82 research outputs found
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A comprehensive review of energy-related data for US commercial buildings
U.S. commercial buildings consumed around 18% of total primary energy in 2017 and a 2.23 EJ increase is expected by 2050. Energy-related data for commercial buildings can be used for various applications, including benchmarking, building component analysis, market potential analysis, and policy making. Although there are plenty of data sources for energy usage in commercial buildings, they have not been thoroughly reviewed and summarized. As a result, users do not have comprehensive guidelines about selections of right data sources for specific application needs. To fill this gap, this paper conducts a comprehensive review to summarize data sources for energy usage in U.S. commercial buildings and discuss their usages for different applications. First, the paper summarizes the survey and simulation data sources for energy usage. The data sources are compared in terms of their data collection methods, released information, and relevant features. Second, this paper analyzes the applications for different survey and simulation data sources. This review categorizes the applications of data sources into five categories, including energy performance benchmarks, energy use forecasts and predictions, energy use contributions of building components, supports of building energy policies, and urban-scale energy use analysis. Moreover, the paper introduces several cases to demonstrate the usages of these data sources.</p
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Assessments of Data Centers for Provision of Frequency Regulation
There are numerous opportunities for data centers to participate in demand response programs considering their large energy capacities, flexible working environments and workloads, redundant design and operation, etc. As a type of demand response, frequency regulation requires fast response, and its potential is not fully explored by data centers yet. This paper proposes a synergistic control strategy for data center frequency regulation which uses both IT and cooling systems. It combines power management techniques at the server level with control of the chilled water supply temperature to track the regulation signal from the electrical market. A frequency regulation flexibility factor is also proposed to increase the IT capacity for frequency regulation. The performance of the control strategy is studied through numerical simulations using an equation-based object-oriented Modelica platform designed for data centers. Simulation results show that with well-tuned control parameters, data centers can provide frequency regulation service in both regulation up and down. The performance of data centers in providing frequency regulation service is largely influenced by the regulation capacity bid, frequency regulation flexibility factor, workload condition, and cooling mode of the cooling system, and not significantly influenced by the time constant of chillers. In addition, compared with a server-only control strategy, the proposed synergistic control strategy can provide an extra regulation capacity of 3% of the design power when chillers are activated. When chillers are deactivated, both strategies have a similar regulation capacity.
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A Bayesian Network Model for Predicting Cooling Load of Commercial Buildings
Cooling load prediction is indispensable to many building energy saving strategies. In this paper, we proposed a new method for predicting the cooling load of commercial buildings. The proposed approach employs a Bayesian Network model to relate the cooling load to outdoor weather conditions and internal building activities. The proposed method is computationally efficient and implementable for use in real buildings, as it does not involve sophisticated mathematical theories. In this paper, we described the proposed method and demonstrated its use via a case study. In this case study, we considered three candidate models for cooling load prediction and they are the proposed Bayesian Network model, a Support Vector Machine model, and an Artificial Neural Network model. We trained the three models with fourteen different training data datasets, each of which had varying amounts and quality of data that were sampled on-site. The prediction results for a testing week shows that the Bayesian Network model achieves similar accuracy as the Support Vector Machine model but better accuracy than the Artificial Neural Network model. Notable in this comparison is that the training process of the Bayesian Network model is fifty-eight times faster than that of the Artificial Neural Network model. The results also suggest that all three models will have much larger prediction deviations if the testing data points are not covered by the training dataset for the studied case (The maximum absolute deviation of the predictions that are not covered by the training dataset can be up to 7 times larger than that of the predictions covered by the training dataset). In addition, we also found the uncertainties in the weather forecast significantly affected the accuracy of the cooling load prediction for the studied case and the Support Vector Machine model was more sensitive to those uncertainties than the other two models.</p
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A methodology to create prototypical building energy models for existing buildings: A case study on US religious worship buildings
Prototypical building energy models are of great significance because they are the starting point in conducting analyses for various applications, such as building energy saving potential analysis, building design, building energy market evaluation, and building energy policy-making. However, current prototypical building energy models only represent limited types of buildings in certain countries. To fill the gap, this paper proposes a methodology to systematically create prototypical building energy models. First, a six-step methodology is introduced: model input identification, data collection, data cleaning, data conversion, model simulation, and model calibration. Then, the methodology is demonstrated by a case study of creating 30 prototypical energy models for U.S. religious worship buildings, representing buildings in 15 climate zones and 2 vintages (pre- and post-1980). Finally, to show the applications of the models, the building energy saving potentials from six efficiency measures are analyzed for pre-1980 U.S. religious worship buildings in three ASHRAE Climate Zones. The results show that the maximum energy saving potentials are approximately 30% for the religious worship buildings in all three climate zones investigated, indicating significant opportunities for energy savings in these buildings through their prototypical building model development.</p
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Model-based optimal operation of heating tower heat pump systems
In current applications of heating tower heat pumps (HTHPs), the systems tend to run with constant speed or fixed set points, which can be inefficient under varying weather data and building loads. To address this issue, this study proposes a model-based optimal operation of the HTHPs to achieve energy savings in both cooling and heating modes. Firstly, a physics-based model for an existing HTHP system was developed. Then, artificial neural network (ANN) models were developed and trained with vast amount of operational data generated by the physics-based model. The ANN models were found to be highly accurate (average relative error less than 1%) and computationally efficient (about 300 times faster than the physics-based model). After that, three optimal approaches were proposed to minimize the total energy consumption of the HTHP system. Approach 1 optimizes the load distribution between different heat pump units. Approach 2 optimizes the speed of fans and pumps by fixed approach and range of the condenser water (or evaporator solution). Approach 3 optimizes both the load distribution and the speed of fans and pumps. The optimization is implemented by using the ANN models, proposed approaches, and a genetic algorithm via a case study. The results show that the energy savings in the cooling season are 2.7%, 11.4%, and 14.8% by the three approaches, respectively. In the heating season, the energy savings of the three approaches are 1.6%, −1.4%, and 4.7%, respectively. Moreover, the thermodynamic performance in typical days was analyzed to investigate how energy savings could be achieved.
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Urban building energy performance prediction and retrofit analysis using data-driven machine learning approach
Stakeholders such as urban planners and energy policymakers use building energy performance modeling and analysis to develop strategic sustainable energy plans with the aim of reducing energy consumption and emissions from the built environment. However, inconsistent energy data and the lack of scalable building models create a gap between building energy modeling and traditional planning practices. An alternative approach is to conduct a large-scale energy usage survey, which is time-consuming. Similarly, existing studies rely on traditional machine learning or statistical approaches for calculating large-scale energy performance. This paper proposes a solution that employs a data-driven machine learning approach to predict the energy performance of urban residential buildings, using both ensemble-based machine learning and end-use demand segregation methods. The proposed methodology consists of five steps: data collection, archetype development, physics-based parametric modeling, machine learning modeling, and urban building energy performance analysis. The devised methodology is tested on the Irish residential building stock and generates a synthetic building dataset of one million buildings through the parametric modeling of 19 identified vital variables for four residential building archetypes. As a part of the machine learning modeling process, the study implemented an end-use demand segregation method, including heating, lighting, equipment, photovoltaic, and hot water, to predict the energy performance of buildings at an urban scale. Furthermore, the model's performance is enhanced by employing an ensemble-based machine learning approach, achieving 91% accuracy compared to the traditional approach's 76%. Accurate prediction of building energy performance enables stakeholders, including energy policymakers and urban planners, to make informed decisions when planning large-scale retrofit measures
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“Performance Comparison of a Heating Tower Heat Pump and an Air-Source Heat Pump: A Comprehensive Modeling and Simulation Study
The heating tower heat pump (HTHP) is proposed as an alternative to the conventional air-source heat pump (ASHP). To investigate the performance improvements of the HTHP over the ASHP, a comprehensive comparison between the two systems was carried out based on a simulation study. Physics-based models for the ASHP and HTHP were developed. The performance of the ASHP under frosting conditions was corrected with a newly developed frosting map, and the regeneration penalization was considered for the HTHP. Based on the models and corrections, hourly simulations were carried out in an office building in Nanjing, China. The results show that the average energy efficiency of the HTHP in summer is 23.1% higher than that of the ASHP due to the water-cooled approach adopted by the HTHP. In winter, the HTHP achieves an increase of 7.4% in efficiency due to the frost free and energy storage characteristics. While the initial cost of the HTHP is 1.2% higher than that of the ASHP, the HTHP can still save 9.7% cost in a 10-year period because of its lower power consumption.</p
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Building Energy Simulation Coupled with CFD for Indoor Environment: A Critical Review and Recent Applications
This paper presents a comprehensive review of the open literature on motivations, methods and applications of linking stratified airflow simulation to building energy simulation (BES). First, we review the motivations for coupling prediction models for building energy and indoor environment. This review classified various exchanged data in different applications as interface data and state data, and found that choosing different data sets may lead to varying performance of stability, convergence, and speed for the co-simulation. Second, our review shows that an external coupling scheme is substantially more popular in implementations of co-simulation than an internal coupling scheme. The external coupling is shown to be generally faster in computational speed, as well as easier to implement, maintain and expand than the internal coupling. Third, the external coupling can be carried out in different data synchronization schemes, including static coupling and dynamic coupling. In comparison, the static coupling that performs data exchange only once is computationally faster and more stable than the dynamic coupling. However, concerning accuracy, the dynamic coupling that requires multiple times of data exchange is more accurate than the static coupling. Furthermore, the review identified that the implementation of the external coupling can be achieved through customized interfaces, middleware, and standard interfaces. The customized interface is straightforward but may be limited to a specific coupling application. The middleware is versatile and user-friendly but usually limited in data synchronization schemes. The standard interface is versatile and promising, but may be difficult to implement. Current applications of the co-simulation are mainly energy performance evaluation and control studies. Finally, we discussed the limitations of the current research and provided an overview for future research.</p
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