Simulation and evaluation of Building Information Modeling in a real pilot site

Eguaras-Martínez, M.; Vidaurre-Arbizu, Marina; Martín-Gómez, César

doi:10.1016/j.apenergy.2013.09.047

Cited by 88 publications

(28 citation statements)

References 25 publications

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“…Considering several aspects, the authors in Reference [32] presented an extensive review on the energy internet for its smart management, and several issues were discussed, which include cost, reliability, scalability, data access, and weather as the prime factors for energy internet. However, the real behavior of people in the building was also presented for energy and cost-saving in Reference [33], whereas, in References [34][35][36][37][38], different criteria for the installation of various components were suggested.…”

Section: Introductionmentioning

confidence: 99%

Perspectives and Intensification of Energy Efficiency in Commercial and Residential Buildings Using Strategic Auditing and Demand-Side Management

et al. 2019

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With the ever-growing power demand, the energy efficiency in commercial and residential buildings is a matter of great concern. Also, strategic energy auditing (SEA) and demand-side management (DSM) are cost-effective means to identify the requirements of power components and their operation in the energy management system. In a commercial or residential building, the major components are light sources and heating, ventilation, and air conditioning. The number of these components to be installed depends upon the technical and environmental standards. In this scenario, energy auditing (EA) allows identifying the methods, scope, and time for energy management, and it helps the costumers to manage their energy consumption wisely to reduce electricity bills. In the literature, most of the traditional strategies employed specific system techniques and algorithms, whereas, in recent years, load shifting-based DSM techniques were used under different operating scenarios. Considering these facts, the energy data in a year were collected under three different seasonal changes, i.e., severe cold, moderate, and severe heat for the variation in load demand under different environmental conditions. In this work, the energy data under three conditions were averaged, and the DSM schemes were developed for the operation of power components before energy auditing and after energy auditing. Moreover, the performance of the proposed DSM techniques was compared with the practical results in both scenarios, and, from the results, it was observed that the energy consumption reduced significantly in the proposed DSM approach.

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Section: Introductionmentioning

confidence: 99%

Perspectives and Intensification of Energy Efficiency in Commercial and Residential Buildings Using Strategic Auditing and Demand-Side Management

et al. 2019

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“…Previous studies [23] [24] pointed out that simulation results frequently vary widely from actual energy use of buildings. Eguaras-Martinez, et al [25] proved that the inclusion or exclusion of occupant behaviors in building simulation resulted in up to 30% of the differences in energy use predictions. Furthermore, Hoes, et al [21] Three approaches are primarily used for occupancy modeling: the stochastic approach, the agent-based approach, and the random-walk approach [26].…”

Section: Introductionmentioning

confidence: 99%

A framework for quantifying the impact of occupant behavior on energy savings of energy conservation measures

Sun

Hong

2017

Energy and Buildings

166

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To improve energy efficiency-during new buildings design or during a building retrofit-evaluating the energy savings potential of energy conservation measures (ECMs) is a critical task. In building retrofits, occupant behavior significantly impacts building energy use and is a leading factor in uncertainty when determining the effectiveness of retrofit ECMs. Current simulation-based assessment methods simplify the representation of occupant behavior by using a standard or representative set of static and homogeneous assumptions ignoring the dynamics, stochastics, and diversity of occupant's energy-related behavior in buildings. The simplification contributes to significant gaps between the simulated and measured actual energy performance of buildings.This study presents a framework for quantifying the impact of occupant behaviors on ECM energy savings using building performance simulation. During the first step of the study, three occupant behavior styles (austerity, normal, and wasteful) were defined to represent different levels of energy consciousness of occupants regarding their interactions with building energy systems (HVAC, windows, lights and plugin equipment). Next, a simulation workflow was introduced to determine a range of the ECM energy savings. Then, guidance was provided to interpret the range of ECM savings to support ECM decision making. Finally, a pilot study was performed in a real building to demonstrate the application of the framework. Simulation results show that the impact of occupant behaviors on ECM savings vary with the type of ECM. Occupant behavior minimally affects energy savings for ECMs that are technology-driven (the relative savings differ by less than 2%) and have little interaction with the occupants; for ECMs with strong occupant interaction, such as the use of zonal control variable refrigerant flow system and natural ventilation, energy savings are significantly affected by occupant behavior (the relative savings differ by up to 20%). The study framework provides a novel, holistic approach to assessing the uncertainty of ECM energy savings related to occupant behavior, enabling stakeholders to understand and assess the risk of adopting energy efficiency technologies for new and existing buildings.

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“…Occupant behaviors in buildings have been widely acknowledged as a major factor contributing to the gaps between measured and simulated energy consumption in buildings [19,23,37,38]. Eguaras-Martinez et al [39] [40] recognized the impact of occupant behaviors as one of the six driving factors of energy use in buildings along with climate, building envelope, building energy and services systems, indoor design criteria, and building operation and maintenance. However, in current practices, simulation users tend to apply default standards or representative settings for occupants in a simplified and homogeneous way using temporal schedules and static assumptions.…”

Section: Introductionmentioning

confidence: 99%

A novel stochastic modeling method to simulate cooling loads in residential districts

Yan

Hong

et al. 2017

Applied Energy

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District cooling systems are widely used in urban residential communities in China. Most district cooling systems are oversized; this leads to wasted investment and low operational efficiency and thus energy wastage. The accurate prediction of district cooling loads that supports rightsizing cooling plant equipment remains a challenge. This study developed a new stochastic modeling method that includes (1) six prototype house models representing a majority of apartments in the district, (2) occupant behavior models in residential buildings reflecting the temporal and spatial diversity and complexity based on a large-scale residential survey in China, and (3) a stochastic sampling process to represent all apartments and occupants in the district. The stochastic method was employed in a case study using the DeST simulation engine to simulate the cooling loads of a real residential district in Wuhan, China. The simulation results agree well with the actual measurement data based on five performance metrics representing the aggregated cooling loads, the peak cooling loads as well as the spatial load distribution, and the load profiles. Two currently used simulation methods were also employed to simulate the district cooling loads. The simulation results showed that oversimplified occupant behavior assumptions lead to significant overestimations of the peak cooling load and total district cooling loads. Future work will aim to simplify the workflow and data requirements of the stochastic method to enable its practical application as well as explore its application in predicting district heating loads and in commercial or mixed-use districts.

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Simulation and evaluation of Building Information Modeling in a real pilot site

Cited by 88 publications

References 25 publications

Perspectives and Intensification of Energy Efficiency in Commercial and Residential Buildings Using Strategic Auditing and Demand-Side Management

Perspectives and Intensification of Energy Efficiency in Commercial and Residential Buildings Using Strategic Auditing and Demand-Side Management

A framework for quantifying the impact of occupant behavior on energy savings of energy conservation measures

A novel stochastic modeling method to simulate cooling loads in residential districts

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