经济型酒店能源基准工具研究

我国经济型酒店能源目前常用 EUI（用能强度）单位面积能耗表示,但是将来用 EUI 代理成为发展趋势,在某些发达国家已被广泛采用。讨论了应用酒店能源基准工具应注意的问题。该能源基准利用多元线性回归分析,将 EUI 作为因变量,将若干能耗影响因素作为自变量,应用最小二乘拟合回归方程作为能源基准。该能源基准评价最终用能源利用率（能效比）表示,它是实际 EUI 与预测 EUI 二者之比的百分数。实际 EUI 根据现场能源计算获得,预测 EUI 根据回归方程计算获得。并给出两个能源基准的评价实例。     

Performance comparison of occupancy count estimation and prediction with common versus dedicated sensors for building model predictive control

Model predictive control is a promising approach to optimize the operation of building systems and provide demand-response functionalities without compromising indoor comfort. The performance of model predictive control relies, among other things, on the quality of weather forecasts and building occupancy predictions. The present study compares the accuracy and computational demand of two occupancy estimation and prediction approaches suitable for building model predictive control: (1) count prediction based on indoor climate modeling and parameter estimation “using common sensors”, (2) count prediction based on data from 3D stereovision camera. The performance of the two approaches was tested in two rooms of a case study building. The results show that the method with dedicated sensors outperforms common sensors. However, if a building is not equipped with dedicated sensors, the present study shows that the common sensor method can be a satisfactory alternative to be used in model predictive control.     

A multi-layer approach for estimating the energy use intensity on an urban scale

Various governments are planning their cities to be climate responsive by reducing the energy consumption and carbon emissions according to different scenarios whilst maintaining good indoor comfort conditions. A robust and reliable tool that can estimate the Energy Use Intensity (EUI) of a city is required. This paper presents a new bottom-up engineering-based multi-layer approach able to analyse the energy performance of existing settlements of every size by retaining as much information as possible about their complexities. The process involves i) creating a 3D model of the urban area, ii) building up templates representing different building characteristics such as functions, the age-band of the buildings and operating schedules, iii) running dynamic thermal simulations and iv) displaying the EUI or total energy demand in the 3D model which can be post-processed for further analysis. This approach offers a flexible simulation process according to various purposes, which is particularly useful in decision-making for urban energy retrofitting or planning for new areas. The hourly high-resolution outcomes would benefit the detailed analysis of energy efficiency strategies in order to achieve carbon reduction. The application of this approach is demonstrated for the case of Yuzhong district in Chongqing municipality, China.     

Decoupling of building energy use and climate

Energy use intensity (EUI) and climate have a well documented correlation, which is generally applied in building energy management. Green buildings have sought to greatly reduce energy consumption and a number of examples are documented in the literature. A sample of high performance buildings constructed in a variety of global locations is analyzed here, and provides evidence that measures to reduce energy consumption have reduced EUI to the point where its correlation with heating degree days is no longer apparent. This result suggests that end-user behaviour is the next major hurdle in lowering the energy consumption of greener buildings.     

Modeling and predicting building's energy use with artificial neural networks: Methods and results

This paper discusses how neural networks, applied to predict energy consumption in buildings, can advantageously be improved, guided by statistical procedures, such as hypothesis testing, information criteria and cross validation. Recent literature has provided evidence that such methods, commonly used independently, when exploited together, can improve the selection and estimation of neural models.We use such an approach to design feed forward neural networks for modeling energy use and predicting hourly load profiles, where both the relevance of input variables and the number of free parameters are systematically treated. The model building process is divided in three parts: (a) the identification of all potential relevant input, (b) the selection of hidden units for this preliminary set of inputs, through an additive phase and (c) the remove of irrelevant inputs and useless hidden units through a subtractive phase.The predictive performance of short term predictors is also examined with regard to prediction horizon. A comparison of the predictive ability of a single-step predictor iteratively used to predict 24 h ahead and a 24-step independently designed predictor is presented.The performance of the developed models and predictors was evaluated using two different data sets, the energy use data of the Energy Prediction Shootout I contest, and of an office building, located in Athens. The results show that statistical analysis as an integral part of neural models, gives a valuable tool to design simple, yet efficient neural models for building energy applications.     

BIM IFC information mapping to building energy analysis (BEA) model with manually extended material information

To reduce fossil fuel based energy consumption in buildings, different methods have been proposed. Interestingly, one of the most significant factors in building energy consumption has been reported in the area of improving building designs. However, building energy analysis (BEA) is typically conducted late in design, by energy analyst specialists. The ability to try out new ideas early in the design process in order to choose the best alternative is not ordinarily taken advantage of, due to the difficulty and expense of modeling the building and energy systems. Building information modeling (BIM) provides the user with an opportunity to explore different energy saving alternatives in the design process while avoiding the time-consuming process of re-entering all the building geometry, enclosure, and HVAC information necessary for a complete energy analysis.While significant time savings are being made by not having to create the building geometry within the simulation interface in BIM energy modeling simulation, there is a good possibility of missing, misplaced, or deformed building elements during a BIM data exchange process. This research focuses on one of the major limitations – inaccuracies through simplifications in construction/material data – and aims to improve the accuracy of energy modeling process by developing an object based approach in materials in which the energy modeler may change and expand various properties in building materials. In testing the performance of the proposed approach, the results from the proposed energy modeling process in the case study are compared to those of existing energy modeling software which showed significant gains in accuracy.     

Development of Dutch occupancy and heating profiles for building simulation

Building simulations are often used to predict energy demand and to determine the financial feasibility of the low-carbon projects. However, recent research has documented large differences between actual and predicted energy consumption. In retrofit projects, this difference creates uncertainty about the payback periods and, as a consequence, owners are reluctant to invest in energy-efficient technologies. The differences between the actual and the expected energy consumption are caused by inexact input data on the thermal properties of the building envelope and by the use of standard occupancy data. Integrating occupancy patterns of diversity and variability in behaviour into building simulation can potentially foresee and account for the impact of behaviour in building performance. The presented research develops and applies occupancy heating profiles for building simulation tools in order create more accurate predictions of energy demand and energy performance. Statistical analyses were used to define the relationship between seven most common household types and occupancy patterns in the Netherlands. The developed household profiles aim at providing energy modellers with reliable, detailed and ready-to-use occupancy data for building simulation. This household-specific occupancy information can be used in projects that are highly sensitive to the uncertainty related to return of investments.     

A systematic procedure to study the influence of occupant behavior on building energy consumption

Efforts have been devoted to the identification of the impacts of occupant behavior on building energy consumption. Various factors influence building energy consumption at the same time, leading to the lack of precision when identifying the individual effects of occupant behavior. This paper reports the development of a new methodology for examining the influences of occupant behavior on building energy consumption; the method is based on a basic data mining technique (cluster analysis). To deal with data inconsistencies, min-max normalization is performed as a data preprocessing step before clustering. Grey relational grades, a measure of relevancy between two factors, are used as weighted coefficients of different attributes in cluster analysis. To demonstrate the applicability of the proposed method, the method was applied to a set of residential buildings’ measurement data. The results show that the method facilitates the evaluation of building energy-saving potential by improving the behavior of building occupants, and provides multifaceted insights into building energy end-use patterns associated with the occupant behavior. The results obtained could help prioritize efforts at modification of occupant behavior in order to reduce building energy consumption, and help improve modeling of occupant behavior in numerical simulation.     

Numerical investigation on thermal performance and correlations of double skin façade with buoyancy-driven airflow

This paper briefly reviews the primary parameters for a double skin façade (DSF) design. The research presents an integrated and iterative modeling process for analyzing the thermal performance of DSF cavities with buoyancy-driven airflow by using a building energy simulation program (BESP) along with a computational fluid dynamics (CFD) package. A typical DSF cavity model has been established and simulated. The model and the modeling process have been calibrated and validated against the experimental data. The validated model was used to develop correlations that can be implemented in a BESP, allowing users to take advantage of the accuracy gained from CFD simulations without the required computation time. Correlations were developed for airflow rate through cavity, average and peak cavity air temperature, cavity air pressure, and interior convection coefficient. The correlations are valuable for “back of the envelope” calculation and for examining accuracy of zonal-model-based energy and airflow simulation programs.     

Modeling the energy retrofit decision in commercial office buildings

Retrofitting existing buildings has emerged as a primary strategy for reducing energy use and carbon emissions, both nationally and in cities. Despite the increasing awareness of retrofitting opportunities and a growing portfolio of successful case studies, little is known about the decision-making processes of building owners and asset managers with respect to energy efficiency investments. Specifically, the research presented here examines the effects of ownership type, tenant demand, and real estate market location on building energy retrofit decisions in the commercial office sector. This paper uses an original, detailed survey of asset managers of 763 office buildings in nineteen cities sampled from the CBRE, Inc. portfolio. Controlling for various building characteristics, the results demonstrate that ownership type and local market do, in fact, influence the retrofit decision.Overall, this analysis provides new evidence for the importance of understanding ownership type and the varying motivations of differing types of owners in building energy efficiency investment decisions. The findings of both the survey analysis and the predictive model demonstrate additional support for the targeting of energy efficiency incentives and outreach based on ownership entity, local market conditions, and specific physical building characteristics.     

Method for modelling space conditioning aggregated daily load curves: Application to a university building

Physically based load modelling methodologies have been widely developed and used because of their ability to predict the energy load dynamic response. Most building energy programs predict energy consumption and energy system performance through a whole building energy simulation as well as a global analysis of building thermal processes and heating, ventilation and air-conditioning (HVAC) system performance. A different approach is presented in this paper by introducing a new method for modelling the daily load profile of a group of air-conditioning systems. This method is based on the simulation of a single HVAC system, a set of end-use electrical measurements, and a detailed walk-through and energy audit. The basic methodology allows deducing the aggregated load of a group of space conditioning devices by the addition of the daily simulation of each individual physical system. As an application, the space conditioning daily demand curve of a university building is studied and results are presented.     

住宅建筑能耗基准确定及用能评价新方法

针对当前建筑能耗基准确定及用能评价方法仍不完善的现状,提出了一种适用于住宅建筑的新方法。通过灰色关联分析确定了不同影响因素(即特征参数)与建筑能耗的关联度,将关联度作为该因素的权值并结合聚类分析对建筑进行合理分类。在此基础上采用累积频率分布法确定了每类建筑的能耗基准值,并对建筑住户进行用能评价。为验证该方法的可行性,将其应用于日本建筑学会所建立的住宅建筑能耗数据库,为聚类后的每类建筑确定了相应的能耗基准并对住户进行了用能评价。结果表明该方法能够综合考虑不同特征参数的影响大小,科学合理地对建筑细化分类,根据分类后的基准评价能够更好地评估住户的节能潜力并提供可行的节能建议。     

Smart grid data analytics framework for increasing energy savings in residential buildings

Human energy consumption has gradually increased greenhouse gas concentrations and is considered the main cause of global warming. Currently, the building sector is a major energy consumer, and its share of energy consumption is increasing because of urbanization. This paper presents a framework for smart grid big data analytics and components required for an energy-saving decision-support system. The proposed system has a layered architecture that includes a smart grid, a data collection layer, an analytics bench, and a web-based portal. A smart metering infrastructure was installed in a residential building to conduct an experiment for evaluating the effectiveness of the proposed framework. Furthermore, a novel hybrid nature-inspired metaheuristic forecast system and a dynamic optimization algorithm are designed behind the analytics bench for achieving accurate prediction and optimization of future energy consumption. The main contribution of this study is that an innovative framework for the energy-saving decision process is presented; the framework can serve as a basis for the future development of a full-scale smart decision support system (SDSS). Through the identification of consumer usage patterns, the SDSS is expected to enhance energy use efficiency and improve the accuracy of future energy demand estimates. End users can reduce their electricity costs by implementing the optimal operating schedules for appliances, which are provided by the SDSS.     

A detailed loads comparison of three building energy modeling programs: EnergyPlus, DeST and DOE-2.1E

Building energy simulation is widely used to help design energy efficient building envelopes and HVAC systems, develop and demonstrate compliance of building energy codes, and implement building energy rating programs. However, large discrepancies exist between simulation results from different building energy modeling programs (BEMPs). This leads many users and stakeholders to lack confidence in the results from BEMPs and building simulation methods. This paper compared the building thermal load modeling capabilities and simulation results of three BEMPs: EnergyPlus, DeST and DOE-2.1E. Test cases, based upon the ASHRAE Standard 140 tests, were designed to isolate and evaluate the key influencing factors responsible for the discrepancies in results between EnergyPlus and DeST. This included the load algorithms and some of the default input parameters. It was concluded that there is little difference between the results from EnergyPlus and DeST if the input values are the same or equivalent despite there being many discrepancies between the heat balance algorithms. DOE-2.1E can produce large errors for cases when adjacent zones have very different conditions, or if a zone is conditioned part-time while adjacent zones are unconditioned. This was due to the lack of a strict zonal heat balance routine in DOE-2.1E, and the steady state handling of heat flow through interior walls and partitions. This comparison study did not produce another test suite, but rather a methodology to design tests that can be used to identify and isolate key influencing factors that drive the building thermal loads, and a process with which to carry them out.     

Analysis of a simplified calibration procedure for 18 design-phase building energy models

This paper evaluates the accuracy of 18 design-phase building energy models, built according to LEED Canada protocol, and investigates the effectiveness of model calibration steps to improve simulation predictions with respect to measured energy data. These calibration steps, applied in professional practice, included inputting actual weather data, adding unregulated loads, revising plug loads (often with submetered data), and other simple updates. In sum, the design-phase energy models underpredicted the total measured energy consumption by 36%. Following the calibration steps, this error was reduced to a net 7% underprediction. For the monthly energy use intensity (EUI), the coefficient of variation of the root mean square error improved from 45% to 24%. Revising plug loads made the largest impact in these cases. This step increased the EUI by 15% median (32% mean) in the models. This impact far exceeded that of calibrating the weather data, even in a sensitivity test using extreme weather years.     

An intelligent approach to assessing the effect of building occupancy on building cooling load prediction

Building cooling load prediction is one of the key factors in the success of energy-saving measures. Many computational models available in the industry have been developed from either forward or inverse modeling approaches. However, these models usually require extensive computer resources and lengthy computation. This paper discusses the use of the multi-layer perceptron (MLP) model, one of the artificial neural network (ANN) models widely adopted in engineering applications, to estimate the cooling load of a building. The training samples used include weather data obtained from the Hong Kong Observatory and building-related data acquired from an existing prestigious commercial building in Hong Kong that houses a mega complex and operates 24 h a day. The paper also discusses the practical difficulties encountered in acquiring building-related data. In contrast to other studies that use ANN models to predict building cooling load, this paper includes the building occupancy rate as one of the input parameters used to determine building cooling load. The results demonstrate that the building occupancy rate plays a critical role in building cooling load prediction and significantly improves predictive accuracy.     

基于LSTM网络的船舶机舱火灾多特征融合探测

针对船舶机舱火灾高效准确探测的需求,建立基于LSTM-ID3 判决的船舶火灾探测方法。首先确定采集船舶火灾特征的三类传感器,然后完成 LSTM 神经网络模型的构建、参数的优化,将 LSTM 神经网络输出的明火、阴燃火、无火的概率值与烟雾持续时间作为决策树的输入量,输出火灾探测结果。利用国家标准火典型数据进行训练,并开展相关试验,对船舶机舱火灾进行探测。试验结果表明,与其他算法进行对比,探测准确率达到97%以上,该方案能对机舱火灾做出有效探测,为船舶安全提供科学依据。     

A new approach to performance-based building design exploration using linear inverse modeling

Despite the development of a large number of building performance simulation tools, designers still need a systematic framework appropriate for energy-oriented decision-making in the early stages of design. While the current workflow follows a “forward” modelling procedure in which simulation tools predict the performance of a design, this study proposes an “inverse” procedure that entails a performance objective that estimates design parameters. Using linear inverse modelling, this approach generates plausible ranges for design parameters given a preferred thermal performance. The paper begins by demonstrating that thermal demand in a particular building operation-and-climate condition can be expressed as a linear regression model and then, in two case-studies, uses the regression model to develop an inverse algorithm. After defining energy performance targets as input, users obtain a probabilistic estimate of design parameters as output that represents a large “menu” of feasible design solutions, provides confidence, and embodies the iterative nature of design.     

Development and validation of regression models to predict monthly heating demand for residential buildings

The present research work concerns development of regression models to predict the monthly heating demand for single-family residential sector in temperate climates, with the aim to be used by architects or design engineers as support tools in the very first stage of their projects in finding efficiently energetic solutions. Another interest to use such simplified models is to make it possible a very quick parametric study in order to optimize the building structure versus environmental or economic criteria. All the energy prediction models were based on an extended database obtained by dynamic simulations for 16 major cities of France. The inputs for the regression models are the building shape factor, the building envelope U-value, the window to floor area ratio, the building time constant and the climate which is defined as function of the sol-air temperature and heating set-point. If the neural network (NN) methods could give precise representations in predicting energy use, with the advantage that they are capable of adjusting themselves to unexpected pattern changes in the incoming data, the multiple regression analysis was also found to be an efficient method, nevertheless with the requirement that an extended database should be used for the regression. The validation is probably the most important level when trying to find prediction models, so 270 different scenarios are analysed in this research work for different inputs of the models. It has been established that the energy equations obtained can do predictions quite well, a maximum deviation between the predicted and the simulated is noticed to be 5.1% for Nice climate, with an average error of 2%. In this paper, we also show that is possible to predict the building heating demand even for more complex scenarios, when the construction is adjacent to non-heated spaces, basements or roof attics.     

Assessing the value of information in residential building simulation: Comparing simulated and actual building loads at the circuit leve

Building energy simulation tools are now being used in a number of new roles such as building operation optimization,performance verification for efficiency programs,and-recently-building energy code analysis,design, and compliance verification in the residential sector. But increasing numbers of studies show major differences between the results of these simulations and the actual measured performance of the buildings they are intended to model. The accuracy and calibration of building simulations have been studied extensively in the commercial sector,but these new applications have created a need to better understand the performance of home energy simulations.In this paper,we assess the ability of the DOE's EnergyPlus software to simulate the energy consumption of 106 homes using audit records,homeowner survey records,and occupancy estimates taken from monitored data.We compare the results of these simulations to device-level monitored data from the actual homes to provide a first measure of the accuracy of the EnergyPlus condensing unit, central air supply fan, and other energy consumption model estimates in a large number of homes.We then conduct sensitivity analysis to observe which physical and behavioral characteristics of the homes and homeowners most influence the accuracy of the modeling.Results show that EnergyPlus models do not accurately or consistently estimate occupied whole-home energy consumption.While some models accurately predict annual energy consumption to within1%of measured data, none of the modeled homes meet ASHRAE criteria for a calibrated model when looking at hourly interval data. The majority of this error is due to appliance and lighting energy overestimates,f ollowed by AC condensing unit use. These inaccuracies are due to factors such as occupant behaviors and differences in appliance and lighting stocks which are not well-captured in traditional energy audit reports.We identify a number of factors which must be specified for an accurate model, and others where using a default value will produce a similar result.The use of building simulation tools reflects a shift from a component-focused approach to a systems approach to residential code analysis and compliance verification that will serve to better identify and deploy efficiency measures in homes. By better understanding the limitations of home energy simulations and adopting strategies to mitigate the effects of model errors, simulation models can serve as valuable decision making tools in the residential sector.