Energy Simulation Tools Research Articles

Model order reduction of building energy simulation models using a convolutional neural network autoencoder

Building energy simulation (BES) tools are fundamental for predicting energy performance and comfort. However, detailed models are computationally complex and demand high simulation times. These lead to difficulties in parametric runs and numerical optimizations. Performing numerous retrofit scenarios is hardly feasible, especially in multi-zone buildings with complex geometries.This paper introduces a novel approach to model order reduction (MOR) of BES models. The approach utilizes a deep learning-based unsupervised convolutional neural network autoencoder (CNN-AE). The method decomposes complex time series data derived from detailed simulations into lower dimension features. The low-dimension representations can be grouped through clustering algorithms to build a reduced-order model (ROM). The approach in this study automatically finds archetype zones of the original model that represent the energy behavior of a group of rooms, and removes redundant ones. The energy demand of the whole building can be estimated through these archetype zones.Our investigation shows that CNN-AE can be efficiently applied to reduce complex building energy simulation models. As proof of concept, a detailed model of a multi-zone campus building with 889 thermal zones is compared to the ROM derived from the CNN-AE. Comprehensive autoencoder hyperparameter training to optimize the accuracy of the model is provided. The ROM supports different purposes, such as energy scenario developments, with a total error of less than 1% compared to the original model, and reduced simulation times by a factor of more than 16.

Analysis of Urban Energy Resources to Achieve Net-Zero Energy Neighborhoods

This paper summarizes a methodology developed to optimize urban-scale energy mix. An optimal capacity estimation method based on energy credits is proposed, the objective of which is to plan renewable and alternative energy sources to yield zero (or positive) year-end energy credits. Several renewable and alternative energy resources are considered, including photovoltaic systems, solar thermal collectors, wind turbines, waste to energy (WtE) potential, as well as thermal seasonal storage. The methodology employs several energy simulation and optimization tools, including Energy Plus, TRNSYS and MATLAB. The optimization employs a non-linear process that uses objective function, boundaries and non-linear/linear constraints as input. The methodology is demonstrated on a hypothetical mixed-use neighborhood, designed to achieve high-energy performance objectives, with three scenarios of energy operations: 1) all electric, 2) all-electric except for DHW, and 3) DHW and space heating arenon-electric. The pilot location of this mixed-use neighborhood, including residential and commercial buildings, is Calgary (AB, Canada). For the all-electric scenario, PV systems implemented in all available south facing roof areas together with a limited number of wind turbines can achieve NZE status. For the other two scenarios, solar thermal collectors coupled to borehole thermal storage (STC and BTES) need to be considered. Although in all cases of the considered scenarios waste-based energy is not required, it can be used to shave the peak electric load, reducing the stress on the grid. This methodology can be employed for the design of an integrated urban energy systems, in different neighborhood designs, to achieve energy self-sufficient, or energy positive status.

How BIM Contributes to a Building’s Energy Efficiency throughout Its Whole Life Cycle: Systematic Mapping

This paper presents a systematic mapping (SM) study with the aim to determine how Building Information Modeling (BIM) methodologies and technologies contribute to energy-related analyses over the course of the entire building life cycle. The method adopted in the study is based on a set of seven research questions. We used a mixed technique combining co-citation analysis and bibliographic coupling in order to analyze the publications’ datasets for the period 2010–2020. The main advantage and novelty of this study are that the joint dataset from the Scopus and Web of Science databases was used to develop the keyword map. The main findings of this study indicate that many BIM-based applications can be used to analyze the building energy performance at all stages of the building life cycle. However, the applications of BIM in conjunction with other information technologies are limited and are still in the initial stage. In the future, the main improvements should be focused on process, model, system, tool, use and information modeling. The most promising long-term solution is an open BIM framework based on open standards, which allows the integration of BIM and energy simulation tools and satisfies specific data exchange requirements.

Thermal comfort prediction of the existing housing stock in southern Spain through calibrated and validated parameterized simulation models

Due to 2030 and 2050 targets of the latest international standards, energetically retrofitting the existing building sector requires special attention. Prior to the proposal of retrofit strategies, it is necessary to analyse the current energy performance of the stock. Although simulation tools provide accurate results of energy performance at the building level, individual assessments of the large-scale stock lead to extensive data collection and huge computational resources. This paper assesses the current performance of one of the most representative building typologies in social housing stock in southern Spain, the H-typology, predicting results on indoor thermal comfort at the stock level. The physical, constructive and geometrical building characterisation and the selection of a calibrated and validated case study through monitoring are used to generate parameterized energy simulation models, providing statistically representative samples of the stock. Open-access energy simulation tools have been combined with statistical software. Conclusions reported show average annual discomfort hours of around 68%, with higher percentage of annual undercooling discomfort hours, and identify the most influential parameters on indoor thermal comfort as infiltration rate, people density and night-time natural ventilation rate. Moreover, 10 Latin Hypercube Samples per parameterized variable derived in highly representative results for thermally analysing the stock.

An Integrated Energy Simulation Model of a Compressed Air System for Sustainable Manufacturing: A Time-Discretized Approach

A compressed air system (CAS) is one of the most common and energy-consuming systems in manufacturing. To practice more economically and environmentally sustainable manufacturing, manufacturers need ways to reduce the energy costs and carbon footprint, resulting from a CAS in their production systems. While preliminary energy studies on a CAS and on machining processes are available separately, existing research studies rarely analyze energy costs using a tool that considers both a CAS and production systems. Therefore, in this study, we propose an energy simulation tool that combines a CAS and a production system to evaluate the effects of a CAS and production parameters on energy consumption and costs at a factory level. In particular, we develop a time-discretized algorithm for simulating a CAS to accurately consider the dynamics of CAS parameters such as pressure and flow rate. From 48 simulation case studies, we show that changes in a CAS such as proper HP sizing, a reduction in compressed air leaks, and a decrease in the discharge pressure can increase productivity and reduce energy costs by up to 11%. The simulation analysis from this study suggests a way to help manufacturers and researchers find more sustainable ways to achieve energy-efficient configurations for production systems including a CAS.

A hierarchical decomposition approach for multi-level building design optimization

With the advancement in and integration of building energy simulation and optimization tools, building design optimization considering different design objectives and involving multiple disciplines has become technically feasible. However, this multifaceted approach unavoidably increases the dimension of the problem and comes with a prohibitively high computational cost. Hierarchical decomposition divides an optimization problem into several interconnected subproblems, and each subproblem has its objectives and constraints. Therefore, a complex problem is represented by a series of manageable subproblems. However, solving subproblems may lead to the local optimal solutions of the original problem. This research proposes a hierarchical decomposition approach that can achieve global optimal solutions. The proposed approach is applied to a multi-objective optimization problem to minimize the carbon emission and operating cost of building. This problem is decomposed into two subproblems to be solved sequentially. The first subproblem is to minimize the energy consumptions of the building. The second subproblem has the same objectives as the original one, but the search space is the result of the first subproblem. The benefit of decomposing this problem is that only the second problem needs to be solved again when additional energy price and carbon emission scenarios to be considered. A mid-rise residential building is selected as a case study to demonstrate the approach. The results show that the decomposition approach can reduce the number of simulations while keeping the full set of solutions of the original problem. The application of the proposed approach is based on a fixed utility rate, and only two objectives are considered for each level of optimization. These limitations could be overcome by adjusting the way to apply this approach.

Design and Energy Performance Analysis of a Hotel Building in a Hot and Dry Climate: A Case Study

In times of unprecedented climate change and energy scarcity, the design and delivery of energy-efficient and sustainable buildings are of utmost importance. This study aimed to design a hotel building for hot and dry climate conditions and perform its energy performance analysis using energy simulation tools. The model of the hotel building was constructed by a graphical tool OpenStudio and EnergyPlus following the ASHRAE Standard 90.1. To reduce the energy demand of the hotel, parametric analysis was conducted and building envelope parameters such as the thickness of insulation layer in the exterior wall and the roof, thermal conductivity of insulation layer, rate of infiltration, U-factor of windows, and thermal resistance of air gap in the interior walls (R-value), window-to-wall ratio, and orientation of the building were tested and the impact on the energy use of the building was analyzed. It was found that most of the design assumptions based on the ASHRAE standard were already optimal for the considered locality, however, were still optimized further to reach the highest efficiency level. Apart from this, three sustainable technologies—thermochromic windows, phase change materials, and solar panels—were incorporated into the building and their energy consumption reduction potential was estimated by energy simulations. Cumulatively, these sustainable technologies were able to reduce the total energy use from 2417 GJ to 1593 GJ (i.e., by 824 GJ or 34%). Calculation of payback period and return on investments showed that thermochromic windows and solar panels have relatively short payback periods and high return on investments, whereas PCM was found to be economically nonviable. The findings of this study are deemed to be useful for designing a sustainable and energy-efficient hotel building in a sub-tropical climate. However, the overall design and energy performance analysis algorithm could be used for various buildings with varying climate conditions.

TESTING THE NEW 3D BAG DATASET FOR ENERGY DEMAND ESTIMATION OF RESIDENTIAL BUILDINGS

Abstract. The 3D BAG v. 2.0 dataset has been recently released: it is a country-wide dataset containing all buildings in the Netherlands, modelled in multiple LoDs (LoD1.2, LoD1.3 and LoD2.2). In particular, the LoD2.2 allows differentiating between different thematic surfaces composing the building envelope. This paper describes the first steps to test and use the 3D BAG 2.0 to perform energy simulations and characterise the energy performance of the building stock. Two well-known energy simulation software packages have been tested: SimStadt and CitySim Pro. Particular care has been paid to generate a suitable, valid CityGML test dataset, located in the municipality of Rijssen-Holten in the central-eastern part of the Netherlands, that has been then used to test the energy simulation tools. Results from the simulation tools have been then stored into the 3D City Database, additionally extended to deal with the CityGML Energy ADE. The whole workflow has been checked in order to guarantee a lossless dataflow.The paper reports on the proposed workflow, the issues encountered, some solutions implemented, and what the next steps will be.

Dynamic thermal behaviour characterization of integrated insulation clay hollow blocks for buildings energy simulations applications

A method is proposed to obtain the thermal properties of hollow clay blocks with integrated insulation for use in building energy simulation tools. Then, we show the significant impact of the interior coating on the dynamic thermal behaviour of these blocks. This method is composed of two numerical and two experimental phases. First, a numerical model in an unsteady regime has been created and calibrated by experimental studies on a wall sample tested in a guarded hot box. Finally, a validation phase is carried out by comparing TRNSYS simulations with in situ test data from a residential building. Finally, we provide reliable values for the thermal properties to be used in energy simulation software (equivalent thermal conductivity of 0.08 W.m−1.K−1, equivalent density of 630 kg.m−3 and equivalent heat capacity of 430 J.kg−1.K−1).

A Hybrid Simulation Model to Predict the Cooling Energy Consumption for Residential Housing in Hong Kong

In Hong Kong, buildings consume 90% of the electricity generated and over 60% of the city’s carbon emissions are attributable to generating power for buildings. In 2018, Hong Kong residential sector consumed 41,965 TJ (26%) of total electricity generated, with private housing accounting for 52% and public housing taking in 26%, making them the two major contributors of greenhouse gas emissions. Furthermore, air conditioning was the major source consuming 38% of the electricity generated for the residential building segment. Strategizing building energy efficiency measures to reduce the cooling energy consumption of the residential building sector can thus have far-reaching benefits. This study proposes a hybrid simulation strategy that integrates artificial intelligence techniques with a building energy simulation tool (EnergyPlus™) to predict the annual cooling energy consumption of residential buildings in Hong Kong. The proposed method predicts long-term thermal energy demand (annual cooling energy consumption) based on short-term (hourly) simulated data. The hybrid simulation model can analyze the impacts of building materials, construction solutions, and indoor–outdoor temperature variations on the cooling energy consumed in apartments. The results indicate that using low thermal conductivity building materials for windows and external walls can reduce the annual cooling energy consumption by 8.19%, and decreasing the window-to-wall ratio from 80% to 40% can give annual cooling energy savings of up to 18%. Moreover, significant net annual cooling energy savings of 13.65% can be achieved by changing the indoor set-point temperature from 24 °C to 26 °C. The proposed model will serve as a reference for building energy efficiency practitioners to identify key relationships between building physical characteristics and operational strategies to minimize cooling energy demand at a minimal time in comparison to traditional energy estimation methods.

Variable-Load Heat Pumps: Impact of the Design and Control Parameters on the Actual Operation Conditions

Nowadays heat pumps (HPs) represent the main alternative to traditional heating systems for the transition to nearly zero-energy buildings. Though HPs are a well-known technology, the estimation of their actual energy performance is still under discussion. Indeed, the proper choice of the HP design parameters (e.g. size, rated supply temperature) and the adopted control strategy can assume a paramount role to cover the mismatch between declared and actual performance of the system. Objective of this work is to analyze this mutual dependence in an operating system to provide guidelines for the design of a residential heating system with a HP. Through a dynamic energy simulation tool, a variable-load air-to-water HP is used to cover the thermal demand of a residential building. The effect of the reciprocal influence of different design choices (e.g. rated heating capacity or design supply temperature) and control strategies (e.g. climatic regulation) is analyzed by simulating different scenarios. To complete the evaluation, the impact of a thermal energy storage is also assessed. The study allows to identify guidelines for the design of different system configurations and results seem to confirm the impact of the investigated parameters on the seasonal performance of the system.

Energy model calibration in an office building by an optimization-based method

In the last years, building energy consumption minimization has come to be an important issue for designers also architects. Different building energy simulation (BES) tools were applied. These programs are efficient to estimate building energy demands and quicken the malfunction assessment. Many of the provided energy simulation tools cannot precisely forecast building energy operation because of numerous interacting variables. To lessen considerable discrepancies between the actual-time data measurements and the simulation achievements, an optimization-based calibration method is presented in this study. Therefore, to minimize this error an optimization algorithm called Slime Mold Optimization (SMO) algorithm is utilized also the energy simulation model. A case study, an office building placed in Dubai, the United Arab Emirates (UAE) in a humid and hot climate region is selected to be modeled and adjusted to show the accuracy of the applied procedure. This case has five floors with 3610 m2 footprint. The building uses only electrical power as the major energy source For the total dataset duration (n = 3216), the MBE of an hour for the calibrated model is equal to 3.24 percent with the CV (RMSE) equal to 11.3 percent. The statistical evaluation has been used for analyzing the precision of the achievements. Based on the results, the procedure is reliable.

Detailed cross comparison of building energy simulation tools results using a reference office building as a case study

Building Energy Simulation (BES) tools play a key role in the optimization of the building system during the different phases, from pre-design through commissioning to operation. BES tools are increasingly used in research as well as in companies. New BES tools and updated versions are continuously being released. Each tool follows an independent validation process but rarely all the tools are compared against each other using a common case study. In this work, the modelling approaches of widespread dynamic simulation tools (i.e. EnergyPlus, TRNSYS, Simulink libraries CarnotUIBK and ALMABuild, IDA ICE, Modelica/Dymola and DALEC), as well as PHPP (a well-known quasi-steady-state tool), are described and the results of all the tools modelling the same characteristic office cell, defined within the IEA SHC Task 56, are compared on a monthly and hourly basis for the climates of Stockholm, Stuttgart and Rome. Unfortunately, different tools require different levels of input detail, which are often not matching with available data, hence the parametrization process highly influences the quality of the simulation results. In the current study to evaluate the deviation between the tools, frequently used statistical indices and normalization methods are analysed and the problems related to their application, in a cross-comparison of different tools, are investigated. In this regard, the deviation thresholds indicated by ASHRAE Guideline 14-2014 are used as a basis to identify results that suggest an acceptable level of disagreement between the predictions of a particular model and the outcomes of all models. The process of reaching a good agreement between all tools required several iterations and great effort on behalf of the modellers. To aid the definition of building component descriptions and future references for inter-model comparison a short history of the executed steps is presented in this work. Together with the comparison of the results of the tools, their computational cost is evaluated and an overview of the modelling approaches supported by the different tools for this case study is provided aiming to support the users in choosing a fit-for-purpose simulation tool.

Evaluation of Energy Performance and Comfort: Case-Study of University Buildings with Design Adapted to Local Climate

One of the main strategies to reduce countries’ energy bills is to invest in efficient buildings. To achieve this objective, the European Union Member States have developed different methodologies to evaluate building energy performance, which are often supported by simulation tools. These tools are based on calculation engines that use databases and simplifications to attempt to bring their results close to real building performance and are mostly designed to be used at the end of the process, neglecting their role in project decision-making processes. To compensate for this situation and to obtain the most accurate results, the methodologies recommend previous work during the building design phase to adopt passive design solutions that learn from experience and aim to adapt the building design to the local climate. However, these design solutions are difficult to adopt while working with medium to large public buildings and are often not properly understood by the simulation tools. In addition, new BIM methodologies are being implemented, starting to enable proper interaction between the designer and the results, and opening up the option of introducing other types of calculations, such as building comfort, in the calculation process. Among the group of countries with limited simulation tools that are starting to be substituted is Spain, which recently launched its first BIM-based energy simulation tool. This tool aims to compensate for the limitations of the former simulation tools and opens up the option of performing comfort calculations by sharing information with other programs. The objective of this research is to evaluate, from different perspectives, the performance of this new simulation tool on three buildings at the University of Alicante. These were chosen as university campuses are responsible for large groups of buildings and belong to the group of stakeholders interested in obtaining efficient and comfortable buildings. These case studies are defined by their extreme adaptation to design recommendations for mild-warm weather. At the end of the process, the difference is measured between simulation and real building performance. The results obtained show that simulation still differs greatly from real building performance from the energy performance point of view, while the comfort evaluation shows results that are closer to the reality of the buildings.

The Impact of Modern Artificial Lighting on the Optimum Window-to-Wall Ratio of Residential Buildings in Jordan

Energy savings gained through natural lighting could be offset by the loss of energy through windows; therefore, the target of this study is to examine the effects of enhancing the efficiency of lighting systems on the optimum window-to-wall ratio (WWR) of Jordanian residential structures. This research proposes the hypothesis that the WWR of residential structures that contain artificial lighting systems with increased efficiency will be lower than buildings in which solar lighting is provided. The energy simulation tool, DesignBuilder (DesignBuilder Software Ltd, Stroud, UK) was used to simulate an intricate model showing a standard Jordanian residential building with a size of 130 m2. The study offers useful guidance regarding the optimum WWR for key decisionmakers when designing energy-efficient residential structures in the context of Jordan. By considering the balance between gains and losses in solar heat and light gain to exploit energy from solar sources with no reverse effects, while making comparisons between different WWR situations, the findings indicate that the typical WWR for residential structures in Jordan that have efficient Light Emitting Diode (LED) systems of lighting installed could be between 25% and 30%, which is lower than the highest WWR stipulated by the ASHRAE standards.

A Life Cycle Cost Analysis of Structural Insulated Panels for Residential Buildings in a Hot and Arid Climate

In hot and humid climatic conditions, cooling tends to dominate building thermal energy use. Cooling loads can be reduced through the adoption of efficient building envelope materials, such as Structural Insulated Panels (SIPs). This study quantifies the life cycle cost and operational energy of a representative case-study house in Bahrain using SIPs and hollow concrete blocks (HCBs) for the envelope over a period of 50 years. Operational energy is calculated using a dynamic energy simulation tool, operational costs are calculated based on the energy demand and local tariff rates, and construction costs are estimated using market prices and quotations. The life cycle cost is quantified using the Net Present Cost technique. Results show that SIPs yield a 20.6% reduction in cooling energy use compared to HCBs. For SIP costs of 12 and 17 USD/m2, the SIP house was cheaper throughout, or had a higher capital cost than the HCB house (breaking even in year 33), respectively. We propose policy recommendations with respect to material pricing, electricity tariffs, and energy efficiency, to improve the operational energy efficiency of houses in Bahrain and similar countries along the Arabian Peninsula.

Modeling, Analysis and Optimization of Grid-Integrated and Islanded Solar PV Systems for the Ethiopian Residential Sector: Considering an Emerging Utility Tariff Plan for 2021 and Beyond

Currently, difficulties such as the depletion of fossil fuel resources and the associated environmental pollution have driven the rise of other energy systems based on green energy sources. In this research, modeling and a viability study of grid-connected and islanded photovoltaic (PV) power systems for supplying the residential load in Mekelle City, Ethiopia, were carried out considering the country’s emerging utility tariff plan for 2021 and beyond. The technical viability of the proposed supply option was analyzed using PVGIS, PVWatts and HOMER Pro tool, while the economic and environmental optimization aspects were carried out using HOMER Pro. Sensitivity analyses and output comparisons among the three renewable energy simulation tools are presented. The results showed that under the consideration of an incremental electricity tariff plan (up to 2021), the analyzed cost of energy of the grid/PV system is around 12% lower than the utility grid tariff. Moreover, we also found that by taking the continuous global solar PV cost reduction into account, the cost of energy of the modeled islanded operation of solar PV power units totally broke the grid tariff in Ethiopia after 2029 based on the tariff for 2021 and well before with the expected escalation of the grid tariff on an annual basis. The technical performance of the system realized through PVGIS and PVWatts was almost comparable to the HOMER Pro outputs. Thus, this investigation will offer a clear direction to the concerned target groups and policy developers in the evolution of PV power supply options throughout the technically viable locations in the country.

Role of BIM and energy simulation tools in designing zero-net energy homes

Purpose The purpose of this study is to design a zero-energy home, which is known to be capable of balancing its own energy production and consumption close to zero. Development of low-energy homes and zero-net energy houses (ZEHs) is vital to move toward energy efficiency and sustainability in the built environment. To achieve zero or low energy targets in homes, it is essential to use the design process that minimizes the need for active mechanical systems. Design/methodology/approach The methodology discussed in this paper consists of an interfacing building information modeling (BIM) tool and a simulation software to determine the potential influence of phase change materials on designing zero-net energy homes. Findings BIM plays a key role in advancing methods for architects and designers to communicate through a common software platform, analyze energy performance through all stages of the design and construction process and make decisions for improving energy efficiency in the built environment. Originality/value This paper reviews the literature relevant to the role of BIM in helping energy simulation for the performance of residential homes to more advanced levels and in modeling the integrated design process of ZEHs.

Conceptualization, development and validation of EMAR: A user-friendly tool for accurate energy simulations of residential buildings via few numerical inputs

The accurate simulation of energy performance is fundamental to design low energy buildings – newly-built or after refurbishment – and thus to promote sustainability. However, this is an involved task requiring high complexity in modeling and simulation. Most accurate building energy simulation tools are not user-friendly for building professionals, thereby hindering a widespread use of effective methodologies – developed by researchers – for the transition to nearly- or net-zero energy buildings. This study tries to solve such an issue by proposing a novel, accurate but user-friendly tool for building modeling and energy simulation. The tool is denoted as EMAR because it is based on the advanced coupling between ENergyplus and MAtlab® addressing Residential buildings, which are a major part of the existing stocks. EMAR completely works under MATLAB® environment and only needs around 60 numerical inputs to generate simplified building models and perform accurate energy simulations. The available outputs are numerous referring to energy and economic performance as well as thermal comfort. No drawings, no schemes of energy systems, no deep modeling expertise are required but only few numbers. EMAR is validated against detailed EnergyPlus models of an ASHRAE test building and of two typical European buildings. The discrepancies are lower than 10% as concerns thermal and primary energy needs, and in most cases are lower than 5%, but the modeling complexity and computational burden are drastically reduced. Thus, EMAR can be a precious tool to perform user-friendly but accurate building energy modeling and simulations.

Predicting energy consumption for residential buildings using ANN through parametric modeling

Controlling buildings energy consumption is a great practical significance. During early design stage, accurate and rapid prediction of energy consumption could provide a quantitative basis for energy-saving designs. Currently, the key problem that are still facing designers is the interoperability between building modeling and energy simulation tools. In addition, design challenges gained recognition due to the complexity of the prevalence of large numbers of independent interrelated variables. Artificial Neural Networks (ANNs) are the most broadly applied artificial intelligence method in buildings’ performance field due to its competence to handle nonlinear variables’ relationships accurately and promptly. This paper presents a methodology based on the ANNs to improve the prediction of energy usage for residential buildings in early design stages. The model is created using a dataset resulted from the calculation of energy consumption by simulating multiple design options with randomly input variables. The proposed methodology can mitigate technical barriers while integrating and automating available commercial tools into a workflow from a parametric model to the simulation of building energy. The developed ANN model is evaluated and validated and used to predict the energy consumption with acceptable accuracy. Finally, a user-friendly interface is designed to facilitate energy consumption prediction without any experience in modeling and simulation tools acting as a decision support tool, which is simple, reliable and easy to use.