Building energy use prediction plays an important role in building energy management and conservation as it can help us to evaluate building energy efficiency, conduct building commissioning, and ...detect and diagnose building system faults. Building energy prediction can be broadly classified into engineering, Artificial Intelligence (AI) based, and hybrid approaches. While engineering and hybrid approaches use thermodynamic equations to estimate energy use, the AI-based approach uses historical data to predict future energy use under constraints. Owing to the ease of use and adaptability to seek optimal solutions in a rapid manner, the AI-based approach has gained popularity in recent years. For this reason and to discuss recent developments in the AI-based approaches for building energy use prediction, this paper conducts an in-depth review of single AI-based methods such as multiple linear regression, artificial neural networks, and support vector regression, and ensemble prediction method that, by combining multiple single AI-based prediction models improves the prediction accuracy manifold. This paper elaborates the principles, applications, advantages and limitations of these AI-based prediction methods and concludes with a discussion on the future directions of the research on AI-based methods for building energy use prediction.
Accurate building energy prediction plays an important role in improving the energy efficiency of buildings. This paper proposes a homogeneous ensemble approach, i.e., use of Random Forest (RF), for ...hourly building energy prediction. The approach was adopted to predict the hourly electricity usage of two educational buildings in North Central Florida. The RF models trained with different parameter settings were compared to investigate the impact of parameter setting on the prediction performance of the model. The results indicated that RF was not very sensitive to the number of variables (mtry) and using empirical mtry is preferable because it saves time and is more accurate. RF was compared with regression tree (RT) and Support Vector Regression (SVR) to validate the superiority of RF in building energy prediction. The prediction performances of RF measured by performance index (PI) were 14–25% and 5–5.5% better than RT and SVR, respectively, indicating that RF was the best prediction model in the comparison.
Moreover, an analysis based on the variable importance of RF was performed to identify the most influential features during different semesters. The results showed that the most influential features vary depending on the semester, indicating the existence of different operational conditions for the tested buildings. A further comparison between RF trained with yearly and monthly data indicated that the energy usage prediction for educational buildings could be improved by taking into consideration their energy behavior changes during different semesters.
•An ensemble bagging tree model (EBT) was used to predict institutional building electricity demand.•Three prediction modules representing different semesters of the test building were developed.•The ...proposed ensemble bagging tree was proven to be effective for short-term building energy prediction.•The proposed variable selection method reduces the computation time of EBT without sacrificing its prediction accuracy.
Broadly speaking, building energy use prediction can be classified into two categories based on modeling approaches namely engineering and Artificial Intelligence (AI). While engineering approach requires solving physical equations representing the thermal performance of systems and components that constitute the buildings, the AI-based approach uses historical data to predict future performance. Although engineering approach estimates energy use with greater accuracy, it falls short in the overall complexity of model building and simulation in which detailed data that represent the building geometry, systems, configurations, and occupant schedule is needed. Whereas, the AI-based approach offers a rapid prediction of building energy use and, if appropriately trained and tested, may be used for quick and efficient decision-making of energy use reduction. Nevertheless, for robust integration with and to improve automated building systems management and intelligence, the need for consistent, stable, and higher prediction accuracy cannot be understated. To alleviate the instability issue, and to improve prediction accuracy, we have exploited and tested an ensemble learning technique, ‘Ensemble Bagging Trees’ (EBT), using data obtained from meteorological systems and building-level occupancy and meters.Results showed that the proposed EBT model predicted hourly electricity demand of the test building with improved accuracy of Mean Absolute Prediction Error that ranged from 2.97% to 4.63%. Additionally, results showed that proposed variable selection method could reduce the computation time of EBT by 38–41% without sacrificing the prediction accuracy. The proposed ensemble learning model that exemplifies improved prediction accuracy over other AI techniques can be used for real-time applications such as system fault detection and diagnosis.
The 21st century is witnessing a fast-paced digital revolution. A significant trend is that cyber and physical environments are being unprecedentedly entangled with the emergence of Internet of ...Things (IoT). IoT has been widely immersed into various domains in the industry. Among those areas where IoT would make significant impacts are building construction, operation, and management by facilitating high-class services, providing efficient functionalities, and moving towards sustainable development goals. So far, IoT itself has entered an ambiguous phase for industrial utilization, and there are limited number of studies focusing on the application of IoT in the building industry. Given the promising future impact of IoT technologies on buildings, and the increasing interests in interdisciplinary research among academics, this paper investigates the state-of-the-art projects and adoptions of IoT for the development of smart buildings within both academia and industry contexts. The wide-ranging IoT concepts are provided, covering the necessary breadth as well as relevant topic depth that directly relates to smart buildings. Current enabling technologies of IoT, especially those applied to buildings and related areas are summarized, which encompasses three different layers based on the conventional IoT architecture. Afterwards, several recent applications of IoT technologies on buildings towards the critical goals of smart buildings are selected and presented. Finally, the priorities and challenges of successful and seamless IoT integration for smart buildings are discussed. Besides, this paper discusses the future research questions to advance the implementation of IoT technologies in both building construction and operation phases. The paper argues that a mature adoption of IoT technologies in the building industry is not yet realized and, therefore, calls for more attention from researchers in the relevant fields from the application perspective.
•The common technologies of Internet of Things (IoT) used in the building industry on a layering basis are summarized.•The potentials of IoT technology application towards the development of smart buildings are recognized and highlighted.•An outline for developing IoT architecture to implement critical functionalities of smart buildings is provided.•Current trends and priorities, and future research areas of IoT application in the building industry are presented.
Energy consumption and indoor environment of buildings are proved to be largely influenced by the presence and behaviors of occupants. The uncertainty caused by occupant behaviors accounts for a ...significant discrepancy between the predicted and actual energy usage. In a real world, building system operations and control will be directly affected by occupant behavior, which may lead to over thirty percent waste against building's designed performance. Therefore, the capability to seamlessly integrate occupant behavior in energy simulation tools and building management systems in the future is clearly important to optimize building energy use while maintaining the same level of services. However, research has not reached the phase that occupant behaviors could be effectively modeled. Thus, the traditional schedule based approach is not adequate to satisfy the needs of building efficiency. In this paper, a thorough survey of occupant behavior modeling and simulation state-of-the-art technologies and methodologies for building energy efficiency is conducted. The paper first identifies and discusses the significance and application scale of building occupant behavior model. Based on the information collected, some recent data acquisition technologies for behavior-related research and occupant behavior modeling approaches are summarized. The advantages and limitations of these modeling methods are compared and analyzed, as well as appropriate recommendations are made for the future research. The paper finally outlines the findings and potential development areas in the field of occupant behavior modeling for energy efficient buildings.
With over 70% of the world population projected to live in urban areas by 2030, the role of cities in sustainable development is gaining greater momentum. Creating healthy and livable communities ...have become a priority in many regions, giving birth to several neighborhood sustainability assessment tools. Yet, these tools largely fail to consider and integrate the four pillars of sustainability namely, environmental, social, economic, and institutional dimensions in a balanced, equitable manner. Without a detailed analysis of the most recent versions of widely used NSA tools, the impact of these tools toward sustainability may be inaccurately measured and reported. Besides, it is crucial to understand the various credits implemented and/or ignored by stakeholders using such tools. With a balanced approach in mind, this paper examines five NSA tools and addresses four objectives namely, (1) to fill the gap in current literature by using the most up-to-date versions of NSA tools in the analysis; (2) to examine the current rating systems’ ability to define the goals of sustainability and to measure their progress; (3) to identify which sustainability criteria are applied most frequently by stakeholders and which ones are ignored; and (4) to offer timely and imminent issues relevant to current NSA tools. The first three objectives listed above are dealt with using actual projects implemented, i.e., data from 115 projects, one of the largest dataset used in any study at this time. Using the results from the analysis, this paper concludes with a series of recommendations for a balanced approach to NSA.
The threats posed by climate change to Earth's ecosystems, human health, and the global economy are self-evident, and the building sector has contributed significantly to the creation of this ...problem. For two decades, the construction industry has attempted to mitigate its environmental impact by adopting green building strategies. However, due to a lack of a secure and accurate measurement, reporting, and verification (MRV) system, lowering the industry's carbon footprint has not been embraced by building owners. The building industry has not been able to participate in the carbon credit markets as well. Several factors are contributing to this failure. The primary issue is the complicated and insecure accounting system for accurately tracking energy consumption and carbon emissions. Although there are several building energy performance (BEP) audit schemes, these programs do not provide a suitable structure for a secure and accurate carbon emission MRV. Some climate action groups have navigated an overview of Blockchain for climate actions, especially in registries and tracking solutions, digital MRV, decentralized environment of clean energies, and climate finance. Moreover, there are several attempts to adopt blockchain MRV systems in the climate action projects, and blockchain carbon credit markets were already in place since Blockchain technology can offer a transparent, reliable, and affordable MRV system. This research walked through the literature of the BEP audit programs, the carbon credit market for the building sector, the possibility of adopting Blockchain technology to a digital BEP MRV. The study found that the digital MRV system, which climate action projects require, can be applied to the building sector with the adoption of Blockchain technology. Next, because a few blockchain carbon credit markets are already running, a blockchain digital MRV system needs to be developed to help the building sector participate in the carbon credit markets.
•A novel agent-based model (ABM) was developed to explore occupant behaviors in the context of built environment.•Relevant data was collected using customized sensor nodes and paper-based survey in a ...test bed building.•A validation study was conducted to test the occupant behavior model with the methods of visualization and calculation of quantification metrics for performance evaluation.•The proposed research framework has the potential to improve both building energy efficiency and occupant comfort during the life cycle of a building.
Occupant behaviors are one of the dominant factors that influence building energy use. Traditional building energy modeling programs use typical occupant schedules that often do not reflect actual situations. Robust occupant behavior modeling that seamlessly integrates with building energy models will not only improve simulation performance, but also provide a deeper understanding of occupant behaviors in buildings. This paper presents a development and validation approach to a novel occupant behavior model in commercial buildings. A robust agent-based modeling (ABM) tool, namely Performance Moderator Functions server (PMFserv), is used as the basis of the occupant behavior model. The ABM considers various occupant perceptions and interactions with window, door, and window-blinds based on the environmental conditions. An elaborate agent-based model that represents an office space in an existing building is developed. This is followed by a validation study of the ABM through the use of embedded sensors that capture the indoor ambient conditions and a survey to record actual occupant behaviors. By comparing the recorded behavior data with ABM output, this paper discusses the proposed ABM's prediction ability, limitations, and extensibility. Finally, the paper concludes with the potential of integrating the occupant behavior model with building energy simulation programs.
The Chinese cement industry produced 2150 million metric tons of cement in 2014, accounting for 58.1% of the world’s total. This industry has a hugely destructive effect on the environment owing to ...its pollution. The environmental impact of cement manufacturing is a major concern for China. Although researchers have attempted to estimate impacts using life cycle assessment approaches, it lacks the ability to provide a holistic evaluation of the impacts on the environment. Emergy analysis, through ecological accounting, offers environmental decision making using elaborate book keeping. In spite of the high environmental impact of the cement industry, there has only been a handful of research work done to compute the unit emergy values (UEVs) of cement manufacturing in China. A thorough study of existing UEVs of cement manufacturing in China showed pitfalls that may lead to inaccurate estimations if used in emergy analysis. There is a strong need for a new, updated UEV for cement manufacturing in China, particularly reflecting both the dry and wet raw materials in the manufacturing process. This paper develops a methodology to calculate the nonrenewable resources used in cement manufacturing, particularly using mainstream cement production line. Our systematic approach-based UEV estimates of cement manufacturing in China using the quota method are 2.56 × 1012 sej/kg (wet material) and 2.46 × 1012 sej/kg (dry material). Emergy indicators such as environmental loading ratios which were calculated at 2390 (wet material) and 2300 (dry material); emergy yield ratios at 15.7 and 15.8; and emergy sustainability indices at 0.0066 and 0.0069 for dry and wet materials used in cement manufacturing, respectively; these show the immense impact on the environment in China.
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.
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•Comparison of energy-based indicators used in building environmental assessment tools.•Application of EIO-LCA, Eco-LCA, Emergy and ATHENA Impact Estimator for buildings.•Lists challenges in evaluating buildings using LCA and similar approaches.
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