight bivariate models are compared with twenty-four univariate models for all hours of the day. We find that the implied forecasting function strongly depends on the hour of the day.Kalman filter; Maximum likelihood estimation; Seemingly Unrelated Regression Equations; Unobserved Components; Time varying parameters; Heating effect

Does red blood cell storage time still influence ICU survival?

Objective: Few studies have shown that aged packed red blood cells (RBC) transfusion negatively influenced the outcome of ICU patients, probably related to storage lesions which could be decreased by leukodepletion of RBC. The purpose of this study was to evaluate the impact of aged leukodepleted-RBC pack, on the outcome of ICU patients. Design: Retrospective, observational, cohort study in a Medical Intensive Care Unit. Patients: Consecutive patients admitted during the years 2005 and 2006, and requiring a transfusion. We recorded patient's demographic data, number of RBC unit and age of each RBC, length of ICU, mortality during ICU stay. Results: Five hundred and thirty-four patients were included with global mortality was 26.6%, length of stay in ICU six days (3-14) and SAPS II 48 (35-62). RBC equaling to 5.9 were transfused per patients (22.7% < 14 days and 57.3% < 21 days). The number of RBC was significantly higher in the dead patients group, but the rate of RBC stored less than 21 days was not different (54% versus 60%; p = 0.21). In a multivariate logistic model, independent predictors of ICU death were SAPS II (OR = 1.02 per point, p < 0.001), number of RBC (OR = 1.08 per RBC, p < 0.001), length of stay in ICU (p < 0.001). Similar results were obtained while introducing the age of RBC as time dependent covariates in a multivariate Cox's model. Conclusions: RBC transfused in our ICU are old. The ICU outcome is independently associated with the number of leucodepleted RBC transfused, but not with their age

An Hourly Periodic State Space Model for Modelling French National Electricity Load

We present a model for hourly electricity load forecasting based on stochastically time-varying processes that are designed to account for changes in customer behaviour and in utility production efficiencies. The model is periodic: it consists of different equations and different parameters for each hour of the day. Dependence between the equations is introduced by covariances between disturbances that drive the time-varying processes. The equations are estimated simultaneously. Our model consists of components that represent trends, seasons at different levels (yearly, weekly, daily, special days and holidays), short-term dynamics and weather regression effects, including nonlinear functions for heating effects. The implementation of our forecasting procedure relies on the multivariate linear Gaussian state space framework, and is applied to the national French hourly electricity load. The analysis focuses on two hours, 9 AM and 12 PM, but forecasting results are presented for all twenty-four hours. Given the time series length of nine years of hourly observations, many features of our model can be estimated readily, including yearly patterns and their time-varying nature. The empirical analysis involves an out-of-sample forecasting assessment up to seven days ahead. The one-day ahead forecasts from forty-eight bivariate models are compared with twenty-four univariate models, one for each hour of the day. We find that the implied forecasting function depends strongly on the hour of the day. © 2008 International Institute of Forecasters

Multicentre, prospective, double-blind, randomised controlled clinical trial comparing different non-opioid analgesic combinations with morphine for postoperative analgesia: the OCTOPUS study

International audienceBackground: Head-to-head comparisons of combinations of more than one non-opioid analgesic (NOA) with morphine alone, for postoperative analgesia, are lacking. The objective of this multicentre, randomised, double-blind controlled trial was to compare the morphine-sparing effects of different combinations of three NOAs-paracetamol (P), nefopam (N), and ketoprofen (K)-for postoperative analgesia. Methods: Patients from 10 hospitals were randomised to one of eight groups: control (C) received saline as placebo, P, N, K, PN, PK, NK, and PNK. Treatments were given intravenously four times a day during the first 48 h after surgery, and morphine patient-controlled analgesia was used as rescue analgesia. The outcome measures were morphine consumption, pain scores, and morphine-related side-effects evaluated 24 and 48 h after surgery. Results: Two hundred and thirty-seven patients undergoing a major surgical procedure were included between July 2013 and November 2016. Despite a failure to reach a calculated sample size, 24 h morphine consumption [median (interquartile range)] was significantly reduced in the PNK group [5 (1-11) mg] compared with either the C group [27 (11-42) mg; P<0.05] or the N group [21 (12-29) mg; P<0.05]. Results were similar 48 h after surgery. Patients experienced less pain in the PNK group compared with the C, N, and P groups. No difference was observed in the incidence of morphine-related side-effects. Conclusions: Combining three NOAs with morphine allows a significant morphine sparing for 48 h after surgery associated with superior analgesia the first 24 h when compared with morphine alone

Blood Gene Expression Predicts Bronchiolitis Obliterans Syndrome

Bronchiolitis obliterans syndrome (BOS), the main manifestation of chronic lung allograft dysfunction, leads to poor long-term survival after lung transplantation. Identifying predictors of BOS is essential to prevent the progression of dysfunction before irreversible damage occurs. By using a large set of 107 samples from lung recipients, we performed microarray gene expression profiling of whole blood to identify early biomarkers of BOS, including samples from 49 patients with stable function for at least 3 years, 32 samples collected at least 6 months before BOS diagnosis (prediction group), and 26 samples at or after BOS diagnosis (diagnosis group). An independent set from 25 lung recipients was used for validation by quantitative PCR (13 stables, 11 in the prediction group, and 8 in the diagnosis group). We identified 50 transcripts differentially expressed between stable and BOS recipients. Three genes, namely POU class 2 associating factor 1 (POU2AF1), T-cell leukemia/lymphoma protein 1A (TCL1A), and B cell lymphocyte kinase, were validated as predictive biomarkers of BOS more than 6 months before diagnosis, with areas under the curve of 0.83, 0.77, and 0.78 respectively. These genes allow stratification based on BOS risk (log-rank test p &lt; 0.01) and are not associated with time posttransplantation. This is the first published large-scale gene expression analysis of blood after lung transplantation. The three-gene blood signature could provide clinicians with new tools to improve follow-up and adapt treatment of patients likely to develop BOS