Future Building Energy Research Articles

Performance analysis of hybrid power configurations: Impact on primary energy intensity, carbon dioxide emissions, and life cycle costs

Utilization of Natural gas and Hydrogen to support current and future building energy needs to offset the total electric demand while improving the grid resiliency and energy efficiency was investigated. Demand side energy management will play an important role in efficiently managing the available energy resources. Performance assessment of different power generation and energy management configurations is presented in this paper. Development of solutions in addressing grid resiliency by providing the ability to design suitable configurations for meeting individual building energy needs is discussed. Primary movers (PM) such as internal combustion engines (ICE) and fuel cells (FC) along with small scale auxiliary renewable energy source such as photovoltaics (PV) were considered. Key attributes of total carbon foot-print, life cycle costs including capital and operational expenditure, electric grid offset or peak shaving capability, thermal energy availability and its further potential to offset total electric demand, and primary energy intensity are analyzed and discussed in detail.

Generating future weather files under climate change scenarios to support building energy simulation – A machine learning approach

General circulation models (GCM) have been used by researchers to assess the effect of climate change in different fields of study. In the case of building energy performance, GCMs can be used to evaluate future building energy performance through simulations. However, a key issue with the use of GCM data in building energy simulation is the inadequate resolution and bias of the data. Therefore, in order to use this data for simulation purposes and better predict future building performance, further processing is required. The first challenge is that the GCMs are usually biased, which means a considerable deviation can be found when the historical GCM data is compared to station observed weather data. The second challenge is that the GCM data has daily temporal resolution rather than the hourly resolution required in building energy simulation.In order to utilize GCM data to estimate future building performance through simulation, the current study suggests a workflow that can be applied to climate change data. First, a bias-correction technique, known as the quantile-quantile method, is applied to remove the bias in the data in order to adapt GCMs to a specific location. The study then uses a hybrid classification-regression model to downscale the bias-corrected GCM data to generate future weather data at an hourly resolution for building energy simulation. In this case, the hybrid model is structured as a combined model, where a classification model serves as the main model together with an auxiliary regression model for cases when data is beyond the range of observed values. The proposed workflow uses observed weather data to determine similar weather patterns from historical data and use it to generate future weather data, contrary to previous studies, which use artificially generated data. However, in cases where the future GCM data showed temperatures ranging outside of the observed data, the study applied a trained regression model to generate hourly weather data.The proposed workflow enables users to generate future weather files year by year under different climate change scenarios and, consequently, extreme weather characteristics are preserved for extreme or reliability analysis and design optimization.

Analysis of Energy Saving Effect and Cost Efficiency of ECMs to Upgrade the Building Energy Code

Building energy codes are key policy tools for improving building energy efficiency by defining the minimum requirement for the energy performance of new buildings. In Korea, the building energy code was focused on prescriptive criteria for a long time but is now gradually introducing performance criteria. However, switching to performance criteria is not straightforward because of the resistance of the market to abandoning the well-consolidated prescriptive criteria. The objective of this study is to derive appropriate measures to strengthen the prescriptive criteria and the performance criteria, considering both the energy-saving effect and the economic efficiency for new office buildings and educational buildings to increase the market acceptance of the building energy codes. To this end, the energy-saving effects of reference buildings resulting from the reinforcement of the prescriptive criteria in the past have been first analyzed. Then, based on the collected energy performance parameters and cost data, the economic efficiency relative to the energy saving deriving from the application of passive and active energy conservation measures (ECMs) were analyzed, and future building energy code’s reinforcement measures were derived.

Propagating downscaled future weather file uncertainties into building energy use

In the United States, buildings consume a significant portion of total energy use and contribute to carbon emissions. Therefore, evaluating their performance for sustainable design is critical. While building standards are updated frequently to improve building efficiency, the climate is changing and classical (deterministic) approaches to quantifying building energy use may not be accurate. Significant uncertainties exist in current models of future building performance. Probabilistic approaches to evaluate whole building performance and account for climate uncertainties require large input data of climate projections with detailed spatial and temporal resolution and considerable computational resources. This paper offers a four-step process to propagate climate uncertainties from future climate projections into building energy performance. The climate uncertainty propagation method consists of a combination of regression, distribution fitting and random sampling. It has the potential to capture climate uncertainties in building simulation tools and is applicable to cases where limited future weather files can be produced. In addition, we include the use of updated design day files in future building performance analysis. Design days drive equipment modernization impacting whole building energy use under climate change. The Department of Energy office reference buildings for Philadelphia climate conditions are used as the case study. The aim of this research is to generate a probabilistic model that captures future building energy performance trends where can be applicable to regions that have limited future hourly weather files to be incorporated into building simulation tools. The results of this paper are intended to promote a probabilistic approach to account for climate uncertainties and to provide guidance in analyzing buildings projected energy consumption under climate change.

WRF‐TEB: Implementation and Evaluation of the Coupled Weather Research and Forecasting (WRF) and Town Energy Balance (TEB) Model

AbstractUrban land surface processes need to be represented to inform future urban climate and building energy projections. Here, the single layer urban canopy model Town Energy Balance (TEB) is coupled to the Weather Research and Forecasting (WRF) model to create WRF‐TEB. The coupling method is described generically, implemented into software, and the code and data are released with a Singularity image to address issues of scientific reproducibility. The coupling is implemented modularly and verified by an integration test. Results show no detectable errors in the coupling. Separately, a meteorological evaluation is undertaken using observations from Toulouse, France. The latter evaluation, during an urban canopy layer heat island episode, shows reasonable ability to estimate turbulent heat flux densities and other meteorological quantities. We conclude that new model couplings should make use of integration tests as meteorological evaluations by themselves are insufficient, given that errors are difficult to attribute because of the interplay between observational errors and multiple parameterization schemes (e.g., radiation, microphysics, and boundary layer).

Modeling County-Level Energy Demands for Commercial Buildings Due to Climate Variability with Prototype Building Simulations

The building sector accounts for nearly 40% of total primary energy consumption in the U.S. and E.U. and 20% of worldwide delivered energy consumption. Climate projections predict an increase of average annual temperatures between 1.1–5.4 °C by 2100. As urbanization is expected to continue increasing at a rapid pace, the energy consumption of buildings is likely to play a pivotal role in the overall energy budget. In this study, we used EnergyPlus building energy models to estimate the future energy demands of commercial buildings in Salt Lake County, Utah, USA, using locally-derived climate projections. We found significant variability in the energy demand profiles when simulating the study buildings under different climate scenarios, based on the energy standard the building was designed to meet, with reductions ranging from 10% to 60% in natural gas consumption for heating and increases ranging from 10% to 30% in electricity consumption for cooling. A case study, using projected 2040 building stock, showed a weighted average decrease in heating energy of 25% and an increase of 15% in cooling energy. We also found that building standards between ASHRAE 90.1-2004 and 90.1-2016 play a comparatively smaller role than variation in climate scenarios on the energy demand variability within building types. Our findings underscore the large range of potential future building energy consumption which depends on climatic conditions, as well as building types and standards.

An international review of occupant-related aspects of building energy codes and standards

In light of recent research, it is evident that occupants are playing an increasingly important role in building energy performance. Despite the important role of building energy codes and standards in design, the occupant-related aspects are typically simple and have not kept up with the leading research. This paper reviews 23 regions’ building energy codes and standards by first comparing their quantitative aspects and then analyzing their mandated rules and approaches. While the present paper focuses on offices, general recommendations are applicable to other building types as well. The review revealed a wide range of occupant-related values, approaches, and attitudes. For example, code-specified occupant density varies by nearly a factor of three between different codes. This underlines the need for development of advancement in occupant behavior modeling approaches for future occupant-centric building performance codes and standards. Moreover, occupants are often referred to only implicitly; underlying expectations about energy-saving occupant behavior from building occupants varies greatly; and, only a few codes address occupant feedback and system usability. Based on the findings of the review, a set of initial recommendations for future building energy codes is proposed.

Application of the open-source cloud platform FIWARE for future building energy management systems

The integration of volatile, decentralized energy into building energy systems requires intelligent algorithms that are capable of controlling the augmented complexity and the interactions of subsystems efficiently. Not only do the algorithms require computing capacity, but also a feasible information and communication infrastructure. One possibility to provide computing power on demand is cloud computing that forms together with an extended connectivity of physical devices the Internet of Things (IoT). We present a customizable open-source IoT platform setup using FIWARE. For the integration of existing building automation networks, we extend an OpenMUC gateway with BACnet functionality and connect it to our platform via MQTT. We successfully demonstrate the overall setup with a web-based visualization of high resolution energy monitoring and a control loop as basic requirements for the integration of more advanced control algorithms in future building energy management systems. We find our setup generally feasible for both use cases and, hence, will aim for the integration of more advanced control algorithms in the future using the presented platform components.

Hourly weather data projection due to climate change for impact assessment on building and infrastructure

The global climate change research has been conducted for a few years in various professional communities. In the building industry, researchers usually investigate the future building energy demands due to the climate change by simulation software. The input files to the simulation software includes projected weather data and building models. Although there exist a few mathematical methods to project the future weather, the morphing method is the most well-known among them. In the meantime, the simulation software and weather data are in a variety of formats depending on country of origin and/or simulation package. In order to provide both the research and the professional communities the possibility to undertake climate change impact assessments on buildings, coastal engineering and construction, land use and other related areas, this study develops the web-based application Weather Morph: Climate Change Weather File Generator accessible to generate the future weather data for more than 2100 locations throughout the world for all four IPCC (Intergovernmental Panel of Climate Change) emission scenarios in the three future time slices of the 2020s, 2050s and 2080s. The output of the application is projected future weather datasets in formats TMY2 and EPW for general use.

Economic model predictive control for demand flexibility of a residential building

Future building energy management systems will have to be capable of adapting to variation in the rate of production of energy from renewable sources. Controllers employing a model predictive control (MPC) framework can optimise and schedule energy usage based on the availability of renewably generated energy. In this paper, an MPC using artificial neural networks (ANNs) was implemented in a residential building. The ANN-MPC was successfully tested and demonstrated good performance predicting the building's energy consumption. The controller was then modified to function as an economic MPC (EMPC) to optimise demand flexibility (i.e., the ability to adapt energy demands to fluctuations in supply). The operational costs of energy usage were associated with this demand flexibility, which was represented by three flexibility indicators: flexibility factor, supply cover factor, and load cover factor. The results from a day-long test showed that these flexibility indicators were maximised (flexibility factor ranged from −0.88 to 0.67, supply cover factor from 0.04 to 0.13, and load cover factor from 0.07 to 0.16) when the EMPC controller's demand flexibility was compared to that of a conventional proportional-integral (PI) controller. The EMPC framework for demand flexibility can be used to regulate on-site energy generation, grid consumption, and grid feed-in and can thus serve as a basis for overall optimisation of the operation of heating systems to achieve greater demand flexibility.

Numerical investigations on the thermal performance of adaptive ETFE foil cushions

It has become increasingly popular among designers and engineers to use ETFE pneumatic foil constructions as an alternative to glass in state of the art building envelopes, due to the low weight, high transparency, mechanical resistance and self-cleaning features of ETFE. However, it is still unclear how the thermal performance of the ETFE cushions can be assessed comprehensively and what are the effects on building thermal performance. This study investigated the thermal performance of a developed prototype ETFE cushion through both experimental measurement and numerical simulation. The numerical model has been validated by results from experimental tests using a large climatic chamber. The thermal resistance of the developed ETFE cushion at various environmental conditions (different indoor and outdoor temperature conditions) and also different inclination angles were studied. Empirical correlations generated based on simulations could be used for future building energy simulation.

Identifying suitable general circulation model for future building cooling energy analysis

These future building energy studies mainly stem from hourly based dynamic building simulation results with the future weather data. The reliability of the future building energy forecast heavily relies on the accuracy of these future weather data. The global circulation models (GCMs) provided by IPCC are the major sources for constructing future weather data. However, there are uncertainties existed among them even with the same climate change scenarios. There is a need to develop a method on how to select the suitable GCM for local application. This research firstly adopted principal component analysis (PCA) method in choosing the suitable GCM for application in Taiwan, and secondly the Taiwanese hourly future meteorological data sets were constructed based on the selected GCM by morphing method. Thirdly, the future cooling energy consumption of an actual office building in the near (2011-2040), the mid (2041-2070), and the far future (2071-2100), were analysed. The results show that NorESM1-M GCM has the lowest root mean square error (RMSE) as opposed to the other GCMs, and was identified as the suitable GCM for further future climate generation processing. The building simulation against the future weather datasets revealed that the average cooling energy use intensity (EUIc) in Taipei will be increased by 12%, 17%, and 34% in the 2020s, 2050s, and 2080s, respectively, as compared to the current climate.

Developing a framework for urban building life cycle energy map with a focus on rapid visual inspection and image processing

The United Nations has predicted a fast pace of worldwide migration into urban areas by the year 2030 that may result in arbitrary growth of cities, leading to adverse environmental impacts and unplanned energy use. As building sector consumes more energy compared to other sectors, understanding urban building energy use is critical to aid in energy reduction goals. This research introduces visual inspection and image processing as methods for compiling building inventory needed for future building energy modelling efforts. A preliminary urban building life cycle energy map for a case study of commercial buildings that contains embodied energy and operating energy as different layers is presented. The map will provide policy makers with insights on different temporal stages of energy consumption in buildings at the urban scale.

Research on Application of Solar Semiconductor Cold Wall in Near-Zero Energy Buildings

With the speeding up of urbanization and growth of national economy, housing industry has developed rapidly in recent years, the residential building energy saving problem has been increasingly outstanding, according to the study, the future China building energy consumption ratio will rise to about 35%. At present, building energy conservation has become one of the recognized effective ways to solve the increasingly serious energy problems. Through the study of the near zero energy building in the hot summer and cold winter area，the thermal comfort of the near zero energy building was satisfied by using the semiconductor cold and hot wall system，the practical effect and operation parameters of the semiconductor cooling wall were given. It was presents a series of Suggestions and improvement Suggestions for the research and energy conservation work of near zero energy buildings in hot summer and cold winter area.

A Review of Internet of Energy Based Building Energy Management Systems: Issues and Recommendations

A building energy management system (BEMS) is a sophisticated method used for monitoring and controlling a building’s energy requirements. A number of potential studies were conducted in nearly or net zero energy buildings (nZEBs) for the optimization of building energy consumption through efficient and sustainable ways. Moreover, policy makers are approving measures to improve building energy efficiency in order to foster sustainable energy usages. However, the intelligence of existing BEMSs or nZEBs is inadequate, because of the static set points for heating, cooling, and lighting, the complexity of large amounts of BEMS data, data loss, and network problems. To solve these issues, a BEMS or nZEB solution based on the Internet of energy (IoE) provides disruptive opportunities for revolutionizing sustainable building energy management. This paper presents a critical review of the potential of an IoE-based BEMS for enhancing the performance of future generation building energy utilization. The detailed studies of the IoE architecture, typical nZEB configuration, different generations of nZEB, and smart building energy systems for future BEMS are investigated. The operations, advantages, and limitations of the existing BEMSs or nZEBs are illustrated. A comprehensive review of the different types of IoE-based BEMS technologies, such as energy routers, storage systems and materials, renewable sources, and plug-and-play interfaces, is then presented. The rigorous review indicates that existing BEMSs require advanced controllers integrated with IoE-based technologies for sustainable building energy usage. The main objective of this review is to highlight several issues and challenges of the conventional controllers and IoE applications of BEMSs or nZEBs. Accordingly, the review provides several suggestions for the research and development of the advanced optimized controller and IoE of future BEMSs. All the highlighted insights and recommendations of this review will hopefully lead to increasing efforts toward the development of the future BEMS applications.

A metamodel for building energy performance

There is a growing use of metamodels to evaluate building energy performance. However, current metamodeling approaches lack a common foundation. The aim of this work was to develop a general metamodel for building energy performance. Based on the premise that each building element can have an impact on energy performance, our general metamodel assumes that the overall impact is a polynomial function of the individual impacts. The model includes time-dependent parameters such as the solar heat gain coefficient and energy system efficiency. A model derived from the general model was tested on an office located in Paris. Energy needs and consumption, the cost of energy consumption and the CO2 emissions for heating and cooling were analyzed. The study highlighted the significant impact of the choice of the performance criteria in evaluating the efficiency of building design solutions. The metamodel represents a fast way to perform calculations with an accuracy close to that of dynamic simulations. It can be used as a basis to perform parametric studies and for future building energy regulations.

Robust multi-objective control of hybrid renewable microgeneration systems with energy storage

Microgeneration technologies are positioned to address future building energy efficiency requirements and facilitate the integration of renewables into buildings to ensure a sustainable, energy-secure future. This paper explores the development of a robust multi-input multi-output (MIMO) controller applicable to the control of hybrid renewable microgeneration systems with the objective of minimising the electrical grid utilisation of a building while fulfilling the thermal demands. The controller employs the inverse dynamics of the building, servicing systems, and energy storage with a robust control methodology. These inverse dynamics provides the control system with knowledge of the complex cause and effect relationships between the system, the controlled inputs, and the external disturbances, while an outer-loop control ensures robust, stable control in the presence of modelling deficiencies/uncertainty and unknown disturbances. Variable structure control compensates for the physical limitations of the systems whereby the control strategy employed switches depending on the current utilisation and availability of the energy supplies. Preliminary results presented for a system consisting of a micro-CHP unit, solar PV, and battery storage indicate that the control strategy is effective in minimising the interaction with the local electrical network and maximising the utilisation of the available renewable energy.

Impact of climate change on U.S. building energy demand: sensitivity to spatiotemporal scales, balance point temperature, and population distribution

Past assessments of climate change impacts on building energy consumption have typically neglected spatial variations in the “balance point” temperature, population distribution effects, and the extremes at smaller spatiotemporal scales where the impacts of climate change are most pronounced. Here we test the impact of these limitations through a sensitivity analysis in the Contiguous United States. Though national/annual total source energy consumption differences between the 2080–99 time period and the present are less than 2 %, we find changes at the state/month scale that are much larger with summer electricity demand increases exceeding 50 % and spring non-electric energy declines of 48 % by the end of the century. The use of a fixed 18.3 °C (65 °F) balance point temperature, versus a more representative state-specific value, leads to an overestimate of the energy consumption changes in most states with a maximum change in the state of Oregon of almost 14 percentage points. Finally, projected population redistribution, when combined with the spatial pattern of climate change, exacerbates the building energy consumption impacts, further increasing source energy consumption in some states (max = +5.3 percentage points) and further diminishing energy consumption declines in others (max = −8.2 percentage points). When integrated over the U.S., the intersection of projected population distribution changes and climate change shifts future building energy consumption from a net decrease to a net increase.

Experimental measurement and numerical simulation of the thermal performance of a double glazing system with an interstitial Venetian blind

Venetian blinds, which were originally designed to provide sun shading and privacy, also have the potential to reduce heat transfer caused by internal and external temperature difference when integrated within the cavity between the two panes of a double glazing unit. In this paper, the thermal performance of a glazing system with and without a Venetian blind with various slat orientation angles under different temperature conditions is investigated through both experiment (undertaken in a large climate chamber) and numerical simulation (obtained via Computational Fluid Dynamic modelling). The thermal resistance of a Venetian blind glazing system varies with the change of slat inclination angle, and it also highly depends on the mean temperature of the glazing and the temperature difference between the indoor and outdoor environment. Inclusion of a Venetian blind modifies both the absolute and relative strengths of convection and radiation. Vertically oriented slats showed the most significant contribution to increasing radiative thermal resistance, which led to the best overall thermal performance. The system achieved up to 28% improvement of U-value when compared with a glazing unit without a Venetian blind. Empirical correlations generated based on simulations could be used for future building energy simulation.

An alternative method to predict future weather data for building energy demand simulation under global climate change

Climate change would affect the building energy demand in the future. Building simulation is a feasible way to quantitatively evaluate this impact. Since the detailed weather is dispensable for the building simulation, it is important to predict the weather data for the future. Given that the uncertainties and limitations of GCMs on regional-scale weather prediction, an alternative method of future weather data generation for future building energy demand simulation is proposed in this paper. Based on the long-/short-term climate periodicity analysis, a Dual-Periodic Time Series Model is established to predict the future monthly temperatures in Shanghai. From the fitting results and the preliminary assessment analysis, it is observed that the alternative forecasting method and the corresponding Dual-Periodic TSM has better capability of characterizing and predicting performance for both recent and future temperature trends in Shanghai than GCM under RCP4.5. In this case, this method can be used as an alternative and supplementary way to the widely used GCM.With consideration of three composite uncertainty scenarios, we convert the predicted monthly temperatures into hourly TMYs by using Morphing method. Using the future TMYs as the weather input of prototypical building models of Shanghai, we can see that the simulated building energy demand presents fluctuant trends in the future periods, different from the continuous uptrends of that using IPCC RCP4.5.