scholarly journals Bayesian Inference of Dwellings Energy Signature at National Scale: Case of the French Residential Stock

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5651
Author(s):  
Nils Artiges ◽  
Simon Rouchier ◽  
Benoit Delinchant ◽  
Frédéric Wurtz
Keyword(s):  
Bayesian Inference ◽  
Stochastic Models ◽  
Central Place ◽  
Energy Planning ◽  
Deterministic Models ◽  
District Energy ◽  
Urban Buildings ◽  
Significant Bias ◽  
Comparative Results ◽  
Energy Signature

Cities take a central place in today’s energy landscape. Urban Buildings Energy Modeling (UBEM) is identified as a promising approach for energy planning and optimization in cities and districts. It generally relies on the use of Building Archetypes, i.e., simplified deterministic models for categorized building typologies. However, this implies large assumptions which may accumulate and induce significant bias on energy consumption estimates. In this work, we address this issue with static stochastic models whose parameters are inferred over national thermo-energy data using Bayesian Inference. We analyze inference results and validate them with a panel of standard indicators. Then, we provide comparative results with deterministic building archetypes and stock data from the TABULA European project. Comparisons between heat loss coefficients show relative coherence between building categories, but highlight some significant bias between both approaches. This bias is also shown in the comparative result of a Monte Carlo simulation using inferred stochastic models for a 10331 dwellings stock. In conclusion, inferred stochastic models show interesting insights over the French dwellings stock and potential for district energy simulation. All code and data involved in this study are released in an open repository.

scholarly journals Mapping Buildings’ Energy-Related Features at Urban Level toward Energy Planning

Buildings ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 322
Author(s):  
Simone Ferrari ◽  
Federica Zagarella ◽  
Paola Caputo ◽  
Giuliano Dall’O’
Keyword(s):  
Energy Demand ◽  
National Level ◽  
Energy Planning ◽  
Urban Context ◽  
Existing Building ◽  
Urban Buildings ◽  
Usage Patterns ◽  
Urban Level ◽  
The City ◽  
Insight Into

To boost energy efficiency in the building sector at urban and district scales, the use of a Geographic Information System (GIS) for data collection and energy spatial analysis is relevant. As highlighted in many studies on this topic reported in literature, the correlation among available databases is complex due to the different levels of information. As the first part of a wide research aimed at estimating the energy demand of urban buildings, we present in this article a focus on the details of the GIS-based procedure developed to assess the main energy-related features of existing building stocks. The procedure is based on the elaboration of data from the Italian Topographic Databases, under provision at the national level according to the INSPIRE European Directive and the national General Census of Population and Houses. It enables one to calculate and map the urban built volume characterized by mostly diffuse use categories in an urban context (residential and offices), to which different equipment and building usage patterns can be associated, and by construction periods, featuring different technological solutions. The method has been applied to the city of Milan (Italy). An insight into the outcomes from the overall method of the wider research is also reported.

scholarly journals Estimating the distribution of time to extinction of infectious diseases in mean-field approaches

2020 ◽  
Author(s):  
Maryam Aliee ◽  
Kat S. Rock ◽  
Matt J. Keeling
Keyword(s):  
Infectious Diseases ◽  
Stochastic Models ◽  
Mean Field ◽  
Practical Method ◽  
Deterministic Models ◽  
Recovery Processes ◽  
Time To Extinction ◽  
And Control ◽  
Field Approaches ◽  
Birth Death

AbstractA key challenge for many infectious diseases is to predict the time to extinction under specific interventions. In general this question requires the use of stochastic models which recognise the inherent individual-based, chance-driven nature of the dynamics; yet stochastic models are inherently computationally expensive, especially when parameter uncertainty also needs to be incorporated. Deterministic models are often used for prediction as they are more tractable, however their inability to precisely reach zero infections makes forecasting extinction times problematic. Here, we study the extinction problem in deterministic models with the help of an effective “birth-death” description of infection and recovery processes. We present a practical method to estimate the distribution, and therefore robust means and prediction intervals, of extinction times by calculating their different moments within the birth-death framework. We show these predictions agree very well with the results of stochastic models by analysing the simplified SIS dynamics as well as studying an example of more complex and realistic dynamics accounting for the infection and control of African sleeping sickness (Trypanosoma brucei gambiense).

Optimization models for managing limited resources in logistics

2021 ◽  
Author(s):  
Aleksandr Mischenko ◽  
Anastasiya Ivanova
Keyword(s):  
Stochastic Models ◽  
Optimization Models ◽  
Limited Resources ◽  
Model Parameters ◽  
Systems Modeling ◽  
Deterministic Models ◽  
Logistics Systems ◽  
Industrial Enterprises ◽  
The Stability ◽  
Senior Students

In the proposed monograph, optimization models for managing limited resources in logical systems are considered. Such systems are primarily used by industrial enterprises, transport companies and trade organizations, including those that carry out wholesale activities. As a rule, the efficiency of these objects largely depends on how rational use of limited resources such as: consumer camera business, labor, vehicles, etc. In this paper, various approaches to managing such resources are considered both for deterministic models and for the situation when a number of model parameters are not specified exactly, that is, for stochastic models. In this case, it is proposed to evaluate the stability of models to the occurrence of various types of risk events, both by the structure of the solution and by the functionality. It is addressed to senior students, postgraduates and masters studying in the specialty "Management" and "Logistics", as well as specialists in the field of logistics systems modeling.

scholarly journals Translating environmental xenobiotic fate models across scales

1997 ◽  
Vol 1 (4) ◽  
pp. 895-904 ◽  
Author(s):  
O. Richter ◽  
B. Diekkrüger
Keyword(s):  
Stochastic Models ◽  
Process Models ◽  
Spatial Process ◽  
Soil Parameters ◽  
Deterministic Models ◽  
Deterministic Process ◽  
Statistical Variation ◽  
Scale Models ◽  
Classical Models ◽  
Environmental Xenobiotic

Abstract. The classical models developed for degradation and transport of xenobiotics have been derived with the assumption of homogeneous environments. Unfortunately, deterministic models function well in the laboratory under homogeneous conditions but such homogeneous conditions often do not prevail in the field. A possible solution is the incorporation of the statistical variation of soil parameters into deterministic process models. This demands the development of stochastic models of spatial variability. To this end, spatial soil parameter fields are conceived as the realisation of a random spatial process. Extrapolation of local fine scale models to large heterogeneous fields is achieved by coupling deterministic process models with random spatial field models.

scholarly journals Future energy scenarios of household in Bhaktapur District

2015 ◽  
Vol 3 ◽  
pp. 36-43
Author(s):  
Yogesh Bajracharya
Keyword(s):  
Total Energy ◽  
Urban Area ◽  
Rural Area ◽  
Energy Demand ◽  
Planning System ◽  
Energy Planning ◽  
District Energy ◽  
Ghg Reduction ◽  
Co2 Equivalent ◽  
Business As Usual

Energy plays a pervasive and critically important role in economic and social development. So, energy planning is important area of study. For Bhaktapur district energy plan, the survey was done by using the questionnaire. Total of 91 samples from rural area and 112 samples from urban area were taken to prepare primary database. The total energy demand was 952 TJ in rural area and 458 TJ in urban area in 2013. LEAP (Long-Range Energy Alternative Planning System) software was used for energy planning. Five scenarios, Business as Usual (BAU), Reference (REF), Accelerated Growth Rate (ACC), and Sustainable Energy for All (SE4ALL) in REF and ACC case scenarios were developed. Total energy demand in 2030 will be 2,748 TJ for BAU, 3,799 TJ for REF and 6,203 TJ for ACC case whereas total demand limits from 766 TJ and 1,241 TJ in SE4ALL scenario in REF and ACC case respectively. Again the total energy demand for 2045 will be 4,945 TJ, 9,104 TJ and 22,592 TJ for BAU, REF and ACC case respectively. Total energy demand for SE4ALL scenario will be 1,807 TJ and 4,381 TJ for REF and ACC case respectively. The GHG reduction up to 2045 by SE4ALL approach is 1.79 million metric tons of CO2 equivalent in REF and 3.54 million metric tons of CO2 equivalent in ACC Scenario.

Approach and practices of district energy planning to achieve low carbon outcomes in China

Energy Policy ◽  
2015 ◽  
Vol 83 ◽  
pp. 109-122 ◽  
Author(s):  
Baoping Xu ◽  
Shaoxiang Zhou ◽  
Lin Hao
Keyword(s):  
Energy Planning ◽  
Low Carbon ◽  
District Energy

The dynamics of neutral mutation

1978 ◽  
Vol 203 (1150) ◽  
pp. 91-91
Keyword(s):  
Stochastic Models ◽  
Diffusion Approximations ◽  
Neutral Systems ◽  
Effective Population ◽  
Gene Frequencies ◽  
Deterministic Models ◽  
New Approach ◽  
Large Populations ◽  
Quantitative Results ◽  
Infinite Alleles Model

A variety of mathematical models have been proposed, over the years since the pioneering work of Fisher and Wright, for the evolution of gene frequencies in large populations under the pressure of selection and mutation. It is broadly true to say that deterministic models are adequate, at least to a first approximation, when selective differences are large compared with the reciprocal of the effective population size. When selection is weaker than this, genetic drift is sufficiently obtrusive to make stochastic models essential. Such models are typically much more difficult to analyse than deterministic ones, and detailed studies have usually been confined to the very special situation of statistical equilibrium. But of course no biological system is really in equilibrium for very long, and moreover the relaxation of nearly neutral systems is usually slow; hence the need for dynamical stochastic models and for their analysis outside a state of equilibrium. The usual way of attacking this problem is by diffusion approximations, and the theory of such processes is surveyed. Questions of existence, uniqueness and adequacy of approximation are well understood, but much less is known about methods of deriving explicit quantitative results or qualitative insight. A new approach is suggested which is particularly useful for studying a locus at which many different alleles are possible. It leads, for example, to a dynamical picture (in terms of a diffusing Poisson process) for the neutral infinite-alleles model of Kimura & Crow.

scholarly journals Revisiting the reduction of stochastic models of genetic feedback loops with fast promoter switching

2019 ◽  
Author(s):  
J. Holehouse ◽  
R. Grima
Keyword(s):  
Transcriptional Regulation ◽  
Master Equation ◽  
Gene Regulatory Networks ◽  
Stochastic Models ◽  
Regulatory Networks ◽  
Burst Size ◽  
Protein Translation ◽  
Feedback Loops ◽  
Deterministic Models ◽  
Gene Regulatory

AbstractPropensity functions of the Hill-type are commonly used to model transcriptional regulation in stochastic models of gene expression. This leads to an effective reduced master equation for the mRNA and protein dynamics only. Based on deterministic considerations, it is often stated or tacitly assumed that such models are valid in the limit of rapid promoter switching. Here, starting from the chemical master equation describing promoter-protein interactions, mRNA transcription, protein translation and decay, we prove that in the limit of fast promoter switching, the distribution of protein numbers is different than that given by standard stochastic models with Hill-type propensities. We show the differences are pronounced whenever the protein-DNA binding rate is much larger than the unbinding rate, a special case of fast promoter switching. Furthermore we show using both theory and simulations that use of the standard stochastic models leads to drastically incorrect predictions for the switching properties of positive feedback loops and that these differences decrease with increasing mean protein burst size. Our results confirm that commonly used stochastic models of gene regulatory networks are only accurate in a subset of the parameter space consistent with rapid promoter switching.Statement of SignificanceA large number of models of gene regulatory networks in the literature assume that since promoter switching is fast then transcriptional regulation can be effectively modeled using Hill functions. While this approach can be rigorously justified for deterministic models, it is presently unclear if it is also the case for stochastic models. In this article we prove that this is not the case, i.e. stochastic models of gene regulatory systems, namely those with feedback loops, describing transcriptional regulation using Hill functions are only valid in a subset of parameter conditions consistent with fast promoter switching. We identify parameter regimes where these models are correct and where their predictions cannot be trusted.

Stochastic models to estimate population dynamics

2020 ◽  
Vol 8 (1) ◽  
pp. 136-152
Author(s):  
Saba Infante ◽  
Luis Sanchez ◽  
Aracelis Hernandez
Keyword(s):  
Stochastic Models ◽  
Growth Dynamics ◽  
Real Data ◽  
Sampling Errors ◽  
High Dimensions ◽  
Deterministic Models ◽  
Dynamics Of Population ◽  
The Republic ◽  
Chemical Conditions ◽  
Control Work

The growth dynamics that a population follows is mainly due to births, deaths or migrations, each of thesephenomena is affected by other factors such as public health, birth control, work sources, economy, safety and conditions of quality of life in neighboring countries, among many others. In this paper is proposed two statistical models based on a system of stochastic differential equations (SDE) that model the dynamics of population growth, and three computational algorithms that allow the generation of probability distribution samples in high dimensions, in models that have non-linear structures and that are useful for making inferences. The algorithms allow to estimate simultaneously states solutions and parameters in SDE models. The interpretation of the parameters is important because they are related to the variables of growth, mortality, migration, physical-chemical conditions of the environment, among other factors. The algorithms are illustrated using real data from a sector of the population of the Republic of Ecuador, and are compared with the results obtained with the models used by theWorld Bank for the same data, which shows that stochastic models Proposals based on an SDE more adequately and reliably adjust the dynamics of demographic randomness, sampling errors and environmental randomness in comparison with the deterministic models used by the World Bank. It is observed that the population grows year by year and seems to have a definite tendency; that is, a clearly growing behavior is seen. To measure the relative success of the algorithms, the relative error was estimated, obtaining small percentage errors.

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