Commercial thermal distribution systems, Final report for CIEE/CEC

Despite the potential for significant energy savings by reducing duct leakage or other thermal losses from duct systems in large commercial buildings, California Title 24 has no provisions to credit energy-efficient duct systems in these buildings. A substantial reason is the lack of readily available simulation tools to demonstrate the energy-saving benefits associated with efficient duct systems in large commercial buildings. The overall goal of the Efficient Distribution Systems (EDS) project within the PIER High Performance Commercial Building Systems Program is to bridge the gaps in current duct thermal performance modeling capabilities, and to expand our understanding of duct thermal performance in California large commercial buildings. As steps toward this goal, our strategy in the EDS project involves two parts: (1) developing a whole-building energy simulation approach for analyzing duct thermal performance in large commercial buildings, and (2) using the tool to identify the energy impacts of duct leakage in California large commercial buildings, in support of future recommendations to address duct performance in the Title 24 Energy Efficiency Standards for Nonresidential Buildings. The specific technical objectives for the EDS project were to: (1) Identify a near-term whole-building energy simulation approach that can be used in the impacts analysis task of this project (see Objective 3), with little or no modification. A secondary objective is to recommend how to proceed with long-term development of an improved compliance tool for Title 24 that addresses duct thermal performance. (2) Develop an Alternative Calculation Method (ACM) change proposal to include a new metric for thermal distribution system efficiency in the reporting requirements for the 2005 Title 24 Standards. The metric will facilitate future comparisons of different system types using a common ''yardstick''. (3) Using the selected near-term simulation approach, assess the impacts of duct system improvements in California large commercial buildings, over a range of building vintages and climates. This assessment will provide a solid foundation for future efforts that address the energy efficiency of large commercial duct systems in Title 24. This report describes our work to address Objective 1, which includes a review of past modeling efforts related to duct thermal performance, and recommends near- and long-term modeling approaches for analyzing duct thermal performance in large commercial buildings.

Performance of thermal distribution systems in large commercial buildings

A prototype archive for a selection of building energy data on thermal distribution systems in commercial buildings was developed and pilot tested. While the pilot demonstrated the successful development of the data archive prototype, several questions remain about the usefulness of such an archive. Specifically, questions on the audience, frequency of use, maintenance, and updating of the archive would need to be addressed before this prototype is taken to the next level.

The demand for electricity is rapidly growing and renewable energy sources such as solar, wind and tidal energy can compensate the demand to a substantial level. Among these, solar energy is abundant, scalable and is cheaper. The generated energy can be used in an efficient way if the DC output is directly supplied to the load instead of converting it to AC. Every electrical system is capable of operating in DC and, for example, energy efficient Light Emitting Diode (LED) lights have become popular as they provides more lumens with less power consumption and also can be directly operated from DC. LED lighting system in large commercial buildings has irradiance levels which vary sigificantly during operation. Extracting maximum power from the energy system and maintaining constant voltage output at different loads is another challenge. This paper proposes a solar Photo Voltaic (PV)-based energy system including Battery Energy Storage System (BESS) for supplying LED lamps to a commercial building through a modified high gain Luo converter. The Perturb and Observe control algorithm has been used for maximum power extraction from a PV cell whereas PI (Proportional Integral) controllers maintain constant output voltage from PV–BESS against different irradiance levels. To supply the desired voltages to the LED lighting system, a modified high gain Luo converter is designed. To make the output voltage constant at different load currents, PI and Sliding Mode Controllers (SMC) are designed with the help of the state-space average model. It is found that the sliding mode controller outperforms the PI controller in terms of behavior in the transient period and tracking capability. The system is simulated using MATLAB/Simulink®. The Sliding Mode Controller has a 95% less transient period and is 75% faster in tracking capability when compared to other controllers. The system could be incorporated with the PV source to obtain green energy.

The statement “energy is not storable” is heard in energy conference lectures around the world, even though each person in the audience is sitting in a vast energy storage device. The heat storage in buildings is an enormous untapped resource for providing regulation services. This will be especially important as the grid is subject to more and more volatility from the introduction of power from renewable energy sources. This paper describes how regulation services can be obtained by exploiting the inherent flexibility of HVAC (Heating, Ventilation, Air Conditioning) systems in commercial buildings. A particular simulation test case is considered — A large commercial building at the University of Florida. The conclusions of this research demonstrate that, 1) A simplified model of the building that is adequate for control can be obtained from input-output measurements. In this study, the only control input considered is the supply fan power. 2) Control synthesis to regulate the building air temperature while simultaneously providing regulation to the grid can be cast as an LQR problem that admits a simple closed form solution. 3) Numerical experiments show that for this HVAC system, 15% of fan power capacity can be provided for regulation, while maintaining indoor temperature deviation to no more than ±0.2 °C. Based on these results, we conclude that the HVAC systems in 90, 000 medium-sized commercial buildings can provide the entire regulation service needed by PJM today, without any noticeable change in indoor air quality. The total regulation services that can be potentially provided by all the commercial buildings in the U.S. that have the necessary equipment in place are much higher. Our results indicate that supply fans in existing commercial buildings can provide about 70% of the current regulation capacity needed in the United States.

Performance of small and large scales rainwater harvesting systems in commercial buildings under different reliability and future water tariff scenarios

This paper describes the use of artificial neural networks (ANNs) to model cooling energy use in commercial buildings, and compares the attributes of ANNs and least-squares (LS) regression modeling techniques. The neuro-biological roots of ANN models and the fundamentals of the backpropagation algorithm are described. The effects of differing values of model parameters (gain, bias and learning rate) and network architectures (three and five layer networks) on the rate of convergence and prediction accuracy of ANN models are discussed. Finally, the attributes of ANN and least-squares regression models are compared in a case-study example using measured energy use data from a large commercial building. The results draw attention to the importance of parameter selection when using ANN models, and indicate that multiple hidden layers in ANNs appear to be necessary when modeling the non-linear energy use typical of commercial buildings. Introduction The ability to accurately predict the behavior of energy using systems in commercial buildings is increasingly valuable. Predicted energy use can be compared to observed energy use in order to identify operational problems and measure the effectiveness of energy conservation retrofits1. Energy use forecasts can also be incorporated into control procedures which enhance comfort conditions and reduce energy and demand expenses2v3. Although engineering models can estimate building energy use, difficulties inherent in the calibration procedure often limit the accuracy of the resulting predictions. Empirical models, such as LS regression models and ANNs, increase the accuracy and reducing the modeling time required for energy use forecasts. This paper describes the use of ANNs to model cooling energy use in a commercial building and compares the attributes of ANN and LS models. The Neurobioiogical Model and ANNs The human brain is a highly complex organ comprised of some lo1 basic units called neurons. Each neuron is connected to about lo4 other neurons. Because of this highly interconnected nature, the architecture of the brain is referred to as being massively parallel or massively interconnected. Each neuron consists of a soma, dendrites, axons and synapses (Figure 1). The soma is the body of the neuron. Dendrites and axons extend from the soma and branch out like roots. If a neuron receives enough active inputs along its dendrites, it fires and sends a voltage spike down the axons. Axons are connected to other dendrites and somas at synapses. When a neuron fires, chemicals called neurotransmitters are diffused across the synapses. Learning is thought to occur at the synapses where the neurotransmitters and neuroreceivers vary to reinforce good connections and discourage bad connections4. Figure I . Schematic representation of a neuron ANNs attempt to mimic parts of the architecture and functionality of the brain. Neurons are simulated in ANNs as connected nodes. The distributed, parallel processing structure of the brain is simulated by arranging the nodes in layers such that each node is connected to all of the nodes in the adjacent layers. In a manner analogous to the response of a neuron, each node sums the inputs it receives and transmits an output signal to the other nodes to which it is connected. The output signal of each ANN node is multiplied by a weight which is varied during the learning process, just as synaptic neurotransmitters and receivers are varied in the human learning process. The distributed, parallel architecture of the brain is well suited to learning and pattern recognition tasks such as vision, in which several processes and Copyright c 1994 by John K. Kissock. Published by the American Institute of Aeronautics and Astronautics, Inc. with permission. 1290 comparisons are made simultaneously. In contrast, the digital computer is a serial device in which a single central processing unit (CPU) sequentially processes a set of instructions. ANN algorithms simulate the brain's parallel architecture in the serial environment of the digital computer, with the hope of mimicking parts of the brain's amazing capacity for learning and pattern recognition. Generalized Delta, Back-Propagation Algorithm Many different types of ANNs have been devised to accomplish a wide variety of tasks including recognition of handwritten English words, speech recognition and image compression5. The ANNs examined here employ a fully-connected, feedforward architecture (Figure 2). Fully-connected means that each node is connected to all of the nodes in the adjacent layers (or columns of nodes). Feedforward indicates that information is passed in a single direction from the input to the output nodes.

The objective of this scoping study is: (1) to identify current market drivers and technology trends that can improve the demand responsiveness of commercial building lighting systems and (2) to quantify the energy, demand and environmental benefits of implementing lighting demand response and energy-saving controls strategies Statewide. Lighting systems in California commercial buildings consume 30 GWh. Lighting systems in commercial buildings often waste energy and unnecessarily stress the electrical grid because lighting controls, especially dimming, are not widely used. But dimmable lighting equipment, especially the dimming ballast, costs more than non-dimming lighting and is expensive to retrofit into existing buildings because of the cost of adding control wiring. Advances in lighting industry capabilities coupled with the pervasiveness of the Internet and wireless technologies have led to new opportunities to realize significant energy saving and reliable demand reduction using intelligent lighting controls. Manufacturers are starting to produce electronic equipment--lighting-application specific controllers (LAS controllers)--that are wirelessly accessible and can control dimmable or multilevel lighting systems obeying different industry-accepted protocols. Some companies make controllers that are inexpensive to install in existing buildings and allow the power consumed by bi-level lighting circuits to be selectively reduced during demand response curtailments. By intelligently limiting the demand from bi-level lighting in California commercial buildings, the utilities would now have an enormous 1 GW demand shed capability at hand. By adding occupancy and light sensors to the remotely controllable lighting circuits, automatic controls could harvest an additional 1 BkWh/yr savings above and beyond the savings that have already been achieved. The lighting industry's adoption of DALI as the principal wired digital control protocol for dimming ballasts and increased awareness of the need to standardize on emerging wireless technologies are evidence of this transformation. In addition to increased standardization of digital control protocols controller capabilities, the lighting industry has improved the performance of dimming lighting systems over the last two years. The system efficacy of today's current dimming ballasts is approaching that of non-dimming program start ballasts. The study finds that the benefits of applying digital controls technologies to California's unique commercial buildings market are enormous. If California were to embark on an concerted 20 year program to improve the demand responsiveness and energy efficiency of commercial building lighting systems, the State could avoid adding generation capacity, improve the elasticity of the grid, save Californians billion of dollars in avoided energy charges and significantly reduce greenhouse gas emissions.

Hydronic heating and cooling systems are among the most common types of heating and cooling systems installed in older existing buildings, especially commercial buildings. According to the 2012 Commercial Building Energy Consumption Survey (CBECS) data set, hydronic heating systems in the United States include two main systems: (i) boilers inside the building represented with a boiler system and (ii) district steam and hot water systems represented with district heating, which are connected to seven different types of zone-level equipment. Similarly, there are two main hydronic cooling systems: central chillers inside (or adjacent to) the building and district chilled water piped in from outside the building. Chiller systems are investigated based on three different classes: (1) water-cooled, (2) air-cooled, and (3) absorption chillers. This study presents a deep analysis of the 2012 CBECS microdata to characterize hydronic heating and cooling systems by year of construction, census division, building area, building site hydronic system energy use index (EUI), and the types of mechanical systems. The results show that nearly 65% of commercial buildings built before 1990 utilize hydronic heating systems. Hydronic heating and cooling system design are a function of a building area. District heating systems are considered as the main heating systems in buildings with an area greater than 18,600 m2 (200,000 ft2). In addition, systems with central chillers inside the buildings are responsible for providing cooling for more than 50% of the commercial buildings with areas greater than 9,000 m2 (∼100,000 ft2). Among the types of chiller systems, the chiller systems connected to the central air handling units, fan coil units, and duct reheats are the most common systems for large buildings. The results of this building stock characterization provide useful insights into the characteristics of hydronic heating and cooling systems in US commercial buildings.

Buildings consume 73% of electricity produced in the United States and, currently, they are largely passive participants in the electric grid. However, the flexibility in building loads can be exploited to provide ancillary services to enhance the grid reliability. In this paper, we investigate two control strategies that allow Heating, Ventilation and Air-Conditioning (HVAC) systems in commercial and residential buildings to provide frequency regulation services to the grid while maintaining occupants comfort. The first optimal control strategy is based on model predictive control acting on a variable air volume HVAC system (continuously variable HVAC load), which is available in large commercial buildings. The second strategy is rule-based control acting on an aggregate of on/off HVAC systems, which are available in residential buildings in addition to many small to medium size commercial buildings. Hardware constraints that include limiting the switching between the different states for on/off HVAC units to maintain their lifetimes are considered. Simulations illustrate that the proposed control strategies provide frequency regulation to the grid, without affecting the indoor climate significantly.

With the increasing global focus on energy efficiency and sustainable development, intelligent LED lighting systems, as an efficient and energy-saving lighting solution, are gradually gaining attention. This paper focuses on the application of intelligent LED lighting systems in commercial buildings in the United States, aiming to conduct an in-depth analysis of their energy-saving effects and user experience through empirical research. The study selected commercial buildings in three different regions of the United States, including office buildings, shopping centers, and hotels, as case study objects. Intelligent LED lighting systems developed by Shenzhen Romanso Electronic Co., Ltd. were deployed in these venues. These systems integrate advanced functions such as intelligent sensor networks, adaptive dimming algorithms, and remote monitoring platforms. After six months of field monitoring, detailed energy consumption data were collected, and a user satisfaction survey was conducted to compare the performance differences between intelligent LED lighting systems and traditional lighting systems. The research findings provide strong empirical support for the widespread application of intelligent LED lighting systems in commercial buildings in the United States and offer valuable references for the further optimization and promotion of intelligent lighting technologies in the future. Future research will further explore system performance optimization strategies and strive to promote intelligent LED lighting systems to more commercial building fields to achieve broader energy-saving benefits and user experience improvements.

Enhancement of ventilation performance of a residential split-system air-conditioning unit

04/01001 Indirect evaporative cooling potential in air-water systems in temperate climates: Costelloe, B. and Finn, D. Energy and Buildings, 2003, 35, (6), 573–591

Buildings and their occupants are responsible for a large portion of global energy consumption, roughly half of which is associated with the HVAC systems. HVAC systems in large buildings are connected and controlled by a Building Management System (BMS) which are prone to errors, faults and mismanagement and HVAC systems can suffer wastage of up to 30% of total energy consumption through poor controls (Zhang 2021, Granderson 2017). This discrepancy between design and reality is often referred to as the ‘performance gap’. Consequently, there is scope to save a significant amount of energy by addressing these issues. And with the global need to urgently reduce energy demands and carbon emissions it has been a popular topic of research.It follows that a method is needed to identify faults, miscalibration and energy wastage in buildings. Such methods are commonly referred to as fault detection and diagnosis (FDD) methods. Building Management Systems in commercial buildings often connect thousands of sensors and actuators together. The constant communication of these devices produces large amounts of data which has historically been discarded or used in a limited capacity for troubleshooting or commissioning. The infrastructure for this data already exists in most buildings, presenting opportunity for deeper analysis and optimisation. Using this data the FDD methods can be automated, taking engineering knowledge and supplying a data set with rule-based algorithms to find any root causes of control inefficiencies within the building. A secondary approach is to use artificial intelligence models over these data sets to find ways to operate the building more efficiently.This paper shows the effectiveness of an automated FDD approach via 6 case studies in commercial office buildings, highlighting the methods by which faults were identified and resolved, as well as the impacts of the corrective action. It then compares these examples with the findings of research into the effectiveness of the AI-based approach and discusses whether the AI-based approach is more effective than the automated FDD approach or whether the AI-based approach is using significantly more computer power and time to, in fact, find rules to correct control patterns that are already widely known within general engineering practise. The results of this research should indicate whether AI or automated FDD are currently more suitable to close the performance gap in commercial buildings.

This report investigates current efficient alternatives for replacing or supplementing electric lighting systems in commercial buildings. Criteria for establishing the economic attractiveness of various lighting alternatives are defined and the effect of future changes in building lighting on utility capacity. The report focuses on the energy savings potential, economic efficiency, and energy demand reduction of three categories of lighting alternatives: (1) use of a renewable resource (daylighting) to replace or supplement electric lighting; (2) use of task/ambient lighting in lieu of overhead task lighting; and (3) equipment changes to improve lighting energy efficiency. The results indicate that all three categories offer opportunities to reduce lighting energy use in commercial buildings. Further, reducing lighting energy causes a reduction in cooling energy use and cooling capacity while increasing heating energy use. It does not typically increase heating capacity because the use of lighting in the building does not offset the need for peak heating at night.