An Efficient and Accurate Building Optimization Strategy Using Singular Value Decomposition

Optimizing the life cycle cost of a building typically involves a large number of variables due to the many options that exist at the time that a building is being designed. Such large-scale optimization problems are often prohibitive within the building industry because of the excessive computational time required by the building energy modeling software; therefore, any optimization studies that are performed during a building design are typically only completed using a small number of variables. To achieve the goal of performing a full life cycle building optimization in an acceptable time frame, this paper proposes an accurate and efficient method using singular value decomposition on the design variables. Through the use of singular value decomposition a large number of design variables can be reduced to a smaller subset of design variables that can be solved more quickly by the optimization algorithm. In this paper the authors apply the singular value decomposition method to a case study of a typical residential building in six separate locations across the U.S. and compare the results with those of the full optimization process over the entire design space

HVAC Solutions for Small- and Medium-sized Commercial Building Retrofit Opportunities

According to the Commercial Building Energy Consumption Survey 2003 (CBECS 2003) conducted by the U.S. Energy Information Administration, over 70% of existing commercial buildings across the United States are more than twenty years old, with many of these buildings soon in need of renovation. Also, CBECS 2003 reports that existing small- and medium-sized commercial buildings (smaller than 200,000 square feet) consume about 75% of the energy used in these buildings, which means there is a great potential for energy savings with integrated technologies and building retrofit solutions, such as HVAC and envelope integration, and window and lighting integration. The primary focus of this study is to compare the annual performance of different types of HVAC equipment in existing small- and medium-sized commercial buildings, and to identify appropriate HVAC systems that could be retrofit into different commercial building types in a cost effective manner. Prototypical building types and characteristics for baseline models are proposed based on the CBECS 2003 microdata; and annual energy simulation results from EnergyPlus are utilized to analyze the different HVAC retrofit technology options

Remotely Accessible Laboratory for Teaching and Research on Solar Thermal Collectors

This paper discusses a new test platform for evaluating the performance of solar thermal collectors that was recently designed and constructed on the roof of the Applied Energy Laboratory at Purdue University, located at 40.4 °N and 86.9 °W. The test platform is mainly used for teaching undergraduate students about applications of thermodynamics and renewable energy, but it can also be used for comparative evaluations of solar thermal collector designs according to an established test standard. The entire system is monitored and controlled by a web-based Building Automation System that automatically tracks and trends both weather data and the performance of individual solar collectors. The online data is particularly helpful for undergraduate education because large numbers of students, including international partners, can access real-time data to learn about solar energy applications. The weather at this location varies significantly by season, which has a substantial impact on the performance of the solar thermal collectors. ASHRAE designates this location as climate zone that experiences both hot, humid summers and cold, dry winters. The solar intensity also varies by season, with longer and more sunny days during the summer and shorter and more cloudy days during the winter. Not surprisingly, evaluations of solar collector performance vary seasonally too. Solar collector efficiency, the ratio of thermal energy collected to the solar energy available, varies from 10% to 80%. Solar energy factor, the ratio of thermal energy collected to the source energy (electricity) to circulate the fluid, varies from 10 to 150. Both performance terms (efficiency and energy factor) are needed to get a complete picture of solar collector performance

Econometric and Environmental Optimization of Combined Cooling, Heating and Power Plant Operation

Combined Cooling, Heat and Power (CCHP) systems have great potential to recover low-grade thermal energy, resulting in higher energy efficiency, reduced emission rates, lower operating costs and a higher level of energy security. In order to fully realize the benefits of CCHP systems in terms of reduced cost and carbon dioxide emissions, effective optimization and control strategies are required. This work presents an approach for optimizing the operation of the CCHP system using a detailed network energy flow model solved by genetic algorithm. The optimal energy dispatch algorithm provides operational signals associated with resource allocation ensuring that the systems meet campus electricity, heating, and cooling demands. The performance of the system will be compared and evaluated with respect to economic and environmental benefits

Comparison of Steady-State and Dynamic Load-Based Performance Evaluation Methodologies for a Residential Air Conditioner

Space cooling and heating equipment account for nearly 32% of the total residential electricity consumption in the U.S. In the residential space conditioning equipment market, air-conditioning and heat-pumping systems are prevalent, so even a slight improvement in these system efficiencies can have a significant impact on reducing the overall energy consumption. Over the years, the energy efficiency benchmarks established by the U.S. Department of Energy have been successful in encouraging manufacturers to develop higher efficiency equipment. These benchmarks are based on an energy efficiency standard, and these standards are based on a rating test procedure that forms the technical basis. Currently, in the U.S., AHRI 210/240 is the rating procedure for residential air-conditioning and heat-pumping equipment, which is based on a steady-state performance measurement method with a degradation coefficient to account for the cycling losses in part-load conditions. Although it provides a standard metric to compare different equipment performances, there has been a debate that this current methodology fails to appropriately characterize the performance of systems with variable-speed compressors and advanced control design. This is largely attributed to the steady-state nature of this current testing approach, which also involves overriding the equipment native control. In contrast to this, a load-based testing methodology has been developed in which the equipment responds to a simulated virtual building load, and the system dynamic performance is measured with its integrated controls. The load-based testing methodology is described in detail by Hjortland and Braun (2019), Patil et al. (2018), and Cheng et al. (2021), which forms the basis for CSA standard draft EXP07:2019 (CSA, 2019). In this paper, these two performance measurement methodologies, steady-state and dynamic load-based, are compared for application to a 5ton residential heat-pump system. The equipment performance was measured in cooling mode and the seasonal performance estimates based on the two testing approaches are compared. The differences in the two test methodologies\u27 performance evaluation results are discussed with a causal analysis of the observed differences

Demonstration of a Load-Based Testing Methodology for Rooftop Units with Integrated Economizers

Current performance evaluation approaches for commercial packaged air conditioning and heat pump equipment (e.g. AHRI 340/360) utilize full-load steady-state performance tests to estimate system EER (energy efficiency ratio) at different ambient conditions and part-load steady-state tests to estimate an IEER (integrated energy efficiency ratio), a figure of merit for system part-load performance. There are some limitations of the current testing approaches and performance metric estimations, including that they do not consider the effects of: 1) test unit embedded controls and their realistic interactions with the building load; 2) different climate zones and building types; and 3) economizer operation. As a result, the overall performance measurement procedure does not appropriately incentivize the development of better performing controls and economizers. In this paper, an improved testing procedure applied to packaged air conditioning equipment, such as rooftop units (RTUs), that include the effects of embedded controls, economizers, climate, and building type is presented. The testing approach is based on allowing the integrated equipment system and controls to respond naturally to a “virtual building load”. This is termed load-based testing and involves dynamically adjusting the indoor room temperature and humidity setpoints for the psychrometric chamber reconditioning system in a manner that emulates the response of a building’s sensible and latent loads to the test equipment controls. The developed test methodology is demonstrated to evaluate the dynamic performance of a 5-ton variable-speed RTU with an integrated economizer in a psychrometric test facility

Experimental Testing of an Oil-Flooded Hermetic Scroll Compressor

In this work, a residential air conditioning compressor designed for vapor injection has been modified in order to inject large quantities of oil into the working chamber in order to approach an isothermal compression process. The compressor was tested with oil injection mass flow fractions of up to 45%. At an evaporating temperature of -10C and condensing temperature of 30C, the overall isentropic efficiency was up to 70% at the highest oil injection rate. Overall, over the testing envelope investigated, there are no significantly negative effects experienced for the compressor and the compressor isentropic efficiency and refrigerant mass flow rate improve monotonically as the oil injection rate is increased

Application of Near-Optimal Tower Control and Free Cooling on the Condenser Water Side for Optimization of Central Cooling Systems

This paper presents an application of tower fan control for optimization of the performance of chiller plants combined with free cooling on the condenser water side. Mathematical models including all the main components of an existing cooling plant were developed and implemented in MATLAB. Simulation results include a mapping of the performance of the plant working in free cooling mode which was used to select control parameters for free cooling operation. Then a mapping of the plant operating with chillers was developed to find the correlation between load and near-optimal air flow, which is the basis of the near-optimal tower control (NOTC) strategy. Finally, simulations were carried out using three consecutive years of historical data to predict the performance of the plant under three different control strategies: 1) tower fan control aiming to keep the temperature of the water supplied to chiller condensers at a constant set point (current control strategy), 2) NOTC and 3) NOTC and free cooling combined. Comparison of the performance of the plant with the baseline (constant condenser water temperature) shows that significant savings can be achieved through the implementation of NOTC along with free cooling. It is expected that the methodology and results of this study provide a useful framework for optimization of cooling plants

A Generalized Approach for Automated Compressor Performance Mapping Using Artificial Neural Network

In the last decades, several research and development efforts led to new compressor technologies that have been successfully introduced into market such as hermetic compressors with variable-speed motors, compressors with economization lines (both vapor and liquid injection lines), hermetic linear compressors, novel capacity modulations, and oil-flooding among others. During the process of implementing new compressor technologies, performance mapping is essential to predict the system behavior across different operating conditions. However, current standard AHRI-540 for compressor performance rating utilizes a 10-coefficient polynomial model that has severe limitations to include compressor enhancement technologies and variable operating range. In addition, it is common practice in industry to calibrate such polynomial correlations with at least 15 to 20 compressor calorimeter test points for a single compressor to ensure a good fit, which results in extended laboratory testing time and relatively high associated costs. Therefore, an automated compressor performance mapping approach based on artificial neural network (ANN) modeling is proposed to address and overcome the limitations of the current standard including applicability to any positive displacement compressors and minimization of number of test points required to accurately predict the compressor envelope. In this paper, the performance of a positive displacement compressor is mapped by this novel methodology, which relies on an algorithm that effectively determines the minimum set of data points required and optimizes the training/testing of ANN architecture. The accuracy and reliability of the proposed methodology are compared to the conventional 10-coefficient polynomial mapping. Lastly, the propagated uncertainties through the model and its extrapolation capabilities are also analyzed