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Neighbourhood form imposes complex effects on the local microclimate. The overlook of neighbourhood microclimate will lead to miscalculation of building energy performance. In this paper, a neighbourhood-scale building simulation coupled between building energy simulation and CFD is proposed to assess the impact of external convective heat transfer coefficient (CHTC) to the building thermal performance. The co-simulation is run for different types of buildings inside the neighbourhood with different combinations of neighbourhood form parameters. The developed simulation scheme allows the iterations of neighbourhood design parameters and simulation model generation in CFD and EnergyPlus. Five empirical methods are used to predict CHTC, and the results of CHTC values, heating and cooling load intensity are compared in detail with the proposed CFD coupled method. The results suggest that the orientation and multiplier are crucial to the building load intensity in Chicago and the neighbourhood form can affect the thermal load of residential building up to about +27.1% and −18.6%, and +17.2% and −7.7% for office buildings. The proposed method and framework in this paper is capable of generating design guidelines for the optimal energy performance of the buildings in the neighbourhood.

The solar chimney is one concept currently explored by scientists and designers for reducing heat gain and inducing natural cooling in both commercial and residential buildings. Solar chimneys can be installed on the building walls and roofs. In this sense, a significant amount of research work has been done on solar chimney since the 1990s. This paper presents an overview of transient mathematical models for solar chimneys with/without a phase change material. The review focuses on the Global Energy Balance (GEB) models and a short description of the main works is reported. The analysis of the literature on the solar chimney modeling using GEBhighlights that the transient mathematical model are scarce, from the authors' knowledge, there are only 24 published works and only six studies incorporated a phase change material (PCM) in a solar chimney. Five steady mathematical models available in the literature were analyzed, as well as the advantages and disadvantages of each model. Based on the analysis, it is concluded that the model - III and model - V are more appropriate to model the transient state with respect to the remaining models, and therefore the unsteady mathematical model –III and model - V were developed and shown. These models are highly recommended to be implemented as a solar chimney model in a Building Energy Simulation (BES) program. Finally, we propose a new transient mathematical model for a double-channel solar chimney, which incorporated a phase change material (PCM). This new mathematical model allows building designers and engineers to predict the potential benefit that a solar chimney with PCMmay have for ventilation. Therefore, this paper would provide a valuable reference for further research into the field of solar chimneys with a PCMand its applications.

In the early architectural design and energy saving reconstruction of building, it is essential need the typical meteorological year（TMY）for building performance analysis. The accuracy of the typical meteorological year data is directly impact the energy simulation results. In order to obtain accurate TMY data for construction engineering applications, the paper does the following work: (1) Reviewed the progress of the typical meteorological year; (2) Several TMY generation methods were compared using the same raw weather data, discusses the applicability of these methods for the cities of typical climate zones in China. Based on the most recent and comprehensive first-hand ground observation data, this paper adopts three methods to generate TMY for the typical city covering the climate zoning of China. Result shows different TMY generation method has applicability in different region, and through the simulation of building energy consumption, the comprehensive method has obvious advantages. It shows that in a region with diverse regional climate characteristics such as China, one certain method for selecting TMY is not enough, and comprehensive methods should be proposed to meet the needs of different building types and energy consumption simulation.

The mining industry is the largest electricity consumer in Chile. Sustainability reports of Chilean mining companies show that electricity consumption of mining camps is 350–500 kWh/m2 per year. Despite cold climate conditions, mining camps show overheating, and 40% of the miners find them uncomfortable. Mining camps’ energy access is difficult because they are located in remote zones. This paper aims to optimize the building envelope and HVAC system to minimize the total energy consumption and eliminate the overheating risk of a real mining camp located at 4400 m.a.s.l. The mining camp is 30,000 m2, built of timber prefabricated lightweight modules and hosts 1700 workers. The electricity consumption of the baseline case is 330 kWh/m2year and shows overheating. Multi-objective optimization is implemented to minimizing the electricity consumption while avoiding overheating. A hybrid multidimensional optimization algorithm implemented in GenOpt, a building energy simulation program (EnergyPlus) and several scripts developed in Pyhthon for optimizing discrete variables and calculating the overheating risk of each thermal zone are coupled. Two different cases are optimized depending on the heating systems: electric heaters (Case I), which is the current situation; and heat pumps with chilled beams with free cooling option (Case II). This paper shows that an efficient HVAC system (Case II) is crucial for achieving thermal comfort and minimizing electricity consumption, which reaches 112.9 kWh/m2year, representing a significant reduction of 66% compared to the baseline case. The optimization process provides not only the optimum set of energy-efficient strategies but also a set of feasible solutions close to the optimum that allows flexibility to choose other strategies based on economic, transportation and on-site construction constraints.

Worldwide, the residential buildings are consuming a considerable amount of energy. The high potential of buildings towards energy efficiency has drawn special attention to the passive design parameters. A comprehensive study on optimal passive design for residential buildings is presented in this paper. Twenty-five different climates are simulated with the aim to produce best practices to reduce building energy demands (for cooling and heating) in addition to the life-cycle cost (LCC). The occupants' adaptive thermal comfort is also improved by implementing the appropriate passive cooling strategies such as blinds and natural ventilation. In this respect, the implemented methodology is composed of four phases: building energy simulation, optimization, Multi-criteria Decision Making (MCDM), sensitivity study, and finally an adaptive comfort analysis. An optimal passive solution of the studied building indicates the potential to save up to 54%, 87% and 52% of the cooling demands (Qcool), heating demands (Qheat) and LCC respectively with respect to the initial configuration. The obtained optimal passive parameters are validated with the National Renewable Energy Laboratory NREL benchmark for low energy building's envelope. Additionally, the integrated passive cooling strategies have demonstrated its competency since it leads to a significant overheating decrease.

Buildings contribute to about 40% of total annual energy consumption in the U.S. and saving a small percentage of that can lead to significant economic and environmental impacts. Building Energy Modeling (BEM) tools are important in reducing building's energy consumption. Most of the available tools only provide total energy consumption related to HVAC system, lighting, hot water, and appliances. While fine-grained data may be available in some computer tools, the process for obtaining these data is time-consuming, error-prone, and requires software-related skills. This paper explains a developed tool that contributes in automation of building energy simulation and providing fine-grained outputs by using Building Information Modeling (BIM) and modified source code of energy simulation tools such as EnergyPlus and OpenStudio. This tool, which is referred to as ABEMAT (Automated Building Energy Modeling and Assessment Tool), receives gbXML file and provides users with the amount of heat transfer through every single building envelope component such as windows, doors, and walls through an automated process. ABEMAT's and EnergyPlus advanced outputs are compared for validation; the latter being a time consuming process and error-prone, which requires software-related skills compared to ABEMAT. Results showed similar findings indicating the functionality of ABEMAT in providing fine-grained outputs.

This paper examines the possibility of increasing Northern Sweden’s degree of self-sufficiency in food supply, at the 65th latitude, by using a data center as a heating source for greenhouse production. A dynamic building energy simulation software was used to compute both the hourly exhaust air output from a 1 MW data center for one year and the corresponding heating demand for two different greenhouse sizes, 2000 m2 and 10 000 m2, and two different production scenarios. Partial year production, 1 Mars – 15 October, without grow lights and full-year production with grow lights. The study showed that 5.5–30.5% of the electrical input to a 1 MW data center could be recovered. The 2000 m2 greenhouse could operate almost entirely, 89.7–97.9%, on excess heat while only 50.0–61.5% of the 10 000 m2 greenhouse heating demand could be met for full- and partial-year production, respectively. Furthermore, it is concluded that the 10 000 m2 greenhouse with full year production was the most prominent case and would cost-effectively yield 7.6% of northern Sweden’s vegetable self-sufficiency.

© 2015 Elsevier Ltd. Both new and existing buildings need to be adapted to climate change, in order to keep providing a comfortable and healthy indoor climate. Preferably, the adaptation measures applied at the building level scale do not require additional energy (i.e. passive measures). Previous studies showed that passive climate change adaptation measures can have a positive effect on thermal comfort in summer and its shoulder seasons in non-air-conditioned residential buildings. In this paper, the effect of these passive climate adaptation measures - applied at building component level - on the cooling and heating energy demand of a terraced house is analyzed using building energy simulations. It is shown that for this particular case the required cooling energy can be limited to a large extent (59-74%) when external solar shading or additional natural ventilation is applied. In addition, it is shown that for a well-insulated terraced house the energy cost for heating is not strongly affected by the application of passive climate change adaptation measures. publisher: Elsevier articletitle: Analysis of the predicted effect of passive climate adaptation measures on energy demand for cooling and heating in a residential building journaltitle: Energy articlelink: http://dx.doi.org/10.1016/j.energy.2015.11.036 content_type: article copyright: Copyright © 2015 Elsevier Ltd. All rights reserved. ispartof: Energy vol:94 pages:811-820 status: published

BESMs (building energy simulation models) play an important role in the design, optimization and retrofit of buildings. Developing a BESM is relatively simple in the building design phase because nearly all inputs are known from design parameters. However, in the building operation phase, developing and calibrating a BESM becomes difficult because all operating parameters must be adjusted according to real-time data. All of these parameters are difficult to measure, and they vary over time. Existing calibration methods of BESMs, which involve hundreds of input parameters, lack standard procedures and require specialized engineers. Engineers must randomly adjust input parameters until the output energy use matches measured energy use. To solve the problem above, a new calibration approach with a detailed procedure is proposed in this paper. This approach relies on electricity submetering data and HVAC (Heating, Ventilation and Air Conditioning) cooling/heating loads. These data are becoming more available in commercial buildings. A case study is demonstrated in a large commercial building with satisfying results. The CV (coefficient of variation) and MBE (mean bias error) of the total hourly electricity consumption simulation, excluding HVAC, are 4% and 3%, respectively. The CVs of an HVAC system are 12% (chiller), 6% (pump) and 5% (fan), and the MBEs are 10% (chiller), 5% (pump) and 4% (fan).

It was found from monitored data from eight dwellings in a case study building in Quebec City (Canada) that there are clear differences in the window opening behavior between different households. This paper aims to develop from data a probabilistic window opening model that accounts for occupant behavior. Logit regression is employed to predict the state (opened/closed) of windows according to indoor and outdoor temperatures environmental and temporal parameters. To replicate the diversity of behavior, normal distribution functions applied to the logit regression coefficients are used so that simulated occupants respond differently to environmental stimuli. It was found that the model offers good prediction for the monitoring by only using the outdoor and indoor temperatures as predictors. The proposed methodology was tested by simulating 10,000 times a full validation year of the case study building and comparing the results with measured data. The agreement was good. The model overestimated slightly the total frequency of window opening in the dwellings and the number of window changes-of-state. A vast range of window opening behavior was generated by the model, showing its ability to reproduce both the aggregated window opening behavior and the diversity of behaviors of the case study building.

In a residential building, a balcony of an upper floor can act as an overhang; and provide solar shading and reduction in electricity consumption of air-conditioner (A/C) for a flat on the underneath floor. However, some residential flats located on the lower levels may receive substantial self-shading effect from some adjacent flats in the same building block, leading to an insignificant shading effect from a balcony. As there is substantial amount of energy consumed and pollutant generated during the production and disposal of a balcony, it is vital to investigate the energy and environmental performance of residential flats installed with balconies at various floor levels. The objective of this study is to investigate an appropriate floor level of a residential building above which balconies should be incorporated. A 21-story residential building was modeled using EnergyPlus. Simulation results indicated that, for a west-facing flat, only the flats located on 15/F to 20/F can give acceptable environmental payback periods, ranging from 58.3 years to 40.7 years, i.e. within the lifespan (60 years) of a building. The corresponding annual savings in A/C consumption range from 234.9 MJ (2.60%) to 336.7 MJ (3.57%). The research methodology and findings are presented in this paper.