• Energy Research

Clay brick is a commonly used building material in China. Due to the enormous land destruction and excessive consumption of resources, such as materials and energy in the manufacture of clay brick, it is important to study its overall sustainability, i.e., in terms of impact on the environment, services, and economy. In this study, emergy analysis is employed, which offers a holistic perspective, unlike typical environmental studies. A series of emergy indices such as renewability rate (R%), nonrenewability rate (N%), unit emergy values (UEVs), emergy yield ratio (EYR), environmental loading ratio (ELR), and emergy sustainability index (ESI) were used to study manufacturing of clay brick. In addition to calculating UEVs of clay brick manufacturing in China (7.18 × 1012 sej/kg), our detailed analysis shows that the nonrenewable resources and imported energy have a dominant impact on the emergy contribution (50.6%) and within the nonrenewable resources, clay is the foremost item, accounting for 33.5% of local emergy inputs. Given different electricity UEVs, the change ranges of clay brick system UEVs are 14.9% (scenario 1), 7.24% (scenario 2), 8.91% (scenario 3), and 6.94% (scenario 4). Furthermore, several policy suggestions are discussed for improving the sustainability of the evaluated system, involving the energy structure adjustment, recycling material replacement, and promotion of energy-saving systems.

Building energy simulation programs are used for optimal sizing of building systems to reduce excessive energy wastage. Such programs employ thermo-dynamic algorithms to estimate every aspect of the target building with a certain level of accuracy. Currently, almost all building simulation tools capture static features of a building including the envelope, geometry, and Heating, Ventilation, and Air Conditioning (HVAC) systems, etc. However, building performance also relies on dynamic features such as occupants’ interactions with the building. Such interactions have not been fully implemented in building energy simulation tools, which potentially influences the comprehensiveness and accuracy of estimations. This paper discusses an information exchange mechanism via coupling of EnergyPlus™, a building energy simulation engine and PMFServ, an occupant behavior modeling tool, to alleviate this issue. The simulation process is conducted in Building Controls Virtual Testbed (BCVTB), a virtual simulation coupling tool that connects the two separate simulation engines on a time-step basis. This approach adds a critical dimension to the traditional building energy simulation programs to seamlessly integrate occupants’ interactions with building components to improve the modeling capability, thereby improving building performance evaluation. The results analysis of this paper reveals a need to consider metrics that measure different types of comfort for building occupants.

In recent years, a new generation of power grid system, referred to as the Smart Grid, with an aim of managing electricity demand in a sustainable, reliable, and economical manner has emerged. With greater knowledge of operational characteristics of individual appliances, necessary automation control strategies can be developed in the Smart Grid to operate appliances in an efficient manner. This paper provides a way of classifying different operational cycles of a household appliance by introducing an unsupervised learning algorithm called k-means clustering. An intrinsic method known as silhouette coefficient was used to measure the classification quality. An identification process is also discussed in this paper to help users identify the operation mode each types of operation cycle stands for. A case study using a typical household refrigerator is presented to validate the proposed method. Results show that the proposed the classification and identification method can partition and identify different operation cycles adequately. Classification of operation cycles for such appliances is beneficial for Smart Grid as it provides a clear and convincing understanding of the operation modes for effective power management.

Abstract Implementing machine-learning models in real applications is crucial to achieving intelligent building control and high energy efficiency. Over the past few decades, numerous studies have attempted to explore the application of machine-learning models to building energy efficiency. However, these studies have focused on analyzing the technical feasibility and superiority of machine learning algorithms for fitting building energy-related data and have not considered methods of implementing machine learning technology in building energy efficiency applications. Therefore, this review aims to summarize the current practical issues involved in applying machine-learning models to building energy efficiency by systematically analyzing existing research findings and limitations. The paper first reviews the application status of machine learning-based building energy efficiency research by analyzing the model implementation process and summarizing the main uses of the technology in the overall building energy management life cycle. The paper then elaborates on the causes of, influences on, and potential solutions for practical issues found in the implementation and promotion of machine learning-based building energy efficiency measures. Finally, this paper discusses valuable future machine learning-based building energy efficiency research directions with regard to technology opportunity discovery, data governance, feature engineering, generalizability test, technology diffusion, and knowledge sharing. This paper will provide building researchers and practitioners with a better understanding of the current application statuses of and potential research directions for machine learning models in building energy efficiency.

Abstract This research presents a new methodology of environmental building design with an integrated energy–e m ergy (spelled with an “ m ”) approach to study building form optimization in the schematic phases. In architecture, selection of sustainability assessment methods critically affects design goals, favoring or restricting choices designers can make. A building subsumes matter and energy to support human lives, but current building performance indicators are still hard to equate technical sides and human dominant sides of various scales in a synthetic metric. Moreover, in order to achieve global sustainability in a building, as a part of the whole built environment, it is necessary to integrate energy and environmental impacts at the highest scope of analysis. Emergy analysis coupled with building energy simulation can be suggested as a holistic indicator for architectural design process. To test the proposed method, a pilot study with a mid-size office building evaluates the consequences of early design decisions such as basic geometry, aspect ratio, window-wall ratio, construction types, etc. The integrated energy–emergy approach to building form optimization consists of three modules namely, Building Energy Simulation (BES) module, Building EMergy Analysis (BEMA) module, and (iii) MetaModel Development (MMD) module. The BES module uses the EnergyPlus tool for whole building energy analysis, while the BEMA module employs analytical methods to estimate emergy quantities, and the MMD module employs the Taguchi method to develop a metamodel for faster and easier whole building emergy simulation. The metamodel developed using Taguchi-ANOVA method for building form optimization was validated with analytical test results to accelerate environmental design decision-making. This study demonstrates possibility of wider applications of emergy synthesis to building energy research and facilitates practical use of emergy simulation in the environmental design process.

Deteriorating levels of indoor air quality is a prominent environmental issue that results in long-lasting harmful effects on human health and wellbeing. A concurrent multi-parameter monitoring approach accounting for most crucial indoor pollutants is critical and essential. The challenges faced by existing conventional equipment in measuring multiple real-time pollutant concentrations include high cost, limited deployability, and detectability of only select pollutants. The aim of this paper is to present a comprehensive indoor air quality monitoring system using a low-cost Raspberry Pi-based air quality sensor module. The custom-built system measures 10 indoor environmental conditions including pollutants: temperature, relative humidity, Particulate Matter (PM)2.5, PM10, Nitrogen dioxide (NO2), Sulfur dioxide (SO2), Carbon monoxide (CO), Ozone (O3), Carbon dioxide (CO2), and Total Volatile Organic Compounds (TVOCs). A residential unit and an educational office building was selected and monitored over a span of seven days. The recorded mean PM2.5, and PM10 concentrations were significantly higher in the residential unit compared to the office building. The mean NO2, SO2, and TVOC concentrations were comparatively similar for both locations. Spearman rank-order analysis displayed a strong correlation between particulate matter and SO2 for both residential unit and the office building while the latter depicted strong temperature and humidity correlation with O3, SO2, PM2.5, and PM10 when compared to the former.

Abstract Traditionally, building rating systems focused on, among others, energy used during operational stage. Recently, there is a strong push by these rating systems to include the life cycle energy use of buildings, particularly using Life Cycle Assessment (LCA), by offering credits that can be used to achieve higher certification levels. As LCA-based tools are evolving to meet this growing demand, it is important to include methods that also quantify the impact of energy being used by ecosystems that indirectly contribute to building life cycle energy use. Using a case-study building, this paper provides an up-to-date comparison of energy-based indicators in tools for building assessment, including those that report both conventional life cycle energy and those that also include a wider systems boundary that captures energy use even further upstream. This paper applies two existing LCA tools, namely, an economic input–output based model, Economic Input–Output LCA, and a process-based model, ATHENA® Impact Estimator, to estimate life cycle energy use in an example building. In order to extend the assessment to address energy use further upstream, this paper also tests the Ecologically based LCA tool and an application of the emergy methodology. All of these tools are applied to the full service life of the building, i.e., all stages, namely, raw material formation, product, construction, use, and end-of-life; and their results are compared. Besides contrasting the use of energy-based indicators in building life cycle tools, this paper uncovered major challenges that confront stakeholders in evaluating the built environments using LCA and similar approaches.