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Highlight • New on/off controller for residential applications validated using minute-level field data. • On/off controller improves the HVAC accuracy around 19% in terms of NMBE. • On/off cycling makes the operation and associated energy use more realistic. Abstract Heating, ventilation, and air-conditioning (HVAC) systems are sized using design conditions, conditions that occur only a small percentage of time each year. As a result, HVAC systems frequently operate at part-load conditions since their capacity is larger than necessary except in the most extreme weather conditions. Most HVAC systems for residential and small commercial buildings in the U.S. use on/off controls, however, most energy modeling software tools do not simulate the on/off nature of this type of HVAC controls. This paper presents the development and validation of an on/off controller for residential applications in EnergyPlus using a custom EMS (energy management system). This controller is validated using minute-level field data collected for a house located in Sacramento, California with simulated occupancy and internal loads. The results obtained from EnergyPlus with and without the use of the developed on/off controller are also compared. The application of the developed on/off controller improves the HVAC energy use results accuracy around 19% in terms of the Normalized Mean Bias Error (NMBE) at the one-minute level compared to the results without the application of the on/off controller. It also makes the nature of operation and associated energy use signal of the HVAC system more realistic in terms of the on/off nature of the residential direct expansion coils. Furthermore, in this paper, the accuracy improvement of the results dominated by the internal loads is investigated by applying the one-minute schedule compared to the usual hourly schedules for the internal thermal loads. [ABSTRACT FROM AUTHOR]

New smart building technologies that offer continuous dynamic optimization of Heating, Ventilation, and Air Conditioning (HVAC) control hold promise to advance building operations for efficiency and grid response. These technologies use data from the control system to determine the analytically optimal setpoints, and then write back the optimal setpoints into the control system to minimize system energy consumption or costs. There are limited studies documenting field validations of these technologies. This paper presents the results from a long-term field evaluation of a model-predictive HVAC optimization system that installed in four commercial buildings. Energy savings analysis was conducted based on pre/post submetered energy use. Across the cohort of evaluation sites, HVAC savings following the implementation of the optimization system were mixed, ranging from 0–9%. Analysis of site operational data showed that occupant comfort was neither positively nor negatively impacted. Key technology adoption considerations and recommendations are summarized in the paper. The technology performs best when HVAC systems are in good working condition, and can be exercised to achieve the full range of its optimized setpoints–however it may not provide extensive additional savings over cases where best practice sequences of operation and reset strategies are already comprehensively implemented. [ABSTRACT FROM AUTHOR]

This paper provides a review of the literature concerning the impact of temporarily increased cooling setpoint temperature on occupant thermal comfort during demand response (DR) events in commercial, air-conditioned buildings. We address concerns regarding thermal comfort as it relates to zone temperature modifications that may be implemented as a part of DR measures. Increased zone setpoint temperatures during the cooling season can adversely affect building occupants physically and psychologically, and impair their perceived indoor air quality and self-estimated performance. In some cases, however, improved occupant thermal comfort due to warmer zone setpoint temperatures during DR events has also been reported. In order to reduce the negative impacts on building occupants, DR must be implemented with careful control and monitoring. There are significant differences in assumptions made about the way people would respond to a thermal environment between the static heat balance model and the adaptive approach, with the adaptive approach offering a wider range of acceptable indoor temperatures. Therefore, application of the adaptive approach could be one option to improve building energy performance by taking advantage of occupant adaptive behaviors during DR events. Depending on the building services and systems available, expectations of the occupants, and the control options they are given to adjust to their thermal environment, occupants could potentially adapt to temperatures higher than what are currently being practiced in buildings. However, an upper limit threshold for temperature modifications, even if temporary ones, must be recognized to minimize adverse impacts on building occupants prior to DR implementation. Therefore, the ideal goal would be to develop menthods that would identify an optimum balance between energy consumption and the building occupant thermal comfort before applying DR strategies. Prior review papers relevant to DR have generally concentrated on the energy saving potential alone, or where energy savings have been prioritized over occupant thermal comfort. This paper reviews implementation of DR from the perspective of occupant thermal comfort and presents a summary of the most relevant experimental and field studies regarding occupant thermal comfort during DR events in commercial, air-conditioned buildings. [ABSTRACT FROM AUTHOR]

Commercial buildings during operation are dynamic environments where changes to control strategies and space usage regularly occur. As a result of these and other issues, a performance gap between design intent and actual building performance emerges. This paper seeks to address the operational performance gap and enhance operational building performance through statistically-based fault detection. Additionally, this paper seeks to remedy the knowledge gap building managers face in the identification of key building faults based on minimal quantities and streams of time-series building data. A new methodology is presented that incorporates simulation and breakout detection to address these issues. Residual based exponentially weighted moving average (EWMA) charts and Shewhart charts are compared against a breakout detection algorithm to identify shifts or faults in building performance data. Artificial faults are introduced into the measured time-series data to test the validity of the chosen statistical techniques. Statistical metric sensitivity and precision are calculated to quantify the performance of the new methodology. A summary of results demonstrate that the breakout detection algorithm was the most effective method in detecting meaningful faults in building performance data, followed by residual based EWMA and Shewhart models. [ABSTRACT FROM AUTHOR]

As the crucial foundation of distributed energy management systems, a reliable prediction of dynamic energy consumption is urgently needed in the building sector. Advances in metering technology and machine learning methods provide both opportunities and challenges. In particular, a clustering-based hybrid model is established in this paper to predict the day-ahead hourly electric demand in hotel buildings, which were initially classified as the non-stationary operated buildings (NOB) due to the irregular electric temporal features. Specifically, with multi-dimensional attributes of 100 days considered by fuzzy c-means (FCM) clustering, similar days and hours were extracted stepwise into several homogeneous groups. Afterwards, the online modified predictor was established by the combination of support vector regression (SVR) and wavelet decomposition, taking the corresponding training samples extracted by FCM as the input. Clustered results of similar days testified the clear identification of stationary subsequences (Group IV) and non-stationary subsequences (Group I). Based on the validation, it can be concluded that the introduction of days-clustering can always improve the accuracy of day-ahead prediction, while the recognition of similar hours is necessary only for non-stationary subsequences. Finally, this paper offers an online short-term prediction approach for model-based energy management, based on the limited and fluctuating historical records. [ABSTRACT FROM AUTHOR]

As buildings become more energy efficient and automated, the role of occupants becomes more significant to assist buildings in reaching their full energy efficiency potential. Predicting occupants’ consumption behavior has been identified as one of the most challenging processes in energy efficiency programs and building management system operations. Thus, more accurate behavior learning algorithms for commercial buildings are needed to improve energy efficiency. In this paper, we propose a method to predict occupants’ energy-use behavior based on individual energy consumption profiles and assess its potential to increase the effectiveness of energy efficiency programs. The proposed method implements a support vector machine in order to model and predict occupants’ short term energy-use patterns and test hypotheses for the existence of a correlation between occupants’ entropy, efficiency and prediction accuracy. The results show an average accuracy of 83% for individual energy-use pattern prediction while being positively correlated with individuals’ energy-use behavior. The main contributions of this paper are: (1) proposing and validating a new method to predict individuals’ energy-use patterns based on their individual workstation-level energy consumption patterns, and (2) assessing the feasibility and potential of implementing this method to enhance the efficacy of energy efficiency programs to further induce energy efficiency. [ABSTRACT FROM AUTHOR]

This paper describes a step-by-step procedure for using measured end-use energy data from a campus building to calibrate a simulation model developed in EnergyPlus. This process included identification of key input parameters for reducing uncertainties in the model. Building thermal zones were modeled to match the actual heating ventilation and air conditioning (HVAC) zoning for each individual variable air-volume (VAV) zone. We evaluated most key building and HVAC system components, including space loads (actual occupancy number, lighting and plug loads), HVAC air-side components (VAV terminals, supply and return fans) and water-side components (chillers, pumps, and cooling towers). Comparison of the pre- and post-calibration model shows that the calibration process greatly improves the model's accuracy for each end use. We propose an automated model calibration procedure that links the model to a real-time data monitoring system, allowing the model to be updated any time. The approach enables the automated data feed from simple measuring and actuation profile (sMAP) into the EnergyPlus model to create realistic schedules of space loads (occupancy, lighting and plug), performance curves of fans, chillers and cooling towers. We also field-tested demand response (DR) control strategies to evaluate the model's performance in predicting dynamic response effects. Finally, this paper describes application of the calibrated model to analyze control systems and DR strategies with the goal of reducing peak demand. [ABSTRACT FROM AUTHOR]

[Display omitted] • nZEBs in Eastern Europe lack carbon neutrality metrics and targets. • Performance requirements for nZEBs in Eastern Europe are presented. • Eastern Europe needs strategy and local governance for nZEB market penetration. • EPCs require major reform, and effective monitoring of nZEB performance is needed. • The cost-optimality calculation method must be adopted to ensure cost-effective market uptake. The European Unions' ambition for the construction sector is to be carbon neutral by 2030 for new construction. Since 2021, all new buildings in the EU should have been constructed as nearly zero-energy buildings (nZEB). However, Eastern European countries struggle to implement the 2018 Energy Performance of Building Directive recast requirements. Next to the economic challenges, equally essential factors hinder renovating the existing residential building stock and adding newly constructed high-performance buildings sourced primarily from renewable energy sources. Therefore, this study provides a cross-study to identify the barriers to nZEB implementation in ten Eastern European countries, including Bulgaria, Croatia, Czechia, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, and Slovakia. The study was conducted between 2019 and 2021 and provides an overview of prospects for nZEB in Eastern Europe. The study examines the challenges of nZEB plans faced in those countries and provides constructive recommendations. The regulations and definitions regarding nZEB energy performance, cooling and heating energy demand, thermal comfort, onsite renewables, and construction quality were analyzed. Results show that most Eastern European countries are unprepared to comply with the EPBD guidelines and cost-optimality approach. The paper ranks each country and recommends specific measures to refine the nZEB definitions. The paper provides a thorough comparative assessment and benchmarking of select EU geography that can help shift the identified gaps into opportunities for the future development of climate-neutral high-performance buildings. [ABSTRACT FROM AUTHOR]

Abstract: Most air handling units (AHUs) in commercial buildings have an air economizer cycle for free cooling under certain outside air conditions. During the economizer cycle, the outside air and return air dampers are modulated to seek supply air temperature at its setpoint. The supply air temperature is typically set at 55F (13°C) to control humidity in the space. However, dehumidification is not necessary when the outside air is dry. Meanwhile, the space may have less cooling load due to envelope heat loss and/or occupant schedule changes. These facts provide an opportunity to use higher supply air temperature to reduce or eliminate mechanical cooling and terminal box reheat. On the contrary, a higher supply air temperature requires increased air flow as well as fan power. Therefore, an optimization question is formed, through which an optimal supply air temperature is identified to minimize total energy consumption. In this paper a steady-state energy consumption model is established for AHU systems under the economizer, and then an analytical optimization method is used to seek an optimal supply air temperature setpoint to minimize the energy cost. This paper presents AHU system energy modeling, supply air temperature optimization, simulated energy savings, and control sequence development. [Copyright &y& Elsevier]

The energy demand for supplying Domestic Hot Water (DHW) has an important share in the overall thermal energy consumption of commercial buildings. The aim of this paper is to analyze the performance of DHW production in commercial public service, i.e., educational buildings, to subsequently identify frequent inherent shortcomings of centralised DHW installations and to provide measures for optimization or economically more viable solutions. Several buildings of the University of Natural Resources and Life Sciences in Vienna have been investigated in terms of DHW consumption and performance of the installed centralised systems. Referring to the investigated university buildings, it can be stated, that in cases of low to medium consumption figures, typical centralized DHW installations with a hot water circulation pipework achieve only poor efficiencies in the range of 2–12%. As for one particular centralized DHW system in operation, focusing on demand controlled DHW supply through adapted flow control, e.g., reducing the runtime of the circulating pumps at specific time intervals, and, if applicable and feasible, decreasing hot water flow- and storage capacities can reduce annual energy consumption for DHW by 15–25% and improve the overall system efficiency significantly as could be evaluated in a detailed case study. A seemingly economical yet ecologically controversial option for improving centralised DHW systems is the partial or complete conversion to electric point-of-use water heaters. This reduces thermal energy losses almost completely, hereby significantly increasing the efficiency of a DHW supply system. Nevertheless, existing hot water supply systems, energized by district heating, usually consume less primary energy and most likely generate fewer CO 2 emissions compared to electric DHW heaters. [ABSTRACT FROM AUTHOR]

Abstract This paper addresses the question of identifying the time-window in short-term past from which the information regarding the future occupant's window opening actions and resulting window states in buildings can be predicted. The addressed sequence duration was in the range between 30 and 240 time-steps of indoor climate data, where the applied temporal discretization was one minute. For that purpose, a deep neural network is trained to predict the window states, where the input sequence duration is handled as an additional hyperparameter. Eventually, the relationship between the prediction accuracy and the time lag of the predicted window state in future is analyzed. The results pointed out, that the optimal predictive performance was achieved for the case where 60 time-steps of the indoor climate data were used as input. Additionally, the results showed that very long sequences (120–240 time-steps) could be addressed efficiently, given the right hyperprameters. Hence, the use of the memory over previous hours of high resolution indoor climate data did not improve the predictive performance, when compared to the case where 30/60 min indoor sequences were used. The analysis of the prediction accuracy in the form of F1 score for the different time lag of future window states dropped from 0.51 to 0.27, when shifting the prediction target from 10 to 60 min in future. [ABSTRACT FROM AUTHOR]

Highlights • The energy performance of all variable-speed packaged units needs to be evaluated. • Field test and simulation are combined in the performance evaluation. • The evaluated unit has > 30% higher cooling efficiency than most units in the field. • All variable-speed units can save 22%−29% electricity than the code requirement. Abstract Packaged air-conditioning units are widely used in commercial buildings. Most packaged units in the field are equipped with constant-speed supply fans and constant-speed compressors. Many efforts have been made to increase the operational efficiency of packaged air conditioners by retrofitting existing units with improved controls or developing innovative products with advanced designs. This paper evaluates the energy performance of a packaged unit with all variable-speed components, including the supply fan, the compressor, and the condenser fan. Both a field test and energy simulation are used to quantify the potential of energy savings from the variable-speed unit (VSU) against reference units. In the field test, the reference is a six-year-old existing unit. It was determined through monitoring for one year that the VSU had a 31% higher cooling efficiency than the reference unit. In the simulation, a retail building model was used to compare the performance between the VSU and two reference units—one representing existing units in the field and the other representing units meeting the latest energy code requirements by ASHRAE Standard 90.1. Simulation results showed that depending on the climate, the VSU can save electricity consumption (including both cooling and fan) by 45%–54% relative to the first reference and by 22%–29% relative to the second reference. These findings are valuable for building designers and contractors in selecting packaged rooftop units for both new constructions and retrofits. [ABSTRACT FROM AUTHOR]

Abstract Convenience stores are known to have higher Energy Use Intensities (EUIs) than most other commercial building types. However, convenience store energy performance analyses have not been systematically investigated. Moreover, current national convenience store benchmarks are inconsistent from one data base to another due to lack of relevant energy data. This paper assesses the energy performance of more than five hundred convenience stores in the same fleet in Eastern United States. The investigation aims to establish benchmarks for convenience stores with and without gasoline pumping stations. The main factors influencing the actual convenience store building energy use were determined. Multiple regression models were developed to find relationships between annual energy consumption per unit area and gross floor area, customer transaction count, building operating year, etc. The statistically characterized results are meant to serve as a reference data set in comparing the whole-building energy performance index for buildings of similar functionality. The statistical correlation analyses can strengthen the robust prediction of energy performance in convenience stores, enabling proper assessment of energy conservation measurements (ECMs) for convenience store operations, as well as guiding additional such energy performance studies. [ABSTRACT FROM AUTHOR]

Highlights • A multi-level building energy consumption benchmarking index system for cooling in large commercial buildings is presented. • The recommended index value is calculated based on real operation data of a commercial building in China. • Evaluation efficiency is improved by using a simplified calculation method. • The benchmarking method was validated using real data from eight large shopping centers in China. Abstract Urban development rapidly increases the total area and the energy consumption of large commercial buildings. Accordingly, the total energy consumption is the focus of numerous energy saving studies. An optimal benchmarking system provides a recommended energy consumption level for each building as well as identifying and prioritizing challenges to energy saving in each building. Based on detailed sub-metering system data and building operational data, this paper presents a simplified energy consumption benchmarking method for air-conditioning systems that cool large commercial buildings. Firstly, a multi-level benchmarking index system is established. Next, the energy performance data of eight large shopping centers in China validates the simplified benchmarking method and finally, the energy performance data of the participating shopping centers is analyzed under the present method. Our data focus on the energy saving potential, as well as informing improvement strategies for building energy performance, that can be used for efficient energy benchmarking process for cooling in large-scale commercial buildings. [ABSTRACT FROM AUTHOR]

• A mathematical model to calculate the LCOS for reversible fuel cells and Li-ion batteries. • Techno-economic model for a standalone PV solar system in green energy buildings. • Uncertainty and simulation cost analyses of energy storage systems used in green buildings. Energy storage systems (ESSs) were introduced to overcome the risks posed by energy curtailment. In this paper, we compare the Levelized cost of storage (LCOS) for PEM Reversible Fuel Cells, Solid Oxide Fuel Cells, and Lithium-Ion Batteries in three different locations in the United States (Tucson, Seattle, and Rochester) that are characterized by different energy consumption profiles. This research examines three commercial buildings of different sizes: small, medium, and large, with three occupancy profiles: low, medium, and high energy consumption in each of the three locations. The objective is to quantify the impact of uncertain demand and uncertain financial parameters on the LCOS. The results show that all ESSs can represent an attractive alternative to energy curtailment regardless of the building size or the occupancy profile. Further, the results show that fuel cells are a better fit for demand uncertainty as they are more resilient to changes and remain attractive despite that. The uncertainty analysis showed that the minimum LCOS was $0.049/kWh, and it was for a PEM-RFC in a small building in Tucson under high energy consumption behavior. Oppositely, the maximum LCOS was $0.679/kWh, and that was for a large building in Seattle under high energy usage occupancy as well. [ABSTRACT FROM AUTHOR]

Power disaggregation algorithms are used to decompose building level power consumption data into individual equipment level power information. In order to ensure energy efficient operation of complex systems, such as commercial buildings, a continuous and detailed energy monitoring system is essential. In this paper a novel power disaggregation technique is presented, in which a single set of aggregated power usage data of multiple HVACs from a single power meter is disaggregated to identify the operations of individual HVAC units. Parameters, including the combined real and reactive power of compressors and air handlers, are used in addition to the phase currents of both, as well as the true index values representing the combination of active compressors at any given time. Four different supervised machine learning algorithms – Decision Trees (DT), Discriminant Analysis (DA), Support Vector Machine (SVM) and k -Nearest Neighbors ( k -NN) were tested. According to results, the k -Nearest Neighbors model was found to be most efficient in solving the problem of aggregated power disaggregation. [ABSTRACT FROM AUTHOR]

Occupants’ energy-consuming behaviors have a significant influence on overall energy consumption in commercial buildings. Accordingly, understanding and intervening in these behaviors offers a significant opportunity for energy savings in commercial buildings. Current approaches to behavior modification rely on available occupant-specific energy consumption data, but capturing such data is generally expensive. One possible solution to this challenge is to link energy consumption to individual occupants’ energy-use behaviors in commercial buildings. In this context, this study proposes a non-intrusive occupant load monitoring (NIOLM) approach that couples occupancy-sensing data—captured from existing Wi-Fi infrastructures—with power changes in aggregate building-wide energy data to thereby disaggregate building-wide data down to the individual. This paper describes two case studies that investigate the feasibility of using the NIOLM approach to identify occupant-specific energy consumption information. Tracking eleven occupants’ energy-use behaviors using NIOLM over a four-month period resulted in an average F-measure of 0.778 and Accuracy of 0.955. The case studies thereby demonstrated that NIOLM successfully tracks individual occupants’ energy-consuming behaviors at minimal cost by utilizing existing high-resolution metering devices and Wi-Fi network infrastructures in commercial buildings. [ABSTRACT FROM AUTHOR]

Packaged air-conditioning systems, such as rooftop units (RTUs), are widely used in small commercial buildings in the United States. Although new regulations and standards require new air-conditioning equipment to have multi-speed supply fan operation, most of the installed units employ fixed-speed supply fans. The electrical energy usage associated with constant volumetric air delivery fan systems is on the order of half the total RTU electrical energy usage because supply fans operate continuously during the occupied periods in commercial buildings for ventilation. In addition, compressors within RTUs typically feature single- or two-stage capacity controls leading to frequent unit cycling and thus, deteriorated performance in energy efficiency. This paper presents relatively comprehensive assessment results for three RTU variable-speed retrofit options: 1) retrofits of fixed-speed supply fans with variable-speed fans for single-compressor-speed units, 2) replacement of existing fixed-speed RTUs with RTUs having two-speed compressors with variable-speed supply fans and 3) retrofits of RTUs with new RTUs that incorporate variable-speed compressors and fans (indoor and outdoor). The assessment study involves parametric simulations across different building types and locations in the U.S. to understand the performance variation with respect to location and building use. Performance improvements in terms of energy efficiency, operation cost and indoor comfort delivery for the various retrofit options are demonstrated compared to conventional single-stage RTUs with constant speed supply fans. The assessment results show that all three retrofit options offer significant fan energy savings, but much smaller savings potential for compressor and condenser fan electricity consumption. The variable-speed-unit retrofit provides the highest cost savings potentials, ranging from 40 to 250 $ per rated cooling ton per year depending on the building type and location. In addition to efficiency and economic benefits, the two-stage-unit and variable-speed-unit retrofits also lead to improved indoor humidity regulation performance due to reduced unit cycling. [ABSTRACT FROM AUTHOR]

Real-time occupancy predictions are essential components for the smart buildings in the imminent future. The occupancy information, such as the presence states and the occupants’ number, allows a robust control of the indoor environment to enhance the building energy performances. With many current studies focusing on the commercial building occupancy, most researchers modeled either the occupancy presence or the occupants’ number without evaluating the model potentials on both of them. This study focuses on 1) providing a unique data set containing the occupancy for the offices located in the U.S with difference pattern varieties, 2) proposing two methods, then comparing them with four existing methods, and 3) both presence of occupancy and occupancy number are predicted and tested using the approaches proposed in this study. In detail, the paper develops a new moving-window inhomogeneous Markov model based on change point analysis. A hierarchical probability sampling model is modified based on existed models. They are additional compared to well-known models from previous researchers. The study further explores and evaluates the predictive power of the models by various temporal scenarios, including 15-min ahead, 30-min ahead, 1-h ahead, and 24-h ahead forecasts. The final results show that the proposed Markov model outperforms the other methods with a max 22% difference in terms of presence forecasts for 15-min, 30 min and 1-h ahead. The proposed Markov model also outperforms other models in occupancy number prediction for all forecast windows with 0.34 RMSE and 0.23 MAE error respectively. However, there is not much performance difference between models for 24-h ahead predictions of occupancy presence forecast. [ABSTRACT FROM AUTHOR]

Australia’s commercial building stock exceeds 134 million m 2 of net lettable area, with retail buildings contributing 35% to this sector’s energy use. The energy intensity of retail buildings in hotter climates is higher than the national average, as is the energy intensity of smaller buildings (under 1500 m 2 ) that are not considered ‘high-end’ commercial properties. Little attention has been paid to improving the energy efficiency of these types of buildings through regulation (for new buildings) or through market mechanisms (for retrofitting). As many of these buildings are single storey ‘warehouse’ type buildings, their predominant heat load comes through the roof, and thus are well suited to benefit from cool roof technology. Despite this, there remains a deficiency in quantifying the benefit of such technology in the context of single-storey retail buildings in Australia. This paper reports on an experimentally validated numerical study aimed at addressing this deficiency. Results show that application of cool roof technology to a warehouse type building in a subtropical environment increases the energy efficiency by shifting space temperature towards the design set point (21–23 °C), and thus reducing cooling energy demand. This study also indicates an energy saving every month with the application of cool roof, with the largest saving in hotter months and no heating penalty in cooler months. Application of cool roof technology on warehouse style buildings across Australian buildings indicates energy savings can be achieved in all broad Australian climatic zones, with the greatest energy reduction associated with tropical, subtropical and desert environments. [ABSTRACT FROM AUTHOR]

This paper evaluates the potential heating and cooling energy use and cost savings associated with improved controls of dynamic insulation materials (DIMs) when used in multilayered wall assemblies for commercial office buildings. Using a genetic algorithm (GA) based optimization technique, optimum R-value settings are identified for the DIMs throughout the year. These optimal R-value settings are selected to maximize energy cost savings when compared to traditional static insulated wall systems. It is found that optimal control settings for DIMs depend on several factors including building design and operation strategies as well as climatic conditions. Moreover, the optimal settings were found to depend on the orientation of exterior walls and well as the climatic conditions. Specifically, the analysis results indicate that north-facing walls are more active and frequently have to change their R-value in order to minimize the office building energy use and cost. When optimally controlled, DIMs could save up to 17% of annual heating and cooling energy costs for US office buildings. [ABSTRACT FROM AUTHOR]

Thermal stratification in large warehouses is a common phenomenon in winter, especially for cold climates. Buoyancy-driven warm air tends to move upwards towards ceilings creating vertical temperature gradients with upper higher temperatures, and consequently leading to excessive heat losses through roofs. These warehouses therefore require thermal destratification through certain air mixing strategies, the selection of which often relies on engineering rules of thumb and experiences but can be better determined through combined experimental and numerical approaches. This paper investigated different thermal destratification methods in two industrial warehouses based on field measurements and CFD simulations with the focus on the quantification of thermal stratification, and the suitable methods modeling air mixing devices, e.g. ceiling rotating fans and bucket fans. The numerical models were first validated by field measurements and then used to simulate different destratification strategies and to compare associated energy impacts. The CFD methods for modeling air mixing devices and their performances can be applied to other similar cases. [ABSTRACT FROM AUTHOR]

This paper presents a modeling framework that uses machine learning algorithms to make long-term, i.e. one year-ahead predictions, of fuel consumption in multiple types of commercial prototype buildings at one-hour resolutions. Weather and schedule variables were used as model inputs, and the hourly fuel consumption simulated with EnergyPlus provided target values. The data was partitioned on a monthly basis, and a feature selection method was incorporated as part of the model to select the best subset of input variables for a given month. Neural networks (NN) and Gaussian process (GP) regression were shown to perform better than multivariate linear regression and ridge regression, and as such, were included as part of the model. The modeling framework was applied to make predictions about fuel consumption in a small office, supermarket, and restaurant in multiple climate zone. It was shown that for all climate zones for all months, the maximum errors pertaining to one year-ahead forecasts of fuel consumption made by the ML model are 15.7 MJ (14,880 Btu), 284.3 MJ (268,516 Btu) and 74.0 MJ (70,138 Btu) respectively. The methods and results from this study can be used to estimate on-site fuel consumption and emissions from buildings, thereby enabling improved decisions pertaining to building efficiency with respect to fuel use. [ABSTRACT FROM AUTHOR]

Ancillary services are required to balance supply and demand in electric power systems. Demand response may provide attractive options for these services, through means such as varying the power consumption of commercial building HVAC systems. However, experimental results from a 30,000 m 2 office building suggest that when a building provides ancillary services it consumes more energy than when it is operated normally. This translates to additional costs and environmental impacts. This paper investigates potential causes for building inefficiency associated with ancillary services provision. We develop a physics-based simulation model that captures heat exchange processes and fan and air duct dynamics. During an ancillary service event, we vary the fan power consumption, and then compute the difference between the baseline and actual energy consumption to determine the efficiency of the actions. We explore the impact of building parameters, control design, and baseline model accuracy on the efficiency. In simulation, we find that shorter duration power changes and less aggressive controllers result in less change in energy consumption. We also find that baseline error has outsized effects on the efficiency calculations. These results offer new understanding of the mechanisms underlying inefficiency and point to opportunities to reduce associated costs and environmental burdens. [ABSTRACT FROM AUTHOR]

Occupants affect energy consumption in buildings by contributing internal heat gains, increasing internal carbon dioxide levels and adapting their behaviour. Estimated occupancy schedules are used in building energy models for regulatory compliance purposes and when empirical data are not available. Metadata, such as personal location data, is now collected and visualised on a global scale and can be used to create more realistic occupancy schedules for non-domestic facilities, such as large retail outlets. This paper describes a protocol for extracting and using freely available metadata to create occupancy schedules that are used as inputs for dynamic simulation models. A sample set of twenty supermarket building models are used to demonstrate the impact metadata schedules have when compared with models using the estimated schedules from regulatory compliance. Metadata can be used to create bespoke occupancy profiles for specific buildings, groups of buildings and building archetypes. This method could also help reduce the gap between predicted and actual performance. In the example models, those using the regulatory compliance schedules underestimated heating demand by approximately 10% and overestimated cooling demand by over 50% when compared to models using the metadata schedules. Although this work focuses on UK facilities, this methodology has scope for global application. [ABSTRACT FROM AUTHOR]

School buildings have a great potential for carbon emission reduction since their annual emission is about 72 million metric tons. Currently, more than 30 % of school buildings were built before 1960 and are underperforming. To effectively reduce carbon emissions via school building retrofits, it is critical for policymakers to understand the carbon intensity reduction potential of retrofitting school buildings in different regions. Hence, this study develops a method to comprehensively assess the long-term carbon intensity reduction potential of aggregated commercial buildings on a county-by-county basis in the continental U.S. We apply this method to the K-12 school buildings including primary and secondary school buildings. This paper predicts the carbon intensity reduction potential of K-12 school buildings with eight building retrofit measures from 2022 to 2050 in the continental U.S. The results reveal several interesting findings: (1) In the approximately 3,000 counties of the U.S. from 2022 to 2050, the carbon intensity reduction potential of retrofitting K-12 school buildings in each county ranges from 0.41 kg/m2 to 40.00 kg/m2. (2) Even in the same climate zone, the trends of carbon intensity reduction potential from 2022 to 2050 are different depending on their electricity sources. For example, in a hot and humid climate zone, the carbon intensity reduction potential in Florida will decrease from 2044 to 2048. However, in Mississippi, the carbon intensity reduction potential from 2044 to 2046 will increase due to the termination of the nuclear energy usage. (3) Generally, reducing lighting power density leads to more carbon intensity reduction in most states, but it might not be applicable for states with high clean energy penetration, such as Washington. [ABSTRACT FROM AUTHOR]

• Freely available online overview of district heating efficiency indicators. • Hourly district heating energy use data from approx. 43 000 buildings. • Building stock segmented into building typologies for analysis of: • -1) Heating energy use intensity, 2) load variation and 3) cooling efficiency This paper provides a state-of-the-art and freely available online overview of the district heating (DH) efficiency of Danish building typologies. The overview is based on DH consumption data from 42 969 buildings constructed up until 2017 that in combination with data from the Danish Building and Dwelling Register constitutes a dataset with a previously unseen level of detail, confidence, sample size, and research opportunities. The paper also presents an initial analysis of the dataset in terms of three heating efficiency indicators, namely 1) Annual heating energy use intensity, 2) Daily heat load variation, and 3) Cooling efficiency. The overall finding was that building typologies perform significantly different from one another concerning indicators 1 and 2, but not for indicator 3. It is therefore not possible to declare one building typology more efficient than the other as this depends on the building construction year and the considered efficiency indicator. The data indicate that further energy-efficiency improvements of the existing building stock are essential to the realization of 4th Generation District Heating technologies in Denmark and that these improvements most likely will lead to increased heat load variations that need to be addressed, for instance by enabling a larger degree of demand-side flexibility in the existing district heating systems. The large temporal variation of the buildings' cooling efficiency indicates that there is a potential for rethinking e.g. domestic hot water production in the summer or introduce differentiated billing based on the daily, weekly, or maybe seasonal cooling performance. The presented dataset can be subject to several future investigations which are also discussed in the paper. [ABSTRACT FROM AUTHOR]

To implement an energy saving measure, companies must first decide to investigate the options available. Representative survey data shows that almost half of companies in the Irish commercial sector do not take this step. This paper explores the barriers and drivers of this. Two logit models are fit to data to estimate the influence of variables, representing company and building characteristics, on the likelihoods of a company investigating either a fabric upgrade or a behaviour change measure. Companies are more likely to investigate a fabric upgrade that: own the building they operate from, make energy related decisions locally, have more than 10 employees, have had a recent renovation, accept longer paybacks, and apply a case by case approach to budget decisions. Hotels and offices were found to have a higher likelihood of investigating fabric options. Lack of knowledge of building floor area reduced the likelihood of investigation of both fabric upgrade and behavioural options. Much of the previous research is concerned with the final adoption of measures; this analysis adds additional insights by identifying the factors that determine if a company is likely to investigate the options available. [ABSTRACT FROM AUTHOR]

Forecasting energy consumption in buildings is a key step towards the realization of optimized energy production, distribution and consumption. This paper presents a data driven approach for analysis and forecast of aggregate space and water thermal load in buildings. The analysis and the forecast models are built using district heating data unobtrusively collected from 10 residential and commercial buildings located in Skellefteå, Sweden. The load forecast models are generated using supervised machine learning techniques, namely, support vector machine, regression tree, feed forward neural network, and multiple linear regression. The model takes the outdoor temperature, historical values of heat load, time factor variables and physical parameters of district heating substations as its input. A performance comparison among the machine learning methods and identification of the importance of models input variables is carried out. The models are evaluated with varying forecast horizons of every hour from 1 up to 48 h. Our results show that support vector machine, feed forward neural network and multiple linear regression are more suitable machine learning methods with lower performance errors than the regression tree. Support vector machine has the least normalized root mean square error of 0.07 for a forecast horizon of 24 h. [ABSTRACT FROM AUTHOR]

This paper exploits a whole-building energy simulation approach to develop and evaluate demand response strategies for commercial buildings. The research is motivated by the increasing penetration of renewable energy sources such as wind and solar, which owing to their stochastic nature, means that enhanced integration of demand response measures in buildings is becoming more challenging and complex. Using EnergyPlus, a simulation model of a multi-purpose commercial building was developed and calibrated. Demand response strategies are evaluated for a number of building zones, which utilise different heating, cooling and ventilation equipment. The results show that for events of varying demand response durations, different strategies should be selected for each zone based on their thermal and usage profiles. Overall, a maximum reduction of 14.7% in electrical power demand was recorded when targeting a centralised chiller load, with smaller reductions for other decentralised building loads. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present a powerful simulation-based multi-objective optimization of building energy efficiency and indoor thermal comfort to obtain the optimal solutions of the comfort-energy efficient configurations of building envelope. The optimization method is developed by integrating a multi-objective artificial bee colony (MOABC) optimization algorithm implemented in MATLAB with EnergyPlus building energy simulation tool. The proposed optimization approach is applied to a single office room; and the building parameters, including the room rotation, window size, cooling and heating setpoint temperatures, glazing and wall material properties are considered as decision variables. In the present study, single-objective and multi-objective optimization analyses of the total annual building electricity consumption and the Predicted Percentage of Dissatisfied (PPD) are investigated to bring down the total energy cost as well as the thermal discomfort in four major climate regions of Iran, i.e. temperate, warm-dry, warm-humid and cold ones. In the results part, the achieved optimal solutions are presented in the form of Pareto fronts to reveal the mutual impacts of variables on the building energy consumption and the thermal discomfort. Finally, the ultimate optimum solution on the Pareto fronts are selected by TOPSIS decision-making method and the results of double-objective minimization problem are compared with the single-objective ones as well as the base design. The results of double-objective optimization problem indicate that in different climates, even though the total building electricity consumption increases a bit about 2.9–11.3%, the PPD significantly decreases about 49.1–56.8% compared to the baseline model. In addition, the comparisons of single-objective and double-objective optimization approaches clearly show that multi-objective optimization methods yield more appropriate results respect to the single ones, mainly because of the lower deviation index value from the ideal solution. [ABSTRACT FROM AUTHOR]

Retrofitting existing buildings has emerged as a primary strategy for reducing energy use and carbon emissions, both nationally and in cities. Despite the increasing awareness of retrofitting opportunities and a growing portfolio of successful case studies, little is known about the decision-making processes of building owners and asset managers with respect to energy efficiency investments. Specifically, the research presented here examines the effects of ownership type, tenant demand, and real estate market location on building energy retrofit decisions in the commercial office sector. This paper uses an original, detailed survey of asset managers of 763 office buildings in nineteen cities sampled from the CBRE, Inc. portfolio. Controlling for various building characteristics, the results demonstrate that ownership type and local market do, in fact, influence the retrofit decision. Overall, this analysis provides new evidence for the importance of understanding ownership type and the varying motivations of differing types of owners in building energy efficiency investment decisions. The findings of both the survey analysis and the predictive model demonstrate additional support for the targeting of energy efficiency incentives and outreach based on ownership entity, local market conditions, and specific physical building characteristics. [ABSTRACT FROM AUTHOR]

Climate control systems typically account for a substantial part of energy consumption in commercial buildings. The obligations established in global agreements as well as regulations and legislation that limit the energy consumption and greenhouse gases emissions gave a novel importance to the HVAC systems rating. This paper describes various indicators for assessing the energy performance of HVAC systems by defining the concepts involved and presenting calculation procedures. Various methods are used, from the basic indicators ( EER , COP ) to the most sophisticated ( SEER , SCOP ), which are due to the need to include more parameters to determine the level of energy efficiency accurately and to adjust it to the actual operation of the equipment. A case study is presented in order to calculate the indicators of actual systems in Europe and to assess developments by geographical location. A difference between the results of the various indicators when changing the geographical location and weather conditions that affect the systems was noticeable, which can lead to misconceptions regarding the energy efficiency. It is therefore important to correctly use the indicators that best fit the location, a task that is not usually performed by the manufacturers. [ABSTRACT FROM AUTHOR]

Accurate, computationally efficient, and cost-effective energy forecasting models are essential for model based control. Existing studies in model based control have mostly been focusing on developing energy forecasting models using simplified physics based or data driven models. However, creating and identification the simplified physics model are often challenging, which requires expert knowledge for model simplification and significant engineering efforts for model training. In addition, the accuracy and robustness of data driven models are always bounded by the training data. To this end, developing high fidelity energy forecasting models with less engineering effort and good performance is still an urgent task. Although the previous studies from the authors have shown great promises in a system identification model and outperformed other data-driven and grey box models, they still have large errors at the special operation situations. Therefore, this paper investigates a novel methodology to develop energy estimation models for on-line building control and optimization using an integrated system identification and data fusion approach. The data fusion approach is able to adapt the forecasting model under the special operation situations based on the real measurements. An eigensystem realization algorithm based model reformation method is developed to convert the system identification models into state space models. Kalman filter based data fusion techniques are then implemented on the state space models to improve the model accuracy and robustness. The developed methodology are evaluated using data from a virtual building (simulated) and a real small size commercial building. Three different data fusion intervals: 15, 30, and 60 min, have been tested. The overall building energy estimation accuracy from this proposed methodology can reach to above 95% in the virtual building and around 90% in the real building. The results also show that the shorter data fusion interval used, the higher accuracy can be achieved. [ABSTRACT FROM AUTHOR]

Building energy consumption used for internal heating and cooling purposes is one of the most viral research topics. Retrofitting and renovation activities in building applications aim towards utilizing modern construction materials, with improved thermal and insulation characteristics. It is more than evident that such an approach leads to an improved thermal shield for the building (improving passive building elements). In addition well calibrated rule based control designs are also being adopted in the last decades as a way to improve the energy efficiency in buildings (improving active elements management). Both of the above approaches though are considered as time static since disturbances with high uncertainty (weather conditions, human presence and activity) along with the unavoidable construction material aging phenomena affect building behavior and HVAC dynamics. As a result control recalibration activities seem more than necessary to maintain energy efficiency. Followed by the rapid evolution in the computing machines sector and simulation software kits, research effort has been focused on model-assisted and co-simulation based control strategies which utilize the available computational power of modern machines towards improving energy efficiency and comfort levels through appropriate design Building Optimization and Control (BOC) systems, utilizing available system models. However the main drawback in model-assisted strategies is the fact that they heavily rely on the available building model which requires a tedious offline pre-application period including many simulation tests and/or field experiments so as to fine tune and tailor manually the model and consequently the control logic implemented. Moreover, no matter how elaborate the building model is, aging characteristics and uncertain disturbances are factors which call for re-designing (periodically) the available simulation model and the respective control strategies. This paper considers an alternative approach to BOC system design. The main attribute of the proposed methodology is that it can provide automated fine-tuning of the BOC system: no human intervention or a simulation model are required for the initial deployment of the controller as well as for the continuously applied fine-tuning procedure. Real-life experiments performed in a highly energy demanding building in Tel Aviv Israel, during spring time, demonstrate that the proposed approach can effectively provide intelligent decisions that none of the currently employed rule/event-based strategy can replicate. [ABSTRACT FROM AUTHOR]

Daylighting is perceived to be a potential measure for energy conservation in commercial buildings. In Thailand where the sky is highly luminous and daytime is long throughout the year, daylighting is however neglected by the use of heat reflective glasses to prevent excessive solar gain from windows. To address the issue, this paper investigates the energy performance of the daylighting from a window with horizontal slats located between two panes of glasses (slat window). The slats can shade the intense beam radiation, but allow penetration of the diffuse daylight for interior illumination. In the study, a model was developed to determine the thermal gain from the slat window. By using the thermal model together with the daylight model developed earlier, the simulations were performed and the results show that the slat window, when used with typical office room, can save 25–70% of the total electrical energy of air-conditioning and lighting in the same room equipped with heat reflective glass window. [ABSTRACT FROM AUTHOR]

• POE was carried out to compare the design target and operational effect after use. • A Large Irregular Green Commercial Building was chosen for in-depth analysis. • We recommend the building energy saving and carbon emission reduction strategy. Commercial buildings, especially large irregular commercial buildings with complex space and dense occupation, have high energy consumption and carbon emissions. Moreover, the actual operating performance differs significantly from its design, in terms of energy conservation and carbon emissions. This study selected an irregular large-scale green commercial building in Dalian, a coastal city in the cold zone of China. The running energy consumption and indoor physical environment were investigated and analyzed, and a satisfaction survey was conducted using the Post-occupancy evaluation (POE) methods. The promotion strategies of holistic design were proposed by comparing the operational building performance with the design target. The research shows that the energy consumption of the building was 68.02 kWh/m2, which failed to meet the standard requirements. The carbon emission was 74.76 kgCO 2 /m2. Less than 10 % of humidity in summer fell in the expected range, and the lowest value of PM 2.5 concentration in winter was 28.3 %. The score of indoor environmental quality and service satisfaction was low. This paper identifies the problems and analyzes the differences between the operation stage and design phase. It provides recommendations and guidelines for optimizing the carbon emission and indoor environment based on the performance improvement of building operational conditions. [ABSTRACT FROM AUTHOR]

Many utility companies have installed Smart Meters (SMs) for residential and commercial buildings in the U.S., which are the part of the Smart Grid (SG) that integrates the electricity grid with communication networks. Along with the growing interest in SMs, the development of the wireless technologies and smart phones has accelerated the applications of Home Automation Devices (HADs) that can also communicate with SMs, Home Energy Management Systems (HEMS), and smart phones. However, there are few if any previous studies that analyze the potential energy saving opportunities for homeowners from HADs using interval data recorded by SMs. Therefore, this paper presents five new pre-screening analysis methods that use interval energy consumption data to better characterize building energy use for the residential customers who want energy savings from the use of HADs before they are installed. This paper is part of a larger study that analyzed and measured energy savings from the use of HADs with smart meter data. [ABSTRACT FROM AUTHOR]

Central air conditioning accounts for more than 40 % of the energy consumption of social buildings. Ensuring the efficient operation of central air conditioning at full working conditions has a positive impact on reducing the total energy consumption of society. In this paper, the historical operation data of central air conditioning in a commercial building in Shenzhen is used as the source and the energy-saving strategy of this central air conditioning system is studied using 2019 data as the main research data. Firstly, the measured parameters are extracted as the Cooling factor, Delivery factor, and Load factor based on factor analysis, and the K-Means algorithm is used as input parameters for pattern recognition of the central air conditioning system. Then, according to the results of pattern recognition, C5.0 and the CHAID decision tree algorithm are used to obtain the energy-saving strategy model. Finally, the 2020 operational data of the building's central air conditioning system was used as the validation data set for energy efficiency verification. The energy-saving strategy based on the C5.0 decision tree algorithm is calculated to save 78,183 kW·h, which is 32.4 % of the total energy consumption, and the energy-saving strategy based on the CHDIA decision tree algorithm is calculated to save 73,182 kW·h, which is 30.3 % of the total energy consumption. [ABSTRACT FROM AUTHOR]

Effective demand responsiveness (DR) is crucial to the stability of the electrical grid. The increasing penetration of renewable energy sources demands higher load variation adaptability. Therefore, consumer-side flexibility is required for responding to abrupt DR signals. Real-time pricing (RTP) offer a direct approach for continually communicating DR signals. RTP has shown effectiveness in residential applications, however, its implications are impaired in industrial buildings which are less price-elastic due to stresses imposed by just in time (JIT) manufacturing and market competition. In this paper, we propose an instantaneous demand control methodology for industrial and commercial buildings, where the DR action is continually updated as new DR signals are received. We utilize the hour-ahead RTP (RTP-HA) tariffs and the demand shifting concept. The instantaneous approach is independent of price prediction uncertainty and scheduling approaches. The controller algorithm is converted to a linear optimization problem which is solved optimally and saves computational time, making it practical for real-time use. The method is robust and verified using MATLAB/SIMULINK with actual, 1 week, data from eight industrial and commercial buildings in Florida. Results show modest reductions in consumers’ electricity bills while maintaining required comfort standards. Results also address the load synchronization problem associated with RTP. [ABSTRACT FROM AUTHOR]

Enthalpy recovery ventilators exchange heat and mass between exhausted air and outdoor ventilation air to reduce mechanical ventilation loads for HVAC systems in buildings. This study demonstrates the energy saving potential of integrating membrane-based energy recovery ventilators at zone-level into the conventional variable air volume (VAV) systems for commercial buildings. Building energy simulations are used to evaluate various operation strategies and potential energy savings for four selected climates including Minneapolis, Atlanta, Baltimore, and Miami. A medium-size DOE reference commercial building model with conventional VAV systems was used as a baseline. Three cases implemented with energy recovery ventilators were simulated using EnergyPlus and their results were compared with baseline cases in compliance with ASHRAE standard 90.1. The annual HVAC energy saving potential can reach up to 18–49% with this technique for various climates. The challenges of implementing and operating the zone-level energy recovery ventilators are also discussed in this paper. [ABSTRACT FROM AUTHOR]

Buildings account for some 40% of the world's total energy usage. One factor that could help to improve energy efficiency in buildings is more accurate modeling of occupancy. In this paper, we propose two novel stochastic inhomogeneous Markov chains to model building occupancy under two scenarios of multi-occupant single-zone (MOSZ) and multi-occupant multi-zone (MOMZ) respectively. In the MOSZ scenario, instead of using occupancy (i.e. the number of occupants in a zone) as the state, we define the state of the inhomogeneous Markov chain as the increment of occupancy in the zone. In the MOMZ scenario, by taking into account interactions among zones, we propose another inhomogeneous Markov chain whose state is a vector in which each component represents the increment of occupancy in each zone. In this way, we can significantly simplify the calculation of transition probability matrix which is a key parameter in Markov chain models. Several simulations with real data have been conducted to evaluate the performance of the proposed models under the two scenarios. To quantify the performance of the proposed models, five variables related to occupancy properties and two evaluation criteria are defined. The results show that our proposed approaches have superior performance over existing ones. [ABSTRACT FROM AUTHOR]

Commercial buildings account for a significant portion of energy consumption and associated carbon emissions around the world. Consequently, many countries are instituting building energy efficiency policies to mitigate the negative environmental impacts of building operations. As building owners and operators act to address the challenge of increasing energy efficiency, occupant behavior modification programs are growing increasingly popular. Recent advances in energy monitoring and control technologies have enabled the development of eco-feedback systems that collect, process, and relay high resolution, real-time energy consumption information to help building occupants control their energy-use. These systems have extended research into the effects of high resolution eco-feedback on building occupant behavior and energy efficiency from residential to commercial building settings. However, little is understood about how organizational network dynamics impact user-engagement levels with such systems and how these network connections may impact the energy conservation behavior of individuals inside commercial buildings. In this paper, results are presented from a novel 9-week eco-feedback system study which demonstrates that organizational network dynamics can significantly impact energy conservation among commercial building occupants. Furthermore, it is shown that exposure to eco-feedback impacts building occupant energy conservation differently in commercial office buildings than it does in residential buildings. [ABSTRACT FROM AUTHOR]

Building energy prediction is a key factor to assess the energy performance of commercial buildings, identify operation issues and propose better operating strategies based on the forecast information. Different models have been used to forecast energy demand in buildings, including whole building energy simulation, regression analysis, and black-box models (e.g., artificial neural networks). This paper presents a different approach to predict the energy demand of commercial buildings using case-based reasoning (CBR). The proposed approach is evaluated using monitored data in a real office building located in Varennes, Québec. The energy demand is predicted at every hour for the following 3 h using weather forecasts. The results show that during occupancy, 7:00–18:00, the coefficient of variance of the root-mean-square-error (CV-RMSE) is below 13.2%, the normalized mean bias error (NMBE) is below 5.8% and the root-mean-square-error (RMSE) is below 14 kW. When the statistical criteria are calculated for all hours of the day, the CV-RMSE is 12.1%, the NMBE is 1.0% and the RMSE is 11 kW. The case study demonstrates that CBR can be used for energy demand prediction and could be implemented in building operation systems. [ABSTRACT FROM AUTHOR]

The economic and environmental benefits of building retrofits have been acknowledged. However, there is a series of barriers that threaten to impede implementing successful retrofit projects such as: lack of funding, lack of interoperability, and unstructured decision making. This paper aims to address these barriers by providing the framework of the Virtual Retrofit Model (VRM), an affordable computational platform that supports streamlined decision making for building retrofit projects. An occupant survey was implemented to identify the primary requirements and perceptions from different types of stakeholders of the buildings. The responses were analyzed to identify the most important criteria of the future retrofit projects to focus on if it were to be renovated in the future. A case study approach was used to describe the outcomes from a year-long demonstration project that has been conducted at an aging commercial building. The research activities focused on integrating theories and technologies of Building Information Modeling (BIM), energy simulation, agent-based modeling, multi-criteria decision support system, and software application that can be employed and adopted in building retrofit projects. The software prototype is designed to connect buildings to a smart grid environment where building energy data should be shared for intelligent decision making. [ABSTRACT FROM AUTHOR]

Underfloor air distribution (UFAD) system is widely used in commercial buildings and data centers for its potential advantages of superior thermal comfort and indoor air quality, etc. In UFAD system, conditioned air is directly supplied to the occupied zone from a pressurized plenum, which causes temperature stratification from the lower zone to the upper zone. The airflow distribution is determined by the pressure decay and flow pattern in the plenum, which is affected by the size of plenum, the open area of the diffusers, air leakage, and the obstructions under raised floor. This paper summarizes the results of an experimental study of airflows through plenum and perforated tiles in a plenum test facility, and the governing equations and boundary conditions on the distribution of velocity and pressure in the plenum are deduced. The linear models of the lost coefficient of air leakage and the velocity ratio of outlet and inlet of the plenum are proposed to simplify the measurement and calculation of the distribution of velocity and pressure in the plenum. The results show that the calculated results coincide with the experimental results, and the models will help to understand the design and operation for the UFAD system. [ABSTRACT FROM AUTHOR]

Abstract: Building performance simulation has a key role in facilitating effective retrofitting of commercial buildings, an essential task to reduce Australia's greenhouse gas emissions. Simulation users commonly rely on assumptions for certain ‘hard-to-measure’ building and behaviour inputs, and the range of values use can significantly affect predicted energy consumption. New financing methods in Australia may attach financial risk to this simulation uncertainty. This paper reports on a study examining the sensitivity of total predicted building energy consumption to hard-to-measure simulation inputs for two template Australian office buildings; a 10-storey office tower located in a Central Business District (CBD), and a 3-storey office on the fringe of a CBD. The predicted energy consumption for the archetypes varied by more than 50% from baseline consumption for all Australian capital cities, using high and low assumptions. The input parameters that significantly influence energy consumption were found to be; cooling set-point, Information and Communications Technology (ICT) power density, ICT usage schedule, and lighting power density. A case study of a simple lighting upgrade to a 10-storey office building in Sydney showed that the payback period could vary from 2.4 to 10.3 years depending on the simulation assumptions used. [Copyright &y& Elsevier]

• A comprehensive analysis of data from commercial buildings in United States to identify operational problems and opportunities. • These opportunities were not strongly correlated with building vintage, size, or building type, but were reflective of how the buildings are operated. • This analysis showed that opportunities to improve building operations exist in almost every building. • There was wide variation in whole building energy savings, ranging from 0 to 50% and 0 to 18 $/m2 with median percent annual whole building savings of 12% and median normalized annual cost savings of $1.75/m2. In the United States, as much as 30% of the 19 EJ that commercial buildings consume is considered excess. Much of the excess energy is due to the inability to manage building operations efficiently. Because almost 20% of the total primary energy consumption is associated with commercial buildings, significant energy reductions in this sector are needed to mitigate climate change. Therefore, many cities and states are mandating periodic "tune-ups" of these buildings to eliminate excess energy consumption. Although the benefits of tune-ups and retro -commissioning are clear, focusing these mandates to look for specific opportunities has been a challenge because of the lack of studies that document the prevalence of opportunities. Therefore, we analyzed building automation system data from 151 buildings across the United States to document common operational problems and opportunities to improve building operations. This analysis showed that opportunities to improve building operations exist in almost every building. These opportunities were not strongly correlated with building vintage or size, but were reflective of how the buildings are operated. The prevalence of the top 20 opportunities ranged between 74% and 23%, with 40% of these associated with air-handling units. The rest of the opportunities are associated with schedules, chilled and hot-water distribution, and zone controls. Of the 151 buildings, 69 of them implemented corrective actions of some or all opportunities that were identified. Implementation varied across the Re-tuning categories, with 60% for schedule opportunities, 50% for zone opportunities, over 40% for the air-handling unit and hot-water opportunities, and 35% of the chilled-water opportunities. There was wide variation in whole building energy savings, ranging from 0 to 50% and 0 to 18 $/m2 with median percent annual whole building savings of 12% and median normalized annual cost savings of $1.75/m2. In addition to documenting these key findings, the paper provides a list of opportunities that can be automatically and continuously identified and corrected and offers a list of those opportunities that should be the focus of the mandates. [ABSTRACT FROM AUTHOR]

Abstract: In Hong Kong, conventional air-conditioning (AC) systems cause a high energy consumption and poor indoor thermal comfort. This paper investigated the AC load profiles of three typical local commercial buildings, i.e. office, hotel and retail, and examined the energy consumption of liquid desiccant cooling system (LDCS) for these buildings. Simulation results showed that the cooling load contributed to more than 98% of total annual load, with high latent load proportion from March to September. The high reheating ratio, i.e. from 60% to 90%, is found to be the main reason for the poor thermal comfort. Therefore, the LDCS which could handle sensible and latent loads separately was proposed. However, the electricity-driven LDCS was found not suitable for local commercial buildings. significant energy saving could be achieved by the solar-assisted LDCS system with thermal storage, when the installation area for solar collectors is larger than the minimum required one, i.e. 9.7m2/(kWm−2) for office and 12.7m2/(kWm−2) for hotel buildings. Energy saving of different system types with different installation areas was also studied. The solar-assisted LDCS shows more energy saving in winter but less in summer. Results and conclusions can be useful for researchers and local engineers. [Copyright &y& Elsevier]