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.     

New artificial neural network prediction method for electrical consumption forecasting based on building end-uses

Due to the current high energy prices it is essential to find ways to take advantage of new energy resources and enable consumers to better understand their load curve. This understanding will help to improve customer flexibility and their ability to respond to price or other signals from the electricity market. In this scenario, one of the most important steps is to carry out an accurate calculation of the expected consumption curve, i.e. the baseline. Subsequently, with a proper baseline, customers can participate in demand response programs and verify performed actions. This paper presents an artificial neural network (ANN) method for short-term prediction of total power consumption in buildings with several independent processes. This problem has been widely discussed in recent literature but a new point of view is proposed. The method is based on two fundamental features: total consumption forecast based on independent processes of the considered load or end-uses; and an adequate selection of the training data set in order to simplify the ANN architecture. Validation of the method has been performed with the prediction of the whole consumption expressed as 96 active energy quarter-hourly values of the Universitat Politècnica de València, a commercial customer consuming 11,500 kW.     

Energy performance optimization of radiant slab cooling using building simulation and field measurements

Few field studies of energy performance of radiant cooling systems have been undertaken. A recently constructed 17,500 m² building with a multi-floor radiant slab cooling system in the tower was investigated through simulation calibrated with measured building energy use and meteorological data. For the very cold, dry region where the building was located, it was found that a typical floor of the tower would have had 30% lower annual energy use with a conventional variable air volume system than with the as-built radiant cooling-variable air volume combination. This was due to (1) simultaneous heating and cooling by the existing radiant cooling and air systems, (2) the large amount of free cooling possible in this climate, and (3) suboptimal control settings. If these issues were remedied and combined with improved envelope and a dedicated outdoor air system with exhaust air heat recovery, a typical floor could achieve annual energy use 80% lower than a typical floor of the existing building HVAC system. This shows that radiant thermal control can make a significant contribution to energy-efficiency, but only if the building design and operating practices complement the strengths of the radiant system.     

Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption

There are several ways to attempt to model a building and its heat gains from external sources as well as internal ones in order to evaluate a proper operation, audit retrofit actions, and forecast energy consumption. Different techniques, varying from simple regression to models that are based on physical principles, can be used for simulation. A frequent hypothesis for all these models is that the input variables should be based on realistic data when they are available, otherwise the evaluation of energy consumption might be highly under or over estimated.In this paper, a comparison is made between a simple model based on artificial neural network (ANN) and a model that is based on physical principles (EnergyPlus) as an auditing and predicting tool in order to forecast building energy consumption. The Administration Building of the University of São Paulo is used as a case study. The building energy consumption profiles are collected as well as the campus meteorological data.Results show that both models are suitable for energy consumption forecast. Additionally, a parametric analysis is carried out for the considered building on EnergyPlus in order to evaluate the influence of several parameters such as the building profile occupation and weather data on such forecasting.     

人工智能新技术在智能建筑中的应用研究

本文分析了人工智能新技术的应用,指出当前引入人工智能技术实现建筑系统智能控制与管理已成为可能。     

Application of artificial neural networks for predicting the cuttability of rocks by drag tools

Many models have previously been developed for predicting specific cutting energy (SE), being the measure of rock cuttability, from intact rock properties employing conventional multiple linear or nonlinear regression techniques. Artificial neural networks (ANN) also have a great potential in building such models. This paper is concerned with the application of ANN for the prediction of cuttability of rocks from their intact properties. For that purpose, data obtained from three different projects were subjected to statistical analyses using MATLAB. Principal components analysis together with the scatterplots of SE against intact rock properties were employed to select the predictors for SE models. Results of the principal components analysis have shown that the most of the variance in the data set can be explained by three principal components. Principal component with the highest variance is weighted mainly on the uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), static modulus of elasticity (Elasticity), and cone indenter hardness (CI), which were regarded as the independent variables driving the data set. Three predictive models for SE were developed employing above independent variables by multiple nonlinear regression with forward stepwise method and ANN, respectively. Neural networks were developed for two different numbers of hidden neurons in the hidden layer. Goodness of the fit measures revealed that ANN models fitted the data as accurately as multiple nonlinear regression model, indicating the usefulness of artificial neural networks in predicting rock cuttability.     

Application of artificial intelligence to rock mechanics: An overview

Different artificial intelligence(AI)methods have been applied to various aspects of rock mechanics,but the fact that none of these methods have been used as a standard implies that doubt as to their generality and validity still exists.For this,a literature review of application of AI to the field of rock mechanics is presented.Comprehensive studies of the researches published in the top journals relative to the fields of rock mechanics,computer applications in engineering,and the textbooks were conducted.The performances of the AI methods that have been used in rock mechanics applications were evaluated.The literature review shows that AI methods have successfully been used to solve various problems in the rock mechanics field and they performed better than the traditional empirical,mathematical or statistical methods.However,their practical applicability is still an issue of concern as many of the existing AI models require some level of expertise before they can be used,because they are not in the form of tractable mathematical equations.Thus some advanced AI methods are still yet to be explored.The limited availability of dataset for the AI simulations is also identified as a major problem.The solutions to the identified problems and the possible future research focus were proposed in the study subsequently.     

A new methodology towards determining building performance under modified outdoor conditions

Great efforts have been made to establish the influence of the urban climate upon the energy consumption of buildings. While many scientific articles present measured data of increased energy consumption due to building surroundings, this paper aims to present a straightforward methodology for the assessment of building performance under modified outdoor conditions. Designers and urban planners should benefit from the results of this paper in their evaluation of proposals to decrease building energy consumption. A number of examples are discussed in order to illustrate the methodology outlined.     

Uncertainty analysis in building performance simulation for design support

Building performance simulation (BPS) has the potential to provide relevant design information by indicating directions for design solutions. A major challenge in simulation tools is how to deal with difficulties through large variety of parameters and complexity of factors such as non-linearity, discreteness, and uncertainty.The purpose of uncertainty and sensitivity analysis can be described as identifying uncertainties in input and output of a system or simulation tool [1], [2] and [3].In practice uncertainty and sensitivity analysis have many additional benefits including: (1) With the help of parameter screening it enables the simplification of a model [4]. (2) It allows the analysis of the robustness of a model [5]. (3) It makes aware of unexpected sensitivities that may lead to errors and/or wrong specifications (quality assurance) [6], [7], [8], [9] and [10]. (4) By changing the input of the parameters and showing the effect on the outcome of a model, it provides a “what-if analysis” (decision support). [11].In this paper a case study is performed based on an office building with respect to various building performance parameters. Uncertainty analysis (UA) is carried out and implications for the results considering energy consumption and thermal comfort are demonstrated and elaborated. The added value and usefulness of the integration of UA in BPS is shown.     

Integrated simulation through the source-code coupling of component models from a modular simulation environment into a comprehensive building performance simulation tool

No single building performance simulation program contains sufficient capabilities and flexibility to fully respond to the full complexity of modern building design and analysis. Consequently, considerable efforts and advances have been made to facilitate the integrated use of multiple simulation tools to provide more extensive modelling capabilities. The research reported in this article has made a contribution towards the goal of integrated simulation by focusing on the internal coupling of component models from a modular simulation environment into a comprehensive building performance simulation tool. A flexible and extensible facility has been designed and developed to enable the use of HVAC component models (TYPEs) from the TRNSYS simulation program within the ESP-r simulation platform. With this, the source code for any number of TRNSYS TYPEs can be compiled with the ESP-r source code to produce an integrated simulation tool that possesses greater capabilities than either simulation program alone.     

Selection criteria for building performance simulation tools: contrasting architects' and engineers' needs

This article summarises a study undertaken to reveal potential challenges and opportunities for using building performance simulation (BPS) tools. The article reviews current trends in building simulation and outlines major criteria for BPS tool selection and evaluation based on analysing users' needs for tools capabilities and requirement specifications. The research is carried out by means of a literature review and two online surveys. The findings are based on an inter-group comparison between architects and engineers. The aim is to rank BPS tool selection criteria and compare 10 state-of-the-art BPS tools in the USA market. Five criteria are composed to stack up against theories and practices of BPS. Based on the experience gained during the survey, suggested criteria are critically reviewed and tested. The final results indicate a wide gap between architects' and engineers' priorities and tool ranking. This gap is discussed and suggestions for improvement of current tools are presented.     

Building typologies as a tool for assessing the energy performance of residential buildings - A case study for the Hellenic building stock

Successful strategies towards minimizing the energy consumption and greenhouse gas emissions attributed to the building sector require knowledge on the energy-related characteristics of the existing building stock. Despite the numerous studies on energy conservation applications in buildings, current knowledge on the energy-related characteristics of the building stock still remains limited. Building typologies can be a useful instrument to facilitate the energy performance assessment of a building stock. This work is based on a harmonised structure for European building typologies (TABULA) developed for residential buildings, but the methodology may be extended to the tertiary sector as well. National typologies are sets of model buildings with characteristic energy-related properties representative of a country's building stock. The model buildings are used as a showcase for demonstrating the energy performance and the potential energy savings from typical and advanced energy conservation measures (ECMs) on the thermal envelope and the heat supply system. The proposed Hellenic residential building typology is presented for the first time along with an assessment of various ECMs that are used for an estimate of the energy performance of building stock in Greece in an effort to meet the 9% indicative national energy savings target by 2016.     

Modelling of cowl performance in building simulation tools using experimental data and computational fluid dynamics

Exhaust cowls are used in conjunction with hybrid ventilation systems to efficiently convert wind energy into negative pressure and thus minimize the electrical energy required by the extract fan. Yet the fact that cowl performance is largely dictated by operating conditions imposes particularly stringent demands on modelling. This paper demonstrates, by way of a concrete example, the need for and potential benefits of a new methodological approach to the modelling of cowls. The study focuses on a specific modelling strategy, applied within a building simulation program, for a cowl used in a hybrid ventilation system. The method is progressively simplified to produce four variants, which chiefly vary according to their level of detail and, hence, the associated modelling effort. Wind pressure coefficients at facade, above roof and in the cowl are needed for all model variants. Some of the investigated variants rely on CFD computations of airflow around the building to determine these values. This study uses the example of a single-family house (SFH) to identify those criteria requiring particular attention in the performance of CFD numerical flow analyses. All four variants are examined on the basis of this example to determine which simplifications to the model are appropriate and permissible without unduly compromising the accuracy of the results.     

方案设计阶段建筑性能模拟方法综述

从模拟辅助设计的过程分析、建筑群性能模拟、建筑单体性能模拟和建筑性能评价方法四个方面回顾了建筑方案设计阶段节能设计的研究成果,分析了现有研究方法的不足,指出解决现有问题应首先研究方案设计阶段的特点,其次分析模拟输入参数的可获得性,并在模拟中引入不确定性分析。     

The use of normative energy calculation beyond building performance rating

Standardized building performance assessment is best expressed with a so-called normative calculation method, such as defined in the Committee for Standardization/International Organization for Standardization (CEN/ISO) calculation standards. The normative calculation method has advantages of simplicity, transparency, robustness and reproducibility. For systematic energy performance assessment at various scales, i.e. at the unit of analysis of one building up to a large-scale collection of buildings, the authors' group developed the Energy Performance Standard Calculation Toolkit (EPSCT). This toolkit calculates objective indicators of energy performance using either the monthly or hourly calculation method as specified in the CEN/ISO standard for building energy calculation. The toolkit is the foundation for numerous single, medium-scale and large-scale building energy management applications. At the largest level, applications should be able to manage hundreds or thousands of buildings. The paper introduces two novel applications that have the normative calculation at their core: (1) network energy performance modelling and (2) agent-based building stock energy modelling.     

Moisture performance of building materials: From material characterization to building simulation using the Moisture Buffer Value concept

Predicting the indoor air relative humidity evolution is of great importance to evaluate people thermal comfort, perceived air quality and energy consumption. In building environments, porous materials of the envelope and furniture act on the indoor air humidity by reducing its variations. Solving the physical processes involved inside the porous materials requires the knowledge of the material hygrothermal properties that needs multiple and, for some of them, time-consuming experimental procedures. Recently, both the NORDTEST Project and Japanese Industrial Standard described a new Moisture Buffer Capacity index that accounts for surrounding air vapor concentration variation. The Moisture Buffer Value (MBV) indicates the amount of water vapor that is transported in or out of a material, during a certain period of time, when the vapor concentration of the surrounding air varies. The MBV evaluation requires only one experimental procedure and its value permits a direct comparison of the building materials moisture performance. However, two limitations can be distinguished: first, no relation between the MBV and the usual material hygrothermal properties has been clearly identified and second, no model has been proposed to actually use the MBV in building simulation. The present study aims to solve these two problems. First, the MBV fundamentals are introduced and discussed; followed by its relation with the usual material properties. Then, a lumped model for building simulation, whose parameters can be determined from the MBV experimental procedure, is described. To finish, examples of the use of this MBV-based lumped model for moisture prediction in buildings are presented.     

Climatic zoning and its application to Spanish building energy performance regulations

The main requirements of European Directive 2002/91/EC on the energy performance of buildings (EPBD) are the application of minimum energy performance requirements for new buildings and certification of the energy performance of buildings. Its transposition into the national law of member states of the European Union has signified the appearance of new and more onerous requirements in terms of construction quality from the point of view of energy performance, and in terms of the procedure for certification of the energy performance of buildings. In both cases, the levels required tend to be based on climatic conditions, fundamentally in countries such as Spain, where the climatic variability is very pronounced. This paper presents a methodology developed for the climatic zoning of the localities not included in the above-mentioned regulations in order to facilitate their application. By way of example, the method is shown which was used to generate the climatic files and carry out the subsequent climatic zoning of all the municipalities of the region of Andalusia in southern Spain.     

Predicting the risk of contractor default in Saudi Arabia utilizing artificial neural network (ANN) and genetic algorithm (GA) techniques

The construction project is subject to several risks, one of the most important of which is contractor default because contractor default may increase the final project cost considerably. In the US construction industry, owners commonly shield themselves from the risk of contractor default by transferring this risk to the contractor, who in turn transfers this risk to a surety company. On the other hand, the General Directorate of Military Works (GDMW) of the Kingdom of Saudi Arabia retains the risk of contractor default rather than transferring it to a third party. An artificial neural network (ANN) and a genetic algorithm (GA) are used in this study to predict the risk of contractor default in construction projects undertaken for the Saudi armed forces. Based on this prediction, the Saudi GDMW can make a decision to engage or not to engage the services of a contractor. In case the models are not able to generate reliable predictions (or generate contradictory outcomes), the GDMW will have to augment its budget with contingency funds to be used in the event of contractor default. The outcome of this study is of particular relevance to construction owners because it proposes an approach that can allow them to replace an indiscriminate blanket policy by a policy that is rational, effective, prudent and economical.     

Calibrating whole building energy models: An evidence-based methodology

This paper reviews existing case studies and methods for calibrating whole building energy models to measured data. This research describes a systematic, evidence-based methodology for the calibration of these models. Under this methodology, parameter values in the final calibrated model reference the source of information used to make changes to the initial model. Thus, the final model is based solely on evidence. Version control software stores a complete record of the calibration process, and the evidence on which the final model is based. Future users can review the changes made throughout the calibration process along with the supporting evidence. In addition to the evidence-based methodology, this paper also describes a new zoning process that represents the real building more closely than the typical core and four perimeter zone approach. Though the methodology is intended to apply to detailed calibration studies with high resolution measured data, the primary aspects of the methodology (evidence-based approach, version control, and zone-typing) are independent of the available measured data.