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At least 1,567 records · Page 87
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Modeling occupancy-driven building loads for large and diversified building stocks through the use of parametric schedules

Building energy modeling provides a fundamental tool to assess the potential for energy efficiency to contribute to reducing world energy consumption and global emissions. Occupancy-related operations are a key source of uncertainty for building energy analysis, particularly for aggregated building stocks. At a district or city level, it is critical to estimate aggregated power load profiles for sizing power grid infrastructure, power plant capacity allocation, and energy efficiency measures. The stochastic nature of behavior-related operations complicates the creation of models that accurately capture building load profiles for entire building stocks. This research introduces a new methodology called parametric schedules to model occupancy-driven schedules for large and diverse building stocks. In contrast to computationally expensive methodologies proposed in the literature, our work does not use a recursive time-consuming step. Occupancy is estimated by the extrapolation of operation times directly from metered electric consumption data; occupancy-related schedules are stochastically assigned to each building model, guaranteeing diversity of operation times in the stock. Our procedure has been tested on a large, diversified data-set of 25,000 commercial buildings in Los Angeles, California. It proved to be able to adequately represent the stochastic schedules diversity of the stock and to refine the stock calibration process by 1%. This innovative approach represents a useful asset for utility companies, grid operators, urban planners, and balancing authorities, seeking to improve building stock modeling and better estimate the impact of energy conservation measures. – This work is part of a larger stock modeling tool called ComStock, which is under development by NREL.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Intelligent multi-zone residential HVAC control strategy based on deep reinforcement learning

Residential heating, ventilation, and air conditioning (HVAC) has been considered as an important demand response resource. However, the optimization of residential HVAC control is no trivial task due to the complexity of the thermal dynamic models of buildings and uncertainty associated with both occupant-driven heat loads and weather forecasts. In this paper, we apply a novel model-free deep reinforcement learning (RL) method, known as the deep deterministic policy gradient (DDPG), to generate an optimal control strategy for a multi-zone residential HVAC system with the goal of minimizing energy consumption cost while maintaining the users’ comfort. Here, the applied deep RL-based method learns through continuous interaction with a simulated building environment and without referring to any prior model knowledge. Simulation results show that compared with the state-of-art deep Q network (DQN), the DDPG-based HVAC control strategy can reduce the energy consumption cost by 15% and reduce the comfort violation by 79%; and when compared with a rule-based HVAC control strategy, the comfort violation can be reduced by 98%. In addition, experiments with different building models and retail price models demonstrate that the well-trained DDPG-based HVAC control strategy has high generalization and adaptability to unseen environments, which indicates its practicability for real-world implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Carbon emission responsive building control: A case study with an all-electric residential community in a cold climate

In the United States, buildings account for 35% of total energy-related carbon dioxide emissions, making them important contributors to decarbonization. Carbon intensities in the power grid are time-varying and can fluctuate significantly within hours, so shifting building loads in response to the carbon intensities can reduce a building’s operational carbon emissions. This paper presents a rule-based carbon responsive control framework that controls the setpoints of thermostatically controlled loads responding to the grid’s carbon emission signals in real time. Based on this framework, four controllers are proposed with different combinations of carbon accounting methods and control rules. To evaluate their performance, we performed simulation studies using models of a 27-home, all-electric, net zero energy residential community located in Basalt, Colorado, United States. The carbon intensity data of four future years from the Cambium data set are adopted to account for the evolving resource mix in the power grid. Various performance metrics, including energy consumption, carbon emission, energy cost, and thermal discomfort, were used to evaluate the performance of the controllers. Sensitivity analysis was also conducted to determine how the control thresholds and intervals affect the controllers’ performance. Simulation results indicate that the carbon responsive controllers can reduce the homes’ annual carbon emissions by 6.0% to 20.5%. However, the energy consumption increased by 0.9% to 6.7%, except in one scenario where it decreased by 2.2%. Compared to the baseline, the change in energy cost was between –2.9% and 3.4%, and thermal discomfort was also maintained within an acceptable range. The little impact on energy cost and thermal discomfort indicates there are no potential roadblocks for customer acceptance when rolling out the controllers in utility programs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Stochastic simulation of occupant-driven energy use in a bottom-up residential building stock model

The residential buildings sector is one of the largest electricity consumers worldwide and contributes disproportionally to peak electricity demand in many regions. Strongly driven by occupant activities, household energy consumption is stochastic and heterogeneous in nature. However, most residential energy models applied by industry use homogeneous, deterministic activity schedules, which work well for predictions of annual energy consumption, but can result in unrealistic hourly or sub-hourly electric load profiles, with exaggerated or muted peaks. The increasing proportion of variable renewable energy generators means that representing the heterogeneity and stochasticity of occupant behavior is now crucial for reliable planning at both bulk-power and distribution-system scales. This work presents a novel and open-source occupancy simulation approach that can simulate a diverse set of individual occupant and household event schedules for all major electricity, fuel, and hot water end uses. To accomplish this, we evaluated three alternative occupant activity simulation approaches before selecting a hybrid combining time-inhomogeneous Markov chains and probability-sampling of event durations and magnitudes. Further, we integrated the stochastic occupancy simulation with an open-source bottom-up physics-simulation building stock model and published a set of 550,000 diverse household end-use activity schedules representing a national housing stock. The simulator was verified against time-use survey data, and simulation results were validated against measured end-use electricity data for accuracy and reliability. While we use data for the United States, our application demonstrates how similar approaches could be applied using the time-use survey data collected in many countries around the world.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

PDE 5 inhibitor improves insulin sensitivity by enhancing mitochondrial function in adipocytes

Adipocytes are involved in many metabolic disorders. It was recently reported that phosphodiesterase type 5 (PDE5) is expressed in human adipose tissue. In addition, PDE5 inhibitors have been shown to improve insulin sensitivity in humans. However, the mechanism underlying the role of PDE5 inhibitors as an insulin sensitizer remains largely unknown. The present study was undertaken to investigate the role of the PDE5 inhibitor udenafil in insulin signaling in adipocytes and whether this is mediated through the regulation of mitochondrial function. To study the mechanism underlying the insulin sensitizing action of PDE5 inhibitors, we evaluated quantitative changes in protein or mRNA levels of mitochondrial oxidative phosphorylation (OxPhos) complex, oxygen consumption rate (OCR), and fatty acid oxidation with varying udenafil concentrations in 3T3-L1 cells. Our cell study suggested that udenafil enhanced the insulin signaling pathway in 3T3-L1 cells. Following udenafil treatment, basal mitochondrial OCR, maximal OxPhos capacity, and OxPhos gene expression significantly increased. Finally, we examined whether udenafil can affect the fatty acid oxidation process. Treatment of 3T3-L1 cells with udenafil (10 and 20 μM) significantly increased fatty acid oxidation rate in a dose-dependent manner. In addition, the expression of peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC-1α) significantly increased. We demonstrated that the PDE5 inhibitor udenafil enhances insulin sensitivity by improving mitochondrial function in 3T3-L1 cells. This might be the mechanism underlying the PDE5 inhibitor-enhanced insulin signaling in adipocytes. This also suggests that udenafil may provide benefit in the treatment of type 2 diabetes and other related cardiovascular diseases. - Highlights: • Udenafil enhanced the insulin signaling pathway. • Udenafil improved mitochondrial function by increasing oxygen consumption rate (OCR). • Udenafil upregulated mitochondrial OxPhos gene expression. • Udenafil was involved in increasing fatty acid oxidation (FAO).

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Efficacy of occupancy-based smart ventilation control strategies in energy-efficient homes in the United States

Proper ventilation of residences is essential for occupant health and comfort, and is responsible for a significant portion of energy consumption in homes. This study examines a method for providing adequate ventilation in homes while reducing energy consumption and peak demand: “smart” control of ventilation through sensing of occupancy and modulation of ventilation fans. We first conducted a detailed simulation study of advanced California homes with several occupancy-based ventilation control strategies. We then look at how general these results are nationally through a second simulation campaign in 15 ASHRAE climate zones. All simulations compared equivalent indoor air quality situations and assessed energy savings benefits. A key difference from previous demand-controlled ventilation strategies is that our study includes the effects of building related contaminants that are continuously emitted, irrespective of occupancy status, consistent with the requirements in ASHRAE Standard 62.2-2016. Under this new assumption, it is very difficult to extract substantial energy savings using only occupancy sensing. For the baseline strategy, savings were less than 10% of ventilation energy and sometimes negative in all cases analyzed other than leakier 2-story homes. Addition of a pre-occupancy flush period increases savings somewhat, but savings are still less than 15% other than in 2-story leakier homes.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

The interactive indoor-outdoor building energy modeling for enhancing the predictions of urban microclimates and building energy demands

There is a lack of an urban building energy modeling framework that considers the influence of surrounding buildings and local urban climate on building thermal performance. This can lead to inaccurate results since the thermal performance of individual buildings is heavily influenced by their surrounding built and climatic environment. This study establishes an interactive indoor-outdoor building energy modeling method to enhance the predictions of urban microclimates and building energy demands by coupling an urban physics model with a physics-based building energy model. Validation of the interactive coupling scheme uses field measurement datasets. Parametric simulation and analysis are conducted to understand the influence of the roof-to-canyon width ratio, canyon orientation, and ground vegetation fraction on canyon temperature, building energy consumption, and energy demand. Furthermore, the impacts of building energy model complexity (e.g., detailed vs. simplified building models) and coupling approaches on canyon temperature and building energy profiles are demonstrated using two case study buildings. In comparison with the one-way coupling approach, cooling energy consumption predicted with the dynamic two-way coupling approach varies by 3.5% and 0.5% for the detailed medium office building model and high-rise building model, respectively, and peak cooling demand varies by 8.4% and 7.0% for the detailed medium office building model and high-rise building model, respectively. Here this study also suggests that adopting a complex two-way coupling approach with environmental data exchange at various elevations is necessary for modeling tall buildings at the urban scale.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

An integrated approach to optimizing concentration shock wave electrodialysis using 2D multicell simulation and response surface models

Shock wave electrodialysis (SWED) is a highly promising technique for energy-efficient ion separation in the context of a circular economy. This paper presents a approach way of modeling and improving SWED using a two-dimensional multicell model combined with the COMSOL program and response surface methodology. The model integrates the Nernst-Planck equation, Darcy's law, and first-order electroosmosis to examine the local concentration, flux of ionic species, distribution of current, and velocity of flow in SWED cells under various operating conditions. We first illustrate the clear depiction of concentration, velocity, and electric potential distribution through contours which aids in identifying optimal operating conditions and designing scalable SWED systems. The results emphasize the significance of surface charge density and voltage in influencing the features of shock waves for obtaining effective ion separation while optimizing energy consumption and improving current efficiency by controlling the retention time of feed flow. Here, this study defines two crucial characteristics of shock waves, namely the length of the flat depletion zone of a fully developed shock wave (shock wave height) and the distance of shock wave propagation (shock wave length). These properties significantly impact separation performance, as determined by the simulation results. Additionally, the response surface methodology is incorporated with the COMSOL models to develop predictive models and graph responses, enabling a more comprehensive understanding of the interactions between parameters and performance indicators, such as removal ratio, energy consumption, and water recovery. Finally, this work suggests design tactics for expanding SWED processes and outlines potential areas for further research. This research provides valuable insights into the prospective applications, design optimization, and scalability of SWED in the field of electrokinetic separation technologies for green chemistry and a circular economy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Predictive model for real-time energy disaggregation using long short-term memory

To provide affordable energy-saving solutions for the small and medium-sized manufacturers (SMMs), we propose a unified framework for generating predictive models that support real-time disaggregation of power consumption from combined inputs, enabling automatic machine state identification simultaneously for joint analysis of energy usage patterns. Further, the proposed framework transforms raw power consumption into a time series with look-back and bootstrap capabilities for historical pattern detection, while a learning architecture utilizes the stacked long short-term memory (LSTM) layers as encoders for embedding generation with sequential awareness. Experimental results demonstrate 93.65% minimum accuracy in ideal case of real-time energy usage and machine state prediction.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Non-equilibrium low-temperature plasma-assisted combustion of iso-octane: Perturbing pyrolysis and oxidation kinetics

Here, in this study, a plasma-coupled flow reactor facility is used to examine the effects of non-equilibrium low-temperature plasmas on perturbing the pyrolysis and oxidation kinetics of iso-octane. Experiments were performed in highly dilute reactive mixtures of nitrogen, at near isothermal conditions for temperatures ranging from 523 K to 1203 K. Experiments cumulatively demonstrated enhanced chemical reactivity with the plasma for temperatures below 900 K, and a lowering of the hot-ignition temperature. Detailed kinetic insight was derived from a 0D plasma-coupled kinetic model, utilizing a constructed mechanism that combined both plasma-specific chemistry and the neutral combustion chemistry. For pyrolysis conditions, the model displayed relatively good agreement with fuel consumption and the formation of most intermediates compared to the experimental data, demonstrating the model is able to accurately predict primary radical formation from the plasma directly interacting with the fuel. Enhanced reactivity was attributed to collisional quenching of excited-states of N 2 with fuel, which led to efficient fuel fragmentation and enhancement of the H-radical flux. For oxidation conditions, the model displayed satisfactory agreement with the experiments. Model predictions were able to accurately predict fuel consumption and most intermediate speciation data for T > 800 K, but most discrepancies were towards T < 800 K in particular with oxygenated intermediates. In the presence of oxygen, plasma effects were predominantly spent on efficient enhancement of O- and H-radical fluxes, leading to further fuel fragmentation and initiation of both the OH- and HO 2 -radical pools. Subsequent reactivity of iso-octane was then dictated by the response of the temperature-dependent neutral chemistry. At low-temperatures (T = 643 K), enhanced fuel radicals and O 2 -additon chemistry lead to the formation of oxygenated species, while at intermediate temperatures (T = 843 K) net decrease in OH-radical reactivity led to an increase in hydrocarbon speciation. Near the self-ignition threshold (T = 1163 K), radicals generated by high-temperature branching reactions dominate the oxidation process and effectively ignition. This study ultimately demonstrated that the enhancement of radicals afforded by the plasma causes a deviation in known understanding of iso-octane kinetics in some regards and warrants future studies to reconcile these discrepancies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

RLGBS: Reinforcement Learning-Guided Beam Search for process optimization in a paper machine dryer section

Paper drying is responsible for over two-thirds of energy consumption in the U.S. pulp and paper industry, presenting significant potential for energy savings through optimization of process parameters. Current approaches often assume fixed operating conditions, neglecting dynamic ambient and process variations that limit achievable savings and real-world applicability. To this end, we develop a physics-based simulation environment for a paper machine dryer section and propose a reinforcement learning (RL) framework to minimize overall energy consumption by optimizing drying process parameters under diverse operating conditions. To mitigate overdrying and numerical instabilities caused by suboptimal local RL actions, we introduce Reinforcement Learning-Guided Beam Search (RLGBS), which explores multiple action sequences in parallel using beam search. Instead of making step-by-step decisions, RLGBS prioritizes solutions based on cumulative probability, reducing the impact of individual suboptimal actions. Experiments demonstrate that RLGBS achieves consistent energy savings under unseen operating conditions not encountered during training, outperforming conventional RL methods. While validated in drying optimization, this framework is broadly applicable to other RL-based industrial process control problems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Environmental assessment and investment strategies of provincial industrial sector in China — Analysis based on DEA model

As an energy-intensive industry, the industrial sector consumes 70% of energy consumption and causes serious environmental pollution in China. Also, the government emphasized the promotion of R&D investment in the industrial sector in China's National Plan on Climate Change (2014–2020). It is meaningful and contributes to assessing energy and environmental performance, as well as R&D and industrial pollution control (IPC) investment strategies of China's industrial sector. A non-radial DEA model, as with natural and managerial disposability, was adopted to evaluate this from provincial and regional perspectives during the 2008–2012 period. Energy and environmental performance was evaluated by unified efficiency under natural disposability (UEN), unified efficiency under managerial disposability (UEM), and unified efficiency under natural and managerial disposability (UENM). The empirical results indicated that Shandong and Hainan were efficient under natural and managerial disposability, while other provinces had the potential to improve their energy and environmental performance. The number of provinces that was fit for investments of R&D and IPC increased from 2008 to 2010, then decreased in 2011 and 2012. In spite of this, many provincial industrial sectors should make efforts to reduce pollution by investment on technology. Tianjin, Heilongjiang, Jiangxi and Henan were especially the best investment objects because investments of R&D and IPC turned to be effective for them during the whole study period. Moreover, western China had the highest average UENM, followed by eastern China and central China. Eastern China and central China were rewarding to expand investments. Coal consumption was the main factor to negatively affect unified efficiency whereas the increase in economic development level was primarily responsible for the improvement of unified efficiency. According to the results, differentiated suggestions to further improve energy and environmental performance were proposed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Life Cycle Assessment to support the quantification of the environmental impacts of an event

In recent years, several tools have been used to define and quantify the environmental impacts associated with an event; however, a lack of uniform approaches for conducting environmental evaluations has been revealed. The aim of this paper is to evaluate whether the Life Cycle Assessment methodology, which is rarely applied to an event, can be an appropriate tool for calculating the environmental impacts associated with the assembly, disassembly, and use phase of an event analysing in particular the components and the displays used to establish the exhibits. The aim is also to include the issues reported by ISO 20121:2012 involving the interested parties that can be monitored but also affected by the event owner, namely the event organiser, the workforce and the supply chain. A small event held in Northern Italy was selected as the subject of the research. The results obtained show that the main contributors are energy consumption for lighting and heating and the use of aluminium materials, such as bars for supporting the spotlights, carpet and the electronic equipment. A sensitivity analysis for estimating the effects of the impact assessment method chosen has also been conducted and an uncertainty analysis has been performed using the Monte Carlo technique. This study highlighted the importance of the energy consumed by heating and lighting on the environmental implications, and indicated that the preparation and assembly should always be considered when quantifying the environmental profile of an event. - Highlights: • LCA methodology, developed for products and services, is applied to an event. • A small event held in Northern Italy is analysed. • The main contributors are energy consumption and the use of aluminium and carpet. • Exhibition site preparation can have important environmental implications. • This study demonstrates the importance of the assembly, disassembly and use phase.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Life cycle impact assessment of home energy management systems (HEMS) using dynamic emissions factors for electricity in Finland

Decarbonising the European economy is a long-term goal in which the residential sector will play a significant role. Smart buildings for energy management are one means of decarbonisation, by reducing energy consumption and related emissions. This study investigated the environmental impacts of smart house automation using life cycle impact assessment. The ReCiPe method was selected for use, in combination with dynamic emissions factors for electricity in Finland. The results indicated that a high level of technology deployment may be counter-effective, due to high electricity consumption by the sensor network, automation system and computing devices. The results also indicated that number of inhabitants per household directly affected the environmental impacts of home automation. A single-person household saw its environmental impacts increase by 15%, while those of a five-person household increased by 3% in the worst-case scenario. The manufacturing phase contributed the major share of environmental impacts, exceeding the use phase in multiple categories. These findings indicate that finding the sweet spot in which technology can promote decarbonisation will be crucial to achieving the goal of a low‑carbon economy. - Highlights: •HEMS did not reduce the overall environmental impact of households. •Environmental impacts of HEMS are greater for single inhabitant households. •Energy efficiency of sensing devices must drastically increase to promote decarbonisation. •The highest life cycle environmental impacts of electronics are during the manufacturing phase. •Raising awareness is a critical part in decreasing the environmental impact of households.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2016

A window into occupant-driven energy outcomes: Leveraging sub-metering infrastructure to examine psychosocial factors driving long-term outcomes of short-term competition-based energy interventions

Competition-based “energy saving” interventions are increasingly promoted as an effective strategy for reducing energy consumption in buildings with large occupant controlled electrical loads. However, the factors that drive energy savings in such interventions are not well understood, nor are the impacts of short-term competitions on long-term energy performance. A total of 39 8-occupant suites in a freshman residence hall were instrumented with “smart” electric meters, which recorded circuit-level electricity consumption at 15-minute intervals. During a three-week Fall 2014 competition, suites competed to reduce their overall electricity demand and achieved a 6.4% reduction in whole-building demand overall and a 12% reduction during hours of peak demand (from 12:00 to 19:00), despite peak seasonal temperatures and all-time record electricity demand. Analysis incorporating weather-normalized HVAC demand after the competition showed a significant “rebound” for a large portion of the suites (19), however 12 suites made further reductions, and the remainder maintained demand at the competition level. As a result, we compared energy data with self-reported survey data and identified self-efficacy beliefs, pro-environmental behaviors, and sense of affiliation with other residents of the hall as key factors distinguishing the suites with the greatest and most persistent reductions in demand from suites that maintained or increased demand.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Energy and environmental consequences of a cool pavement campaign

Raising the albedo (solar reflectance) of streets can lower outside air temperature, reduce building energy use, and improve air quality in cities. However, the production and installation of pavement maintenance and rehabilitation treatments with enhanced albedo (“cool” pavements) may entail more or less energy consumption and carbon emission than that of less-reflective treatments. We developed several case studies in which a cool surface treatment is substituted for a more typical treatment (that is, a cool technology is selected instead of a more typical technology). We then assessed over a 50-year analysis period the changes in primary energy demand (PED, excluding feedstock energy) and global warming potential (GWP, meaning carbon dioxide equivalent) in Los Angeles and Fresno, California. The analysis considers two stages of the pavement life cycle: materials and construction (MAC), comprising material production, transport, and construction; and use, scoped as the influence of pavement albedo on cooling, heating, and lighting energy consumption in buildings. In Los Angeles, substituting a styrene acrylate reflective coating or a chip seal for a slurry seal in routine maintenance, or a bonded concrete overlay on asphalt (BCOA) without supplementary cementitious materials (SCM) for mill-and-fill asphalt concrete in conventional or long-life rehabilitation, induced MAC-stage PED and GWP penalties that substantially exceeded use-stage savings, primarily due to material production. Modified rehabilitation cases in which SCM comprised 21% to 50% of the BCOA's total cementitious content by mass (portland cement + SCM) yielded smaller total (MAC + use) PED and GWP penalties, or even total PED and GWP savings. Trends in Fresno were similar, with some differences in GWP outcomes that result from Fresno's longer heating season. The modified rehabilitation cases using BCOA with high SCM content yielded total GWP savings in each city; all other cases yielded total GWP penalties. Here, the magnitude of the one-time GWP offset offered by global cooling from the increased albedo itself always, and sometimes greatly, exceeded the 50-year total GWP penalty or savings. In Los Angeles, the annual building conditioning (cooling + heating) PED and energy cost savings intensities yielded by cool pavements were each about an order of magnitude smaller than the corresponding savings from cool roofs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Integrating diagnostics and model-based optimization

Energy Management and Information Systems are a family of analytics technologies that include energy information systems, fault detection and diagnostics (FDD), and automated system optimization tools. Such systems have the potential to enable buildings to meet energy management goals of reducing total energy consumption and cost. Most current market offerings use data-driven and rule-based analytics. However, the use of physics-based models in the analytics offers potential improvements by providing an accurate estimation of outputs based on representation of the physical principles governing the building system behaviors. This also permits the use of design stage models to inform commissioning and operation. This paper describes the development and testing of a hybrid data-driven and physics model-based operational tool for energy efficiency in central cooling plants. The tool offers FDD functionality, setpoint optimization, and visualization of key performance parameters. It was demonstrated at a university campus in the mixed-humid ASHRAE Climate Zone 4A. Key performance metrics that were analyzed include plant electricity use reduction, plant model calibration, and system economics. Annual simulations indicate the tool can provide electricity savings of greater than 10% for approximately six months of the year, mainly during the winter season when wet bulb temperatures are low, though only 1.38% savings for the entire year. Additionally, over a 4-day period in April, recommended optimal setpoints were implemented, resulting in 17% savings versus metered baseline consumption. With respect to model calibration, the difference between model-predicted and measured parameters was less than 10% for 90% of data points acquired for three of six chillers, and for each ten cooling towers. Finally, the tool users reported that satisfaction with the capabilities was equal to or better than that with the preexisting BAS system.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗