Useful energy transfer in air-to-air heat recovery units in partly heated low energy buildings

In this study, the performance of ventilation systems with heat recovery in residential buildings with a low energy demand for heating was evaluated. In a completely heated building, the percentage of useful recovered heat will be equal to the nominal effectiveness of the heat exchanger. In the case some rooms are not heated, they will still receive preheated air. This part of the recovered heat will not directly increase comfort, so it does not completely contribute to the energy savings of the building. Simulations were done with TRNSYS to assess the percentage of usefully recovered heat. This value was found to be lower than the nominal effectiveness, but varying with several parameters

Influence Of Building Zoning On Annual Energy Demand

Simulation tools are widely used to assess the energy consumption of a building. In the modeling process, some choices should be made by the simulation tool user such as the division of the building into thermal zones. The zoning process is user dependent, which results in some difference in energy consumption results and model set-up and computational times. The aim of this work is to assess the influence of building zoning on the results of the dynamic thermal simulation including airflow and thermal transfers between zones For this purpose, several different building zonings are applied to the same office building, and then the results of the dynamic thermal simulations are compared in terms of energy consumption (heating, cooling, and auxiliaries) and computational and set-up times. To assess the impact of thermal zoning, five cases are studied (from the most to the least complex): - 1) *49-zone model* : each zone gathers the premises with the same air handling system, the same occupancy profile, at each floor and building orientation. - 2) *44-zone model* : the premises containing the same air handling system are gathered at every floor, even though their occupancy profile is different. - 3) *26-zone model*: all floors are merged, except for the first and the top floors (under-roof). - 4) *21-zone model* : the first and the under-roof floors are merged with the others if the premises have the same occupancy profile and handling system. - 5) *11-zone model* : the premises with a different orientation but with the same occupancy profile and handling system are gathered. The importance of airflow coupling is evaluated by using the most detailed model (49 zones) and comparing the cases with or without considering air transfer from offices to corridors and toilets (from which air is extracted). Then, to study the impact of thermally connecting juxtaposed zones, the “21-zone model” with and without thermal transfer are compared. Finally, the impact of merging the floors is analyzed by considering different roof and floor insulations and the impact of merging the orientations is studied by using different glazed surface ratio

Support Vector Machine in Prediction of Building Energy Demand Using Pseudo Dynamic Approach

Building's energy consumption prediction is a major concern in the recent years and many efforts have been achieved in order to improve the energy management of buildings. In particular, the prediction of energy consumption in building is essential for the energy operator to build an optimal operating strategy, which could be integrated to building's energy management system (BEMS). This paper proposes a prediction model for building energy consumption using support vector machine (SVM). Data-driven model, for instance, SVM is very sensitive to the selection of training data. Thus the relevant days data selection method based on Dynamic Time Warping is used to train SVM model. In addition, to encompass thermal inertia of building, pseudo dynamic model is applied since it takes into account information of transition of energy consumption effects and occupancy profile. Relevant days data selection and whole training data model is applied to the case studies of Ecole des Mines de Nantes, France Office building. The results showed that support vector machine based on relevant data selection method is able to predict the energy consumption of building with a high accuracy in compare to whole data training. In addition, relevant data selection method is computationally cheaper (around 8 minute training time) in contrast to whole data training (around 31 hour for weekend and 116 hour for working days) and reveals realistic control implementation for online system as well.Comment: Proceedings of ECOS 2015-The 28th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems , Jun 2015, Pau, Franc

Genome-wide organization of eukaryotic pre-initiation complex is influenced by nonconsensus protein-DNA binding

Genome-wide binding preferences of the key components of eukaryotic pre-initiation complex (PIC) have been recently measured with high resolution in Saccharomyces cerevisiae by Rhee and Pugh (Nature (2012) 483:295-301). Yet the rules determining the PIC binding specificity remain poorly understood. In this study we show that nonconsensus protein-DNA binding significantly influences PIC binding preferences. We estimate that such nonconsensus binding contribute statistically at least 2-3 kcal/mol (on average) of additional attractive free energy per protein, per core promoter region. The predicted attractive effect is particularly strong at repeated poly(dA:dT) and poly(dC:dG) tracts. Overall, the computed free energy landscape of nonconsensus protein-DNA binding shows strong correlation with the measured genome-wide PIC occupancy. Remarkably, statistical PIC binding preferences to both TFIID-dominated and SAGA-dominated genes correlate with the nonconsensus free energy landscape, yet these two groups of genes are distinguishable based on the average free energy profiles. We suggest that the predicted nonconsensus binding mechanism provides a genome-wide background for specific promoter elements, such as transcription factor binding sites, TATA-like elements, and specific binding of the PIC components to nucleosomes. We also show that nonconsensus binding influences transcriptional frequency genome-wide

Pseudo Dynamic Transitional Modeling of Building Heating Energy Demand Using Artificial Neural Network

International audienceThis paper presents the building heating demand prediction model with occupancy profile and operational heating power level characteristics in short time horizon (a couple of days) using artificial neural network. In addition, novel pseudo dynamic transitional model is introduced, which consider time dependent attributes of operational power level characteristics and its effect in the overall model performance is outlined. Pseudo dynamic model is applied to a case study of French Institution building and compared its results with static and other pseudo dynamic neural network models. The results show the coefficients of correlation in static and pseudo dynamic neural network model of 0.82 and 0.89 (with energy consumption error of 0.02%) during the learning phase, and 0.61 and 0.85 during the prediction phase respectively. Further, orthogonal array design is applied to the pseudo dynamic model to check the schedule of occupancy profile and operational heating power level characteristics. The results show the new schedule and provide the robust design for pseudo dynamic model. Due to prediction in short time horizon, it finds application for Energy Services Company (ESCOs) to manage the heating load for dynamic control of heat production system

Simplified modelling of air source heat pumps producing detailed results

Created by the Building Research Establishment (BRE), the Standard Assessment Procedure (SAP) is the UK Government‟s recommended method of assessing the energy ratings of dwellings. Modelling future complex dwellings, and their servicing systems, will require a more advanced calculation which is as simple as SAP to use but can produce more detailed results. This paper extends a novel advanced dynamic calculation method (IDEAS – Inverse Dynamics based Energy Analysis and Simulation) of assessing the controllability of a building and its servicing systems. IDEAS produces SAP compliant results and allows confident (i.e. calibrated in SAP) predictions to be made regarding the impact of novel heating and renewable energy systems. This paper describes the addition of an Air Source Heat Pump (ASHP) model to IDEAS. This allows for detailed analysis to be made of ASHPs in a SAP compliant framework. The benefits of using the IDEAS method is highlighted with capabilities outwith the scope of SAP also possible. For example, IDEAS can be used as sizing tool for a heating system in a building