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A novel methodology for energy performance benchmarking of buildings by means of linear mixed effect model: The case of space and DHW heating of out-patient Healthcare Centres

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APPLIED ENERGY 2016年第Jun.1期171卷 592-607页

作者： Capozzoli, Alfonso Piscitelli, Marco Savino Neri, Francesco Grassi, Daniele Serale, Gianluca Politecn Torino TEBE Res Grp Dept Energy DENERG Corso Duca Abruzzi 24 I-10129 Turin Italy EXITone Spa Stradale San Secondo 96 I-10064 Turin Italy

The current EU energy efficiency directive 2012/27/EU defines the existing building stocks as one of the most promising potential sector for achieving energy saving. Robust methodologies aimed to quantify the potential reduction of energy consumption for large building stocks need to be developed. To this purpose, a benchmarking analysis is necessary in order to support public planners in determining how well a building is performing, in setting credible targets for improving performance or in detecting abnormal energy consumption. In the present work, a novel methodology is proposed to perform a benchmarking analysis particularly suitable for heterogeneous samples of buildings. The methodology is based on the estimation of a statistical model for energy consumption - the linear mixed effects model -, so as to account for both the fixed effects shared by all individuals within a dataset and the random effects related to particular groups/classes of individuals in the population. The groups of individuals within the population have been classified by resorting to a supervised learning technique. Under this backdrop, a Monte Carlo simulation is worked out to compute the frequency distribution of annual energy consumption and identify a reference value for each group/class of buildings. The benchmarking analysis was tested for a case study of 100 out-patient Healthcare Centres in Northern Italy, finally resulting in 12 different frequency distributions for space and Domestic Hot Water heating energy consumption, one for each class of homogeneous class of buildings. From the median value of each frequency distribution, reference values were extracted to be used in a benchmarking analysis. Beyond being flexible, open and upgradeable over time, a benchmarking analysis relying on both a sound statistical basis and on stochastic simulation is indeed able to overcome the limitations of the more common deterministic or one-dimensional bench marking approach. (C) 2016 Elsevier

关键词： Energy benchmarking analysis linear mixed effect model Healthcare Centres Building energy performance Monte Carlo simulation Supervised learning technique

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SECCHI DISK DEPTH ESTIMATES USING MERIS SATELLITE DATA AND A linear mixed effect model OVER LAKE SIMCOE, CANADA

SECCHI DISK DEPTH ESTIMATES USING MERIS SATELLITE DATA AND A...

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IEEE Joint International Geoscience and Remote Sensing Symposium (IGARSS) / 35th Canadian Symposium on Remote Sensing

作者： Zolfaghari, Kiana Duguay, Claude R. Univ Waterloo Dept Geog & Environm Management Waterloo ON N2L 3G1 Canada

ISBN: (纸本)9781479957750

Lakes offer several economical, ecological, natural, and social benefits, the quality of which depend directly on their water quality. Conventional methodologies of assessing lakes' water quality are expensive and limited. Remote sensing introduces new technologies for effective, accurate and more efficient water quality measurements. This paper utilizes MERIS and in-situ data to assess the water quality on Lake Simcoe, Canada, by specifically looking at Secchi Disk depth. A linear mixed effect model was used as the regression method. There was a significant positive correlation between measured and predicted Log(10)(SDD) (R-2=0.78). Quality of water in Lake Simcoe has significantly deteriorated over the course of the past decade, with severe environmental and social impacts. Temporally, the highest and lowest average values for SDD were found in summer (1.13 m - 20.03 m) and fall (0.78 m - 28.67 m). Spatially, the SDD average value in the time period of study (2002-2011) was high in the northern east region, and low in the southern part (0.94 m- 21.64 m).

关键词： MERIS linear mixed effect model Secchi Disk Depth Lake Simcoe

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Pattern Mixture Sensitivity Analyses via Multiple Imputations for Non-Ignorable Dropout in Joint modeling of Cognition and Risk of Dementia

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STATISTICS IN MEDICINE 2025年第6期44卷 e70040页

作者： Gorbach, Tetiana Carpenter, James R. Frost, Chris Josefsson, Maria Nicholas, Jennifer Nyberg, Lars Umea Univ Umea Sch Business Econ & Stat Dept Stat Umea Sweden London Sch Hyg & Trop Med London England UCL MRC Clin Trials Unit London England Umea Univ Dept Integrat Med Biol Umea Sweden Umea Univ Umea Ctr Funct Brain Imaging Umea Sweden Umea Univ Dept Radiat Sci Umea Sweden

Motivated by the Swedish Betula study, we consider the joint modeling of longitudinal memory assessments and the hazard of dementia. In the Betula data, the time-to-dementia onset or its absence is available for all participants, while some memory measurements are missing. In longitudinal studies of aging, one cannot rule out the possibility of dropout due to health issues resulting in missing not at random longitudinal measurements. We, therefore, propose a pattern-mixture sensitivity analysis for missing not-at-random data in the joint modeling framework. The sensitivity analysis is implemented via multiple imputation as follows: (i) multiply impute missing not at random longitudinal measurements under a set of plausible pattern-mixture imputation models that allow for acceleration of memory decline after dropout, (ii) fit the joint model to each imputed longitudinal memory and time-to-dementia dataset, and (iii) combine the results of step (ii). Our work illustrates that sensitivity analyses via multiple imputations are an accessible, pragmatic method to evaluate the consequences of missing not at-random data on inference and prediction. This flexible approach can accommodate a range of models for the longitudinal and event-time processes. In particular, the pattern-mixture modeling approach provides an accessible way to frame plausible missing not at random assumptions for different missing data patterns. Applying our approach to the Betula study shows that worse memory levels and steeper memory decline were associated with a higher risk of dementia for all considered scenarios.

关键词： linear mixed effect model multiple imputation pattern mixture model proportional hazards model sensitivity analysis

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Improving Reference Evapotranspiration Predictions with Hybrid modeling Approach

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EARTH SYSTEMS AND ENVIRONMENT 2025年 1-18页

作者： Habeeb, Rimsha Almazah, Mohammed M. A. Hussain, Ijaz Al-Rezami, A. Y. Raza, Ali Ray, Ram L. Quaid I Azam Univ Dept Stat Islamabad Pakistan King Khalid Univ Coll Sci & Arts Muhyil Dept Math Muhyil 61421 Saudi Arabia Prince Sattam bin Abdulaziz Univ Coll Humanities & Sci Math Dept Al Kharj 16278 Saudi Arabia Jiangsu Univ Sch Agr Engn Zhenjiang 212013 Peoples R China Prairie View A&M Univ Coll Agr Food & Nat Resources Dept Agr Nutr & Human Ecol Prairie View TX 77446 USA

Accurate prediction of Reference Evapotranspiration (ET0) is essential for determining crop water requirements, significantly contributing to the effective management and sustainable planning of the world's scarce water resources. Many statistical and Machine Learning (ML) models have been proposed for predicting ET0, but no universal algorithm has been found to be the best performance for all the diverse conditions. Therefore, this study introduces a novel hybrid modeling approach that integrates the linear mixed effect (LME) model with Nonlinear Autoregressive Neural Network (NANN) and Support Vector Machine (SVM) ML models. By combining the strengths of LME for capturing linear patterns and NANN and SVM for modeling nonlinear residuals, this approach aims to minimize prediction errors and enhance accuracy under diverse conditions. For this purpose, climatic data from 1980 to 2021 from five meteorological stations located in Pakistan were utilized. Moreover, panel (i.e., Levin Lin Chu (LLC), Im, Pesaran, and Shin (IPS)) and Moving Block Bootstrap (MBB) unit root tests were applied to investigate the stationarity in the dataset. Correlation analysis showed that temperature had the highest correlation with ET0 among climatic variables. Both hybrid models and the LME model were evaluated across five meteorological stations in Pakistan. Results from the Statistical assessment indicated that although both proposed models exhibited commendable performance in the prediction of ET0, the hybrid model LME-SVM exhibited a comparative advantage over the hybrid model LME-NANN. The Taylor diagram confirmed a strong positive correlation between observed (estimated by modified Hargreaves-Samani equation) and predicted ET0 values for both hybrid models.

关键词： Reference evapotranspiration Prediction error Hybrid model Nonlinear Autoregressive Neural Network linear mixed effect model Unit root test

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Estimation of Water Quality Parameters in Lake Erie from MERIS Using linear mixed effect models

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REMOTE SENSING 2016年第6期8卷 473页

作者： Zolfaghari, Kiana Duguay, Claude R. Univ Waterloo Interdisciplinary Ctr Climate Change Waterloo ON N2L 3G1 Canada Univ Waterloo Dept Geog & Environm Management Waterloo ON N2L 3G1 Canada

linear mixed effect (LME) models are applied to the CoastColour atmospherically-corrected Medium Resolution Imaging Spectrometer (MERIS) reflectance, L2R full resolution product, to derive chlorophyll-a (chl-a) concentration and Secchi disk depth (SDD) in Lake Erie, which is considered as a Case II water (i.e., turbid and productive). A LME model considers the correlation that exists in the field measurements which have been performed repeatedly in space and time. In this study, models are developed based on the relation between the logarithmic scale of the water quality parameters and band ratios: B07:665 nm to B09:708.75 nm for log(10)chl-a and B06:620 nm to B04:510 nm for log(10)SDD. Cross validation is performed on the models. The results show good performance of the models, with Root Mean Square Errors (RMSE) and Mean Bias Errors (MBE) of 0.31 and 0.018 for log(10)chl-a, and 0.19 and 0.006 for log(10)SDD, respectively. The models are then applied to a time series of MERIS images acquired over Lake Erie from 2004-2012 to investigate the spatial and temporal variations of the water quality parameters. Produced maps reveal distinct monthly patterns for different regions of Lake Erie that are in agreement with known biogeochemical properties of the lake. The Detroit River and Maumee River carry sediments and nutrients to the shallow western basin. Hence, the shallow western basin of Lake Erie experiences the most intense algal blooms and the highest turbidity compared to the other sections of the lake. Maumee Bay, Sandusky Bay, Rondeau Bay and Long Point Bay are estimated to have prolonged intense algal bloom.

关键词： Lake Erie MERIS CoastColour linear mixed effect model chlorophyll-a Secchi disk depth

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modelling the concentration of chloroform in the air of a Norwegian swimming pool facility-A repeated measures study

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SCIENCE OF THE TOTAL ENVIRONMENT 2019年 664卷 1039-1044页

作者： Nitter, Therese B. Svendsen, Kristin von H. Norwegian Univ Sci & Technol NTNU Trondheim Norway Ctr Sports Facil & Technol SIAT Trondheim Norway Dept Ind Econ & Technol Management Trondheim Norway

Certain volatile disinfection by-products (DBPs) off -gassing from pool water can cause eye and skin irritations, respiratory problems, and even cancer. No guidelines or recommendations concerning DBPs in the air exist in Norway. Traditionally, ventilation strategies in indoor swimming pools arc based on reducing condensation on the windows rather than ensuring proper air quality in the users' breathing zone. A total of 93 air samples of airborne concentrations of trihalomelhanes (THMs) were collected via stationary sampling. We investigated the distribution of total THM (CITIM) 0.05 m and 0.60 m above the water surface at six different locations in the poolroom and the covarialion between the water and air quality parameters. Based on a linear mixed effects model, the most important determinants in terms of predicting the air concentration of CHC13 were height above water surface, air changes of fresh air per hour, concentration of combined chlorine in the water, relative humidity (RH) and day of the week. Approximately 36% of the total variability could be attributed to these variables;hence, to reduce the average exposure in the poolroom, hazard control should focus on these variables. Based on the identified predictor variables, the supplied air should be controlled based on water quality in addition to the traditional control censors for RH and air temperature used in the ventilation system of Norwegian swimming facilities. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Determinants of exposure ACH THM Ventilation linear mixed effect model

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Response of lightning NO_x emissions and ozone production to climate change: Insights from the Atmospheric Chemistry and Climate model Intercomparison Project

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GEOPHYSICAL RESEARCH LETTERS 2016年第10期43卷 5492-5500页

作者： Finney, D. L. Doherty, R. M. Wild, O. Young, P. J. Butler, A. Univ Edinburgh Sch Geosci Edinburgh Midlothian Scotland Univ Lancaster Lancaster Environm Ctr Lancaster England Biomath & Stat Scotland Edinburgh Midlothian Scotland

Results from an ensemble of models are used to investigate the response of lightning nitrogen oxide emissions to climate change and the consequent impacts on ozone production. Most models generate lightning using a parameterization based on cloud top height. With this approach and a present-day global emission of 5 TgN, we estimate a linear response with respect to changes in global surface temperature of +0.44 +/- 0.05 TgN K-1. However, two models using alternative approaches give +0.14 and -0.55 TgN K-1 suggesting that the simulated response is highly dependent on lightning parameterization. Lightning NOx is found to have an ozone production efficiency of 6.5 +/- 4.7 times that of surface NOx sources. This wide range of efficiencies across models is partly due to the assumed vertical distribution of the lightning source and partly to the treatment of nonmethane volatile organic compound (NMVOC) chemistry. Careful consideration of the vertical distribution of emissions is needed, given its large influence on ozone production.

关键词： lightning climate change ozone ACCMIP linear mixed effect model

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Integrated analysis of the heterogeneous microarray data

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BMC BIOINFORMATICS 2011年第Sup5期12卷 1-8页

作者： Yi, Sung Gon Park, Taesung Seoul Natl Univ Dept Stat Seoul South Korea Case Western Reserve Univ Dept Epidemiol & Biostat Cleveland OH 44106 USA

Background: As the magnitude of the experiment increases, it is common to combine various types of microarrays such as paired and non-paired microarrays from different laboratories or hospitals. Thus, it is important to analyze microarray data together to derive a combined conclusion after accounting for heterogeneity among data sets. One of the main objectives of the microarray experiment is to identify differentially expressed genes among the different experimental groups. We propose the linear mixed effect model for the integrated analysis of the heterogeneous microarray data sets. Results: The proposed linear mixed effect model was illustrated using the data from 133 microarrays collected at three different hospitals. Though simulation studies, we compared the proposed linear mixed effect model approach with the meta-analysis and the ANOVA model approaches. The linear mixed effect model approach was shown to provide higher powers than the other approaches. Conclusions: The linear mixed effect model has advantages of allowing for various types of covariance structures over ANOVA model. Further, it can handle easily the correlated microarray data such as paired microarray data and repeated microarray data from the same subject.

关键词： Microarray Data Covariance Structure ANOVA model linear mixed effect model Statistical Test Procedure

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Response of spring yield dynamics to climate change across altitude gradient and varied hydrogeological conditions

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SCIENCE OF THE TOTAL ENVIRONMENT 2024年 921卷 171082页

作者： Lamacova, Anna Ledvinka, Ondrej Bohdalkova, Leona Oulehle, Filip Kreisinger, Jakub Vlnas, Radek Czech Hydrometeorol Inst Hydrol Div Na Sabatce 2050-17 Prague 4 Czech Republic Czech Geol Survey Dept Environm Geochem & Biogeochem Klarov 3 Prague 1 Czech Republic Charles Univ Prague Fac Sci Dept Phys Geog & Geoecol Albertov 6 Prague 2 Czech Republic Czech Acad Sci Global Change Res Inst Dept Biogeochem & Hydrol Cycles Belidla 986-4a Brno 60300 Czech Republic Charles Univ Prague Fac Sci Dept Zool Vinicna 7 Prague 2 Czech Republic

Springs offer insights into groundwater dynamics. Long-term monitoring of spring yields can reflect the response of groundwater storage to climate change. We analyzed the yield trends of 136 springs across 18 hydrogeological regions in Czechia from 1971 to 2020. The trend -free pre -whitening Mann -Kendall test and linear mixed -effects models were used to assess environmental impacts on spring yields. Overall, 71 % of the springs showed no longterm trends, 28 % exhibited decreasing trends, and 1.5 % showed increasing trends in annual spring yields. Altitude has been demonstrated as a contributing factor influencing spring responses to climate change. Lowland springs (<300 m a.s.l.) exhibited the highest proportion of decreasing annual trends (41 %), while uplands (300-600 m a.s.l.) and highlands (>600 m a.s.l.) showed declines in 26 % and 25 % of springs, respectively. Moreover, highlands recorded a 7 % yield increase, indicating a complex interplay between altitude and spring response to climatic factors. A strong positive correlation was found between precipitation and yields (p < 0.01), whereas temperature increases negatively affected spring yields (p < 0.01). The interaction between temperature changes and region transmissivity highlighted the vulnerability of springs in low-transmissivity regions, predominantly those in crystalline and flysch bedrock areas, to climatic shifts. Generally, these regions have lower spring yields compared to the high-transmissivity areas of the Cretaceous basins. Although these lower -yield regions are not used as a primary water source for large areas, unlike regions with high-transmissivity bedrock, they provide water resources for local supply. Analysis of annual spring maxima frequencies revealed a shift in the culmination of maxima occurrences from April to March, with a significant decrease in April (p < 0.05) and May (p < 0.1) and an increase in March (p < 0.05), suggesting a change in spring yield seasonality. The 2015-20

关键词： Groundwater Drought Seasonal yield fluctuations Transmissivity Trend analysis linear mixed effect model

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Detection of Alzheimer’s disease onset using MRI and PET neuroimaging:longitudinal data analysis and machine learning

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Neural Regeneration Research 2023年第10期18卷 2134-2140页

作者： Iroshan Aberathne Don Kulasiri Sandhya Samarasinghe Centre for Advanced Computational Solutions(C-fACS) Lincoln UniversityChristchurchNew Zealand

The scientists are dedicated to studying the detection of Alzheimer’s disease onset to find a cure, or at the very least, medication that can slow the progression of the disease. This article explores the effectiveness of longitudinal data analysis, artificial intelligence, and machine learning approaches based on magnetic resonance imaging and positron emission tomography neuroimaging modalities for progression estimation and the detection of Alzheimer’s disease onset. The significance of feature extraction in highly complex neuroimaging data, identification of vulnerable brain regions, and the determination of the threshold values for plaques, tangles, and neurodegeneration of these regions will extensively be evaluated. Developing automated methods to improve the aforementioned research areas would enable specialists to determine the progression of the disease and find the link between the biomarkers and more accurate detection of Alzheimer’s disease onset.

关键词： deep learning image processing linear mixed effect model neuroimaging neuroimaging data sources onset of Alzheimer’s disease detection pattern recognition

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