VTechWorks Repository :: Browsing by Author "Jazizadeh, Farrokh"

Browsing by Author "Jazizadeh, Farrokh"

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An Analysis of Short-Term Load Forecasting on Residential Buildings Using Deep Learning Models
Suresh, Sreerag (Virginia Tech, 2020-07-07)
Building energy load forecasting is becoming an increasingly important task with the rapid deployment of smart homes, integration of renewables into the grid and the advent of decentralized energy systems. Residential load forecasting has been a challenging task since the residential load is highly stochastic. Deep learning models have showed tremendous promise in the fields of time-series and sequential data and have been successfully used in the field of short-term load forecasting at the building level. Although, other studies have looked at using deep learning models for building energy forecasting, most of those studies have looked at limited number of homes or an aggregate load of a collection of homes. This study aims to address this gap and serve as an investigation on selecting the better deep learning model architecture for short term load forecasting on 3 communities of residential buildings. The deep learning models CNN and LSTM have been used in the study. For 15-min ahead forecasting for a collection of homes it was found that homes with a higher variance were better predicted by using CNN models and LSTM showed better performance for homes with lower variances. The effect of adding weather variables on 24-hour ahead forecasting was studied and it was observed that adding weather parameters did not show an improvement in forecasting performance. In all the homes, deep learning models are shown to outperform the simple ANN model.
Anonymous Indoor Positioning System using Depth Sensors for Context-aware Human-Building Interaction
Ballivian, Sergio Marlon (Virginia Tech, 2019-05-24)
Indoor Localization Systems (ILS), also known as Indoor Positioning Systems (IPS), has been created to determine the position of individuals and other assets inside facilities. Indoor Localization Systems have been implemented for monitoring individuals and objects in a variety of sectors. In addition, ILS could be used for energy and sustainability purposes. Energy management is a complex and important challenge in the Built Environment. The indoor localization market is expected to increase by 33.8 billion in the next 5 years based on the 2016 global survey report (Marketsandmarkets.com). Therefore, this thesis focused on exploring and investigating "depth sensors" application in detecting occupants' indoor positions to be used for smarter management of energy consumption in buildings. An interconnected passive depth-sensor-based system of occupants' positioning was investigated for human-building interaction applications. This research investigates the fundamental requirements for depth-sensing technology to detect, identify and track subjects as they move across different spaces. This depth-based approach is capable of sensing and identifying individuals by accounting for the privacy concerns of users in an indoor environment. The proposed system relies on a fixed depth sensor that detects the skeleton, measures the depth, and further extracts multiple features from the characteristics of the human body to identify them through a classifier. An example application of such a system is to capture an individuals' thermal preferences in an environment and deliver services (targeted air conditioning) accordingly while they move in the building. The outcome of this study will enable the application of cost-effective depth sensors for identification and tracking purposes in indoor environments. This research will contribute to the feasibility of accurate detection of individuals and smarter energy management using depth sensing technologies by proposing new features and creating combinations with typical biometric features. The addition of features such as the area and volume of human body surface was shown to increase the accuracy of the identification of individuals. Depth-sensing imaging could be combined with different ILS approaches and provide reliable information for service delivery in building spaces. The proposed sensing technology could enable the inference of people location and thermal preferences across different indoor spaces, as well as, sustainable operations by detecting unoccupied rooms in buildings.
A BIM-based Interoperability Platform in Support of Building Operation and Energy Management
Xiong, Yunjie (Virginia Tech, 2020-03-18)
Building energy efficiency is progressively becoming a crucial topic in the architecture, engineering, and construction (AEC) sector. Energy management tools have been developed to promise appropriate energy savings. Building energy simulation (BES) is a tool mainly used to analyze and compare the energy consumption of various design/operation scenarios, while building automation systems (BAS) works as another energy management tool to monitor, measure and collect operational data, all in an effort to optimize energy consumption. By integrating the energy simulated data and actual operational data, the accuracy of a building energy model can be increased while the calibrated energy model can be applied as a benchmark for guiding the operational strategies. This research predicted that building information modeling (BIM) would link BES and BAS by acting as a visual model and a database throughout the lifecycle of a building. The intent of the research was to use BIM to document energy-related information and to allow its exchange between BES and BAS. Thus, the energy-related data exchange process would be simplified, and the productive efficiency of facility management processes would increase. A systematic literature review has been conducted in investigating the most popular used data formats and data exchange methods for the integration of BIM/BES and BAS, the results showed the industry foundation classes (IFC) was the most common choice for BIM tools mainly and database is a key solution for managing huge actual operational datasets, which was a reference for the next step in research. Then a BIM-based framework was proposed to supporting the data exchange process among BIM/BES/BAS. 4 modules including BIM Module, Operational Data Module, Energy Simulation Module and Analysis and Visualization Module with an interface were designed in the framework to document energy-related information and to allow its exchange between BES and BAS. A prototype of the framework was developed as a platform and a case study of an entire office suite was conducted using the platform to validate this framework. The results showed that the proposed framework enables automated or semi-automated multiple-model development and data analytics processes. In addition, the research explored how BIM can enhance the application of energy modeling during building operation processes as a means to improve overall energy performance and facility management productivity.
Building Energy Profile Clustering Based on Energy Consumption Patterns
Afzalan, Milad (Virginia Tech, 2020-06)
With the widespread adoption of smart meters in buildings, an unprecedented amount of high- resolution energy data is released, which provides opportunities to understand building consumption patterns. Accordingly, research efforts have employed data analytics and machine learning methods for the segmentation of consumers based on their load profiles, which help utilities and energy providers for customized/personalized targeting for energy programs. However, building energy segmentation methodologies may present oversimplified representations of load shapes, which do not properly capture the realistic energy consumption patterns, in terms of temporal shapes and magnitude. In this thesis, we introduce a clustering technique that is capable of preserving both temporal patterns and total consumption of load shapes from customers’ energy data. The proposed approach first overpopulates clusters as the initial stage to preserve the accuracy and merges the similar ones to reduce redundancy in the second stage by integrating time-series similarity techniques. For such a purpose, different time-series similarity measures based on Dynamic Time Warping (DTW) are employed. Furthermore, evaluations of different unsupervised clustering methods such as k-means, hierarchical clustering, fuzzy c-means, and self-organizing map were presented on building load shape portfolios, and their performance were quantitatively and qualitatively compared. The evaluation was carried out on real energy data of ~250 households. The comparative assessment (both qualitatively and quantitatively) demonstrated the applicability of the proposed approach compared to benchmark techniques for power time-series clustering of household load shapes. The contribution of this thesis is to: (1) present a comparative assessment of clustering techniques on household electricity load shapes and highlighting the inadequacy of conventional validation indices for choosing the cluster number and (2) propose a two-stage clustering approach to improve the representation of temporal patterns and magnitude of household load shapes.
Data-driven customer energy behavior characterization for distributed energy management
Afzalan, Milad (Virginia Tech, 2020-07-01)
With the ever-growing concerns of environmental and climate concerns for energy consumption in our society, it is crucial to develop novel solutions that improve the efficient utilization of distributed energy resources for energy efficiency and demand response (DR). As such, there is a need to develop targeted energy programs, which not only meet the requirement of energy goals for a community but also take the energy use patterns of individual households into account. To this end, a sound understanding of the energy behavior of customers at the neighborhood level is needed, which requires operational analytics on the wealth of energy data from customers and devices. In this dissertation, we focus on data-driven solutions for customer energy behavior characterization with applications to distributed energy management and flexibility provision. To do so, the following problems were studied: (1) how different customers can be segmented for DR events based on their energy-saving potential and balancing peak and off-peak demand, (2) what are the opportunities for extracting Time-of-Use of specific loads for automated DR applications from the whole-house energy data without in-situ training, and (3) how flexibility in customer demand adoption of renewable and distributed resources (e.g., solar panels, battery, and smart loads) can improve the demand-supply problem. In the first study, a segmentation methodology form historical energy data of households is proposed to estimate the energy-saving potential for DR programs at a community level. The proposed approach characterizes certain attributes in time-series data such as frequency, consistency, and peak time usage. The empirical evaluation of real energy data of 400 households shows the successful ranking of different subsets of consumers according to their peak energy reduction potential for the DR event. Specifically, it was shown that the proposed approach could successfully identify the 20-30% of customers who could achieve 50-70% total possible demand reduction for DR. Furthermore, the rebound effect problem (creating undesired peak demand after a DR event) was studied, and it was shown that the proposed approach has the potential of identifying a subset of consumers (~5%-40% with specific loads like AC and electric vehicle) who contribute to balance the peak and off-peak demand. A projection on Austin, TX showed 16MWh reduction during a 2-h event can be achieved by a justified selection of 20% of residential customers. In the second study, the feasibility of inferring time-of-use (ToU) operation of flexible loads for DR applications was investigated. Unlike several efforts that required considerable model parameter selection or training, we sought to infer ToU from machine learning models without in-situ training. As the first part of this study, the ToU inference from low-resolution 15-minute data (smart meter data) was investigated. A framework was introduced which leveraged the smart meter data from a set of neighbor buildings (equipped with plug meters) with similar energy use behavior for training. Through identifying similar buildings in energy use behavior, the machine learning classification models (including neural network, SVM, and random forest) were employed for inference of appliance ToU in buildings by accounting for resident behavior reflected in their energy load shapes from smart meter data. Investigation on electric vehicle (EV) and dryer for 10 buildings over 20 days showed an average F-score of 83% and 71%. As the second part of this study, the ToU inference from high-resolution data (60Hz) was investigated. A self-configuring framework, based on the concept of spectral clustering, was introduced that automatically extracts the appliance signature from historical data in the environment to avoid the problem of model parameter selection. Using the framework, appliance signatures are matched with new events in the electricity signal to identify the ToU of major loads. The results on ~1500 events showed an F-score of >80% for major loads like AC, washing machine, and dishwasher. In the third study, the problem of demand-supply balance, in the presence of varying levels of small-scale distributed resources (solar panel, battery, and smart load) was investigated. The concept of load complementarity between consumers and prosumers for load balancing among a community of ~250 households was investigated. The impact of different scenarios such as varying levels of solar penetration, battery integration level, in addition to users' flexibility for balancing the supply and demand were quantitatively measured. It was shown that (1) even with 100% adoption of solar panels, the renewable supply cannot cover the demand of the network during afternoon times (e.g., after 3 pm), (2) integrating battery for individual households could improve the self-sufficiency by more than 15% during solar generation time, and (3) without any battery, smart loads are also capable of improving the self-sufficiency as an alternative, by providing ~60% of what commercial battery systems would offer. The contribution of this dissertation is through introducing data-driven solutions/investigations for characterizing the energy behavior of households, which could increase the flexibility of the aggregate daily energy load profiles for a community. When combined, the findings of this research can serve to the field of utility-scale energy analytics for the integration of DR and improved reshaping of network energy profiles (i.e., mitigating the peaks and valleys in daily demand profiles).
Decentralized HVAC Operations: Novel Sensing Technologies and Control for Human-Aware HVAC Operations
Jung, Wooyoung (Virginia Tech, 2020-04-13)
Advances in Information and Communication Technology (ICT) paved the way for decentralized Heating, Ventilation, and Air-Conditioning (HVAC) HVAC operations. It has been envisioned that development of personal thermal comfort profiles leads to accurate predictions of each occupant's thermal comfort state and such information is employed in context-aware HVAC operations for energy efficiency. This dissertation has three key contributions in realizing this envisioned HVAC operation. First, it presents a systematic review of research trends and developments in context-aware HVAC operations. Second, it contributes to expanding the feasibility of the envisioned HVAC operation by introducing novel sensing technologies. Third, it contributes to shedding light on viability and potentials of comfort-aware operations (i.e., integrating personal thermal comfort models into HVAC control logic) through a comprehensive assessment of energy efficiency implications. In the first contribution, by developing a taxonomy, two major modalities – occupancy-driven and comfort-aware operations – in Human-In-The-Loop (HITL) HVAC operations were identified and reviewed quantitatively and qualitatively. The synthesis of previous studies has indicated that field evaluations of occupancy-driven operations showed lower potentials in energy saving, compared to the ones with comfort-aware operations. However, the results in comfort-aware operations could be biased given the small number of explorations. Moreover, required data representation schema have been presented to foster constructive performance assessments across different research efforts. In the end, the current state of research and future directions of HITL HVAC operations were discussed to shed light on future research need. As the second contribution, moving toward expanding the feasibility of comfort-aware operations, novel and smart sensing solutions have been introduced. It has been noted that, in order to have high accuracy in predicting individual's thermal comfort state (≥90%), user physiological response data play a key part. However, the limited number of applicable sensing technologies (e.g., infrared cameras) has impeded the potentials of implementation. After defining required characteristics in physiological sensing solutions in context of comfort-aware operations (applicability, sensitivity, ubiquity, and non-intrusiveness), the potentials of RGB cameras, Doppler radar sensors, and heat flux sensors were evaluated. RGB cameras, available in many smart computing devices, could be a ubiquitous solution in quantifying thermoregulation states. Leveraging the mechanism of skin blood perfusion, two thermoregulation state quantification methods have been developed. Then, applicability and sensitivity were checked with two experimental studies. In the first experimental study aiming to see applicability (distinguishing between 20 and 30C with fully acclimated human bodies), for 16 out of 18 human subjects, an increase in their blood perfusion was observed. In the second experimental study aiming to evaluate sensitivity (distinguishing responses to a continuous variation of air temperature from 20 to 30C), 10 out of 15 subjects showed a positive correlation between blood perfusion and thermal sensations. Also, the superiority of heat flux data, compared to skin temperature data, has been demonstrated in predicting personal thermal comfort states through the developments of machine-learning-based prediction models with feature engineering. Specifically, with random forest classifier, the median value of prediction accuracy was improved by 3.8%. Lastly, Doppler radar sensors were evaluated for their capability of quantifying user thermoregulation states leveraging the periodic movement of the chest/abdomen area induced by respiration. In an experimental study, the results showed that, with sufficient acclimation time, the DRS-based approach could show distinction between respiration states for two distinct air temperatures (20 and 30C). On the other hand, in a transient temperature without acclimation time, it was shown that, some of the human subjects (38.9%) used respiration as an active means of heat exchange for thermoregulation. Lastly, a comprehensive evaluation of comfort-aware operations' performance was carried out with a diverse set of contextual and operational factors. First, a novel comfort-aware operation strategy was introduced to leverage personal sensitivity to thermal comfort (i.e., different responses to temperature changes; e.g., sensitive to being cold) in optimization. By developing an agent-based simulation framework and thorough diverse scenarios with different numbers and combinations of occupants (i.e., human agents in the simulation), it was shown that this approach is superior in generating collectively satisfying environments against other approaches focusing on individual preferred temperatures in selection of optimized setpoints. The energy implications of comfort-aware operations were also evaluated to understand the impact from a wide range of factors (e.g., human and building factors) and their combinatorial effect given the uncertainty of multioccupancy scenarios. The results demonstrated that characteristics of occupants' thermal comfort profiles are dominant in impacting the energy use patterns, followed by the number of occupants, and the operational strategies. In addition, when it comes to energy efficiency, more occupants in a thermal zone/building result in reducing the efficacy of comfort-driven operation (i.e., the integration of personal thermal comfort profiles). Hence, this study provided a better understanding of true viability of comfort-driven HVAC operations and provided the probabilistic bounds of energy saving potentials. These series of studies have been presented as seven journal articles and they are included in this dissertation.
A Deep Learning Approach to Predict Accident Occurrence Based on Traffic Dynamics
Khaghani, Farnaz (Virginia Tech, 2020-05)
Traffic accidents are of concern for traffic safety; 1.25 million deaths are reported each year. Hence, it is crucial to have access to real-time data and rapidly detect or predict accidents. Predicting the occurrence of a highway car accident accurately any significant length of time into the future is not feasible since the vast majority of crashes occur due to unpredictable human negligence and/or error. However, rapid traffic incident detection could reduce incident-related congestion and secondary crashes, alleviate the waste of vehicles’ fuel and passengers’ time, and provide appropriate information for emergency response and field operation. While the focus of most previously proposed techniques is predicting the number of accidents in a certain region, the problem of predicting the accident occurrence or fast detection of the accident has been little studied. To address this gap, we propose a deep learning approach and build a deep neural network model based on long short term memory (LSTM). We apply it to forecast the expected speed values on freeways’ links and identify the anomalies as potential accident occurrences. Several detailed features such as weather, traffic speed, and traffic flow of upstream and downstream points are extracted from big datasets. We assess the proposed approach on a traffic dataset from Sacramento, California. The experimental results demonstrate the potential of the proposed approach in identifying the anomalies in speed value and matching them with accidents in the same area. We show that this approach can handle a high rate of rapid accident detection and be implemented in real-time travelers’ information or emergency management systems.
Directional Airflow for HVAC Systems
Abedi, Milad (Virginia Tech, 2019)
Directional airflow has been utilized to enable targeted air conditioning in cars and airplanes for many years, where the occupants could adjust the direction of flow. In the building sector however, HVAC systems are usually equipped with stationary diffusors that can only supply the air either in the form of diffusion or with fixed direction to the room in which they have been installed. In the present thesis, the possibility of adopting directional airflow in lieu of the conventional uniform diffusors has been investigated. The potential benefits of such a modification in control capabilities of the HVAC system in terms of improvements in the overall occupant thermal comfort and energy consumption of the HVAC system have been investigated via a simulation study and an experimental study. In the simulation study, an average of 59% per cycle reduction was achieved in the energy consumption. The reduction in the required duration of airflow (proportional to energy consumption) in the experimental study was 64% per cycle. The feasibility of autonomous control of the directional airflow, has been studied in a simulation experiment by utilizing the Reinforcement Learning algorithm which is an artificial intelligence approach that facilitates autonomous control in unknown environments. In order to demonstrate the feasibility of enabling the existing HVAC systems to control the direction of airflow, a device (called active diffusor) was designed and prototyped. The active diffusor successfully replaced the existing uniform diffusor and was able to effectively target the occupant positions by accurately directing the airflow jet to the desired positions.
An Enhanced RCS Heuristic and an Enhanced RCPM Algorithm to Perform Delay Analysis in Schedules without Phantom Float
Franco Duran, Diana Marcela (Virginia Tech, 2020-04-08)
On a regular basis, project managers concentrate their efforts on critical and near-critical activities. However, the concepts of total float and critical path lose their significance after applying resource-constrained scheduling (RCS) methodologies. RCS techniques solve the resource conflicts but create phantom float in the schedules (i.e., a float that does not exist). RCS techniques overlook the resources relationships between activities that compete for the same but unavailable resources. Therefore, each time an activity uses this apparent float (phantom float), there is a resource violation in the schedule. Due to the projects' size and complexity, schedulers use scheduling software such as Primavera P6 to fix the resource conflicts of a schedule. The software correctly determines the activities' earliest dates that satisfy the resource limitations, but they calculate total float based on a "Time Context" ignoring the presence of resource constraints. Thus, the results show incorrect total float values and a broken critical path. The lack of a continuous critical path makes impossible the anticipation of the impact of a delaying event in the project completion time. Several algorithms have been developed to address the shortcomings of RCS methods. These RCS related algorithms were developed with the aim of providing project managers a tool to correctly schedule and identify critical activities with respect to time and resource allocation and correctly calculate the total float of each activity under resource constraints. In this regard, the Resource-Constrained Critical Path Method (RCPM) is an algorithm that correctly calculates the floats of activities and identifies a continuous critical path in resource-constrained schedules. Regardless of the RCPM provides more reliable float values than traditional RCS-related algorithms, there are some shortcomings that must be addressed to enhance its capability. This study addresses the existing shortcomings of RCPM to make it more practical for real construction projects.
Establishing the Need for Tailored Energy Feedback Programs in Buildings
Khosrowpour, Ardalan (Virginia Tech, 2016-10-06)
Buildings account for 40% of energy consumption in the US. Despite all improvements in buildings shell, equipment, and design, CO2 emissions from buildings are increasing as a result of increased energy consumption. Since occupants spend more than 90% of their time indoors, they are inseparable and significant elements of building system dynamics. Hence, there is a great potential for energy efficiency in buildings using a wide range of programs such as education, intervention, energy feedback, etc. Due to advancement of technology and accessibility of high resolution energy consumption data, utility companies are enabled to focus on implementing energy feedback programs to induce energy efficiency and reduce the peak energy load in the commercial and residential sector. In order to better understand various aspects of energy feedback programs, in the first chapter of this dissertation, I conduct a comprehensive literature review on the state-of-the-art energy feedback study methods and identify gaps of knowledge and challenges faced by researchers in the field. Accordingly, the future research vision is laid out at the intersection of methods and gaps of knowledge used in energy feedback studies and future research opportunities and questions are provided. One of the major gaps of knowledge I identified in the literature review is the lack of quantitative analyses used to investigate the variability of occupant responses to commercial buildings energy feedback programs to evaluate the need for targeted and tailored energy feedback programs. In the second chapter, I conducted a comprehensive analysis on occupant energy-use responses under the influence of a uniform energy feedback program. Furthermore, I investigated the effectiveness of notifications on increasing the level of engagement of the occupants in these studies. The results supported the existence of a variability in responses and engagement level in a uniform energy feedback program which may be due to intra-class variability of occupant behavior. In the third chapter, based on the established need for a targeted energy feedback program, I investigate the predictability of occupant energy consumption behavior and its correlation with energy consumption. The results report that 46% of occupants may be good candidates for targeted energy feedback programs due to their combination of higher levels of energy-use and predictability of their energy consumption behavior.
Evaluation of In-Service Residential Water Meters: Analysis of Registration Error and Metering Infrastructure Upgrades
Mantilla Pena, Carlos Fernando (Virginia Tech, 2020-01-22)
The American Water Works Association (AWWA) and the International Water Association (IWA) have designated the volume of water not registered by water meters as a form of "apparent loss" in a distribution system. The term apparent is given because this volume is not technically a water loss, as is the case of wasted water from real leaks in the distribution system. Large volumes of apparent losses hurt the revenue of utilities that rely on water metering to bill their customers. This is critical to utilities given that billed consumption is often the main source of income to provide adequate service. This form of apparent losses is a challenge to water management, particularly, in the case of significant drought because of the uncertainty about the real volume of water consumed. Although the impact of apparent losses from a single residential service connection is not as significant compared to an industrial meter with low accuracy, the cumulative effect of apparent losses across residential users can be very significant. Until the early 2000's water utilities in the U.S. relied on mechanical water meters to measure residential water use. Since then, electronic meters with higher accuracy at low flow rates have been developed. Data collection from meters has also evolved as well, from the manual reading by an operator, to drive-by systems and most recently to remote readings using a network of transmitters/receivers (i.e., advanced metering infrastructure or AMI). An expectation of this dissertation is that it will help water utilities to have a better idea of the volume of apparent losses due to metering inaccuracy (i.e., registration error) and provide insights into the effects of installing AMI systems to residential metered water (MW). To achieve this goal, two main objectives are fulfilled 1) to expand on the knowledge of registration error (RE) in mechanical nutating-disc (ND) meters used to monitor residential consumption, and 2) to evaluate the impact of metering infrastructure upgrades on the volume of metered water (MW) from residential service connections. This dissertation follows the manuscript format with three journal articles constituting the main chapters after a general introduction characterizing the issues in Chapter 1. Chapter 2 is an experimental study that evaluates the influence of service time (ST) and volumetric throughput (TP) on the accuracy of ND meters within the recommended flow rates set by the U.S. water industry for meters with an internal diameter of ⅝-in. (15-mm). Over 300 meters removed from service were tested for accuracy. Key findings of this study are 1) ND meters that have been in service over 25 years have a greater likelihood of poor accuracy at the minimum recommended flow rate (Q^min) of 0.25 gallons per minute (gpm) (57 liters per hour (L/h)) and 0.5 gpm (114 L/h) independent of TP, and 2) comparison with data from accelerated laboratory testing showed that simulated use may not necessarily reflect the actual performance of ND meters in service, particularly, at 0.25 and 0.5 gpm. Chapter 3 is an experimental study that investigates REs of ND meters below the minimum recommended flow rate (Q^min = 0.25 gpm), particularly, at ½, ¼ and ⅛ of Q^min. Over 100 meters removed from service were tested in this study. Key findings of this study are 1) confirmed how performance decreases with reducing flow rate below Q^min, 2) of the variables considered, TP was found to be a better indicator of RE at Q_(1/8)^min up to an approximate meter reading of 0.66 MG (2.5 ML) compared to ST for 10 ≤ ST ≤ 24 years, with minimal influence at Q_(1/4)^min and none at Q_(1/2)^min, and 3) a strong linear relationship was found between RE at Q_(1/2)^min and RE at Q^min independent of TP or ST. Chapter 4 is a study that evaluates the extent to which the implementation of a new AMI system combined with a system-wide installation of new ND meters impacted the volume of MW from residential service connections of a 22,000-person municipality in southwest Virginia. Time-series analysis techniques were employed to evaluate changes in the trend of bimonthly MW and median daily MW over a six-year period. Key findings of this study are 1) the AMI system improved the accountability of MW for the utility, 2) despite an ongoing downward annual trend in MW, average bimonthly MW mildly increased after the AMI system was fully operational, and 3) annual MW increased by 2.2% in the 12-month period immediately following the metering infrastructure upgrade.
The field of human building interaction for convergent research and innovation for intelligent built environments
Becerik-Gerber, Burcin; Lucas, Gale; Aryal, Ashrant; Awada, Mohamad; Berges, Mario; Billington, Sarah; Boric-Lubecke, Olga; Ghahramani, Ali; Heydarian, Arsalan; Hoelscher, Christoph; Jazizadeh, Farrokh; Khan, Azam; Langevin, Jared; Liu, Ruying; Marks, Frederick; Mauriello, Matthew Louis; Murnane, Elizabeth; Noh, Haeyoung; Pritoni, Marco; Roll, Shawn; Schaumann, Davide; Seyedrezaei, Mirmahdi; Taylor, John E.; Zhao, Jie; Zhu, Runhe (Nature Portfolio, 2022-12-21)
Human-Building Interaction (HBI) is a convergent field that represents the growing complexities of the dynamic interplay between human experience and intelligence within built environments. This paper provides core definitions, research dimensions, and an overall vision for the future of HBI as developed through consensus among 25 interdisciplinary experts in a series of facilitated workshops. Three primary areas contribute to and require attention in HBI research: humans (human experiences, performance, and well-being), buildings (building design and operations), and technologies (sensing, inference, and awareness). Three critical interdisciplinary research domains intersect these areas: control systems and decision making, trust and collaboration, and modeling and simulation. Finally, at the core, it is vital for HBI research to center on and support equity, privacy, and sustainability. Compelling research questions are posed for each primary area, research domain, and core principle. State-of-the-art methods used in HBI studies are discussed, and examples of original research are offered to illustrate opportunities for the advancement of HBI research.
flEECe, an energy use and occupant behavior dataset for net-zero energy affordable senior residential buildings
Paige, Frederick; Agee, Philip; Jazizadeh, Farrokh (Nature, 2019-11-26)
The behaviors of building occupants have continued to perplex scholars for years in our attempts to develop models for energy efficient housing. Building simulations, project delivery approaches, policies, and more have fell short of their optimistic goals due to the complexity of human behavior. As a part of a multiphase longitudinal affordable housing study, this dataset represents energy and occupant behavior attributes for 6 affordable housing units over nine months in Virginia, USA which are not performing to the net-zero energy standard they were designed for. This dataset provides researchers the ability to analyze the following variables: energy performance, occupant behaviors, energy literacy, and ecological perceptions. Energy data is provided at a 1 Hz sampling rate for four circuits: main, hot water heater, dryer, and HVAC. Building specifications, occupancy, weather data, and neighboring building energy use data are provided to add depth to the dataset. This dataset can be used to update building energy use models, predictive maintenance, policy frameworks, construction risk models, economic models, and more.
Heat Flux Sensing for Machine-Learning-Based Personal Thermal Comfort Modeling
Jung, Wooyoung; Jazizadeh, Farrokh; Diller, Thomas E. (MDPI, 2019-08-25)
In recent years, physiological features have gained more attention in developing models of personal thermal comfort for improved and accurate adaptive operation of Human-In-The-Loop (HITL) Heating, Ventilation, and Air-Conditioning (HVAC) systems. Pursuing the identification of effective physiological sensing systems for enhancing flexibility of human-centered and distributed control, using machine learning algorithms, we have investigated how heat flux sensing could improve personal thermal comfort inference under transient ambient conditions. We have explored the variations of heat exchange rates of facial and wrist skin. These areas are often exposed in indoor environments and contribute to the thermoregulation mechanism through skin heat exchange, which we have coupled with variations of skin and ambient temperatures for inference of personal thermal preferences. Adopting an experimental and data analysis methodology, we have evaluated the modeling of personal thermal preference of 18 human subjects for well-known classifiers using different scenarios of learning. The experimental measurements have revealed the differences in personal thermal preferences and how they are reflected in physiological variables. Further, we have shown that heat exchange rates have high potential in improving the performance of personal inference models even compared to the use of skin temperature.
A Machine Learning Framework to Infer Time-of-Use of Flexible Loads: Resident Behavior Learning for Demand Response
Afzalan, Milad; Jazizadeh, Farrokh (IEEE, 2020-06-26)
Load shapes obtained from smart meter data are commonly utilized to understand daily energy use patterns for adaptive operations in applications such as Demand Response (DR). However, they do not provide information on the underlying causes of specic energy use patterns i.e., inference on appliances' time-of-use (ToU) as actionable information. In this paper, we investigated a scalable machine learning framework to infer the appliances' ToU from energy load shapes in a collection of residential buildings. A scalable and generalized inference model obviates the need for model training in each building to facilitate its adoption by relying on training data from a set of previously observed buildings with available appliance- level data. To this end, we demonstrated the feasibility of using load shape segmentation to boost ToU inference in buildings by learning from their nearest matches that share similar energy use patterns. To infer an appliance ToU for a building, classication models are trained for inference on subintervals of load shapes from matched buildings with known ToU. The framework was evaluated using real-world energy data from Pecan Street Dataport. The results for a case study on electric vehicles (EV) and dryers showed promising performance by using 15-min smart meter load shape data with 83% and 71% F-score values, respectively, and without in-situ training.
mD-Resilience: A Multi-Dimensional Approach for Resilience-Based Performance Assessment in Urban Transportation
Khaghani, Farnaz; Jazizadeh, Farrokh (MDPI, 2020-06-15)
As demonstrated for extreme events, the resilience concept is used to evaluate the ability of a transportation system to resist and recover from disturbances. Motivated by the high cumulative impact of recurrent perturbations on transportation systems, we have investigated resilience quantification as a performance assessment method for high-probability low-impact (HPLI) disturbances such as traffic congestions. Resilience-based metrics are supplementary to conventional travel-time-based indices in literature. However, resilience is commonly quantified as a scalar variable despite its multi-dimensional nature. Accordingly, by hypothesizing increased information gain in performance assessment, we have investigated a multi-dimensional approach (mD-Resilience) for resilience quantification. Examining roadways’ resilience to recurrent congestions as a contributor to sustainable mobility, we proposed to measure resilience with several attributes that characterize the degradation stage, the recovery stage, and possible recovery paths. These attributes were integrated into a performance index by using Data Envelopment Analysis (DEA) as a non-parametric method. We demonstrated the increased information gain by quantifying the performance of major freeways in Los Angeles, California using Performance Measurement System (PeMS) data. The comparison of mD-Resilience approach with the method based on area under resilience curves showed its potential in distinguishing the severity of congestions. Furthermore, we showed that mD-Resilience also characterizes performance from the lens of delay and bottleneck severities.
Modeling, Control, and Design Study of Balanced Pneumatic Suspension for Improved Roll Stability in Heavy Trucks
Chen, Yang (Virginia Tech, 2017-05-03)
This research investigates a novel arrangement to pneumatic suspensions that are commonly used in heavy trucks, toward providing a dynamically balanced system that resists body roll and provides added roll stability to the vehicle. The new suspension, referred to as "balanced suspension," is implemented by retrofitting a conventional pneumatic suspension with two leveling valves and a symmetric plumbing arrangement to provide a balanced airflow and air pressure in the airsprings. This new design contributes to a balanced force distribution among the axles, which enables the suspension to maintain the body in a leveled position both statically and dynamically. This is in contrast to conventional heavy truck pneumatic suspensions that are mainly adjusted quasi-statically to level the body in response to load variations. The main objectives of the research are to discover and analyze the effects of various pneumatic components on the suspension dynamic response and numerically study the benefits of the pneumatically balanced suspension system. A pneumatic suspension model is established to capture the details of airsprings, leveling valves, check valves, pipes, and air tank based on the laws of fluid mechanics and thermodynamics. Experiments are designed and conducted to help determine and verify the modeling parameters and components. Co-simulation technique is applied to establish a multi-domain model that couples highly non-linear fluid dynamics of the pneumatic suspension with complex multi-body dynamics of an articulated vehicle. The model is used to extensively study effects of pneumatic balanced control of the suspensions on the tractor and trailer combination dynamics. The simulations indicate that the dual leveling valve arrangement of the balanced suspension provides better adjustments to the body roll by charging the airsprings on the jounce side, while purging air from the rebound side. Such an adjustment allows maintaining a larger difference in suspension force from side to side, which resists the vehicle sway and levels the truck body during cornering. Additionally, the balanced suspension better equalizes the front and rear drive axle air pressures, for a better dynamic load sharing and pitch control. It is evident from the simulation results that the balanced suspension increases roll stiffness without affecting vertical stiffness, and thereby it can serve as an anti-roll bar that results in a more stable body roll during steering maneuvers. Moreover, the Failure Mode and Effects Analysis (FMEA) study suggests that when one side of the balanced suspension fails, the other side acts to compensate for the failure. On the other hand, if the trailer is also equipped with dual leveling valves, such an arrangement will bring an additional stabilizing effect to the vehicle in case of the tractor suspension failure. The overall research results presented show that significant improvements on vehicle roll dynamics and suspension dynamic responsiveness can be achieved from the balanced suspension system.
A Pilot Study for Identifying Tasks and Degrees of Visual Fidelity for Applications of Head Mounted Display Systems for Construction
Soto, Cecilia Irene (Virginia Tech, 2017-09-14)
The rise in technology and reduced costs has led to more research on the use of Augmented Reality (AR). However, applications for AR Head Mounted Display (HMD) systems are still being defined. AR HMD systems have potential to help users interact and experience information in a way that could improve their performance. In the construction sector, workers use black and white construction level of detail drawings for assembly and inspection tasks. For this thesis, Microsoft HoloLens was used in an experiment to see the effects of AR models on user performance and comprehension. There were three conditions for this study, two of the conditions used AR model displays and the third condition used a traditional paper drawing of the model. This study measured participants' accuracy and comprehension of the model presented to them. The conclusion of this thesis is that using 3D AR models may improve participants' comprehension of construction drawings.
Quantification of Demand-Supply Balancing Capacity among Prosumers and Consumers: Community Self-Sufficiency Assessment for Energy Trading
Afzalan, Milad; Jazizadeh, Farrokh (MDPI, 2021-07-17)
With the increased adoption of distributed energy resources (DERs) and renewables, such as solar panels at the building level, consumers turn into prosumers with generation capability to supply their on-site demand. The temporal complementarity between supply and demand at the building level provides opportunities for energy exchange between prosumers and consumers towards community-level self-sufficiency. Investigating different aspects of community-level energy exchange in cyber and physical layers has received attention in recent years with the increase in renewables adoption. In this study, we have presented an in-depth investigation into the impact of energy exchange through the quantification of temporal energy deficit–surplus complementarity and its associated self-sufficiency capacities by considering the impact of variations in community infrastructure configurations, variations in household energy use patterns, and the potential for user adaptation for load flexibility. To this end, we have adopted a data-driven simulation using real-world data from a case-study neighborhood consisting of ~250 residential buildings in Austin, TX with a mix of prosumers and consumers and detailed data on decentralized DERs. By accounting for the uncertainties in energy consumption patterns across households, different levels of PV and energy storage integration, and different modalities of user adaptation, various scenarios of operations were simulated. The analysis showed that with PV integration of more than 75%, energy exchange could result in self-sufficiency for the entire community during peak generation hours from 11 a.m. to 3 p.m. However, there are limited opportunities for energy exchange during later times with PV-standalone systems. As a potential solution, leveraging building-level storage or user adaptation for load shedding/shifting during the 2-h low-generation timeframe (i.e., 5–7 p.m.) was shown to increase community self-sufficiency during generation hours by 17% and 5–10%, respectively, to 83% and 71–76%.
Resilience-based Operational Analytics of Transportation Infrastructure: A Data-driven Approach for Smart Cities
Khaghani, Farnaz (Virginia Tech, 2020-07-01)
Studying recurrent mobility perturbations, such as traffic congestions, is a major concern of engineers, planners, and authorities as they not only bring about delay and inconvenience but also have consequent negative impacts like greenhouse gas emission, increase in fuel consumption, or safety issues. In this dissertation, we proposed using the resilience concept, which has been commonly used for assessing the impact of extreme events and disturbances on the transportation system, for high-probability low impact (HPLI) events to (a) provide a performance assessment framework for transportation systems' response to traffic congestions, (b) investigate the role of transit modes in the resilience of urban roadways to congestion, and (c) study the impact of network topology on the resilience of roadways functionality performance. We proposed a multi-dimensional approach to characterize the resilience of urban transportation roadways for recurrent congestions. The resilience concept could provide an effective benchmark for comparative performance and identifying the behavior of the system in the discharging process in congestion. To this end, we used a Data Envelopment Analysis (DEA) approach to integrate multiple resilience-oriented attributes to estimate the efficiency (resilience) of the frontier in roadways. Our results from an empirical study on California highways through the PeMS data have shown the potential of the multi-dimensional approach in increasing information gain and differentiating between the severity of congestion across a transportation network. Leveraging this resilience-based characterization of recurrent disruptions, in the second study, we investigated the role of multi-modal resourcefulness of urban transportation systems, in terms of diversity and equity, on the resilience of roadways to daily-based congestions. We looked at the physical infrastructure availability and distribution (i.e. diversity) and accessibility and coverage to capture socio-economic factors (i.e. equity) to more comprehensively understand the role of resourcefulness in resilience. We conducted this investigation by using a GPS dataset of taxi trips in the Washington DC metropolitan area in 2017. Our results demonstrated the strong correlation of trips' resilience with transportation equity and to a lesser extent with transportation diversity. Furthermore, we learned the impact of equity and diversity can mostly be seen at the recovery stage of resilience. In the third study, we looked at another aspect of transportation supply in urban areas, spatial configuration, and topology. The goal of this study was to investigate the role of network topology and configuration on resilience to congestion. We used OSMnx, a toolkit for street network analysis based on the data from OpenStreetMap, to model and analyze the urban roadways network configurations. We further employed a multidimensional visualization strategy using radar charts to compare the topology of street networks on a single graphic. Leveraging the geometric descriptors of radar charts, we used the compactness and Jaccard Index to quantitatively compare the topology profiles. We use the same taxi trips dataset used in the second study to characterize resilience and identify the correlation with network topology. The results indicated a strong correlation between resilience and betweenness centrality, diameter, and Page Rank among other features of a transportation network. We further looked at the capacity of roadways as a common cause for the strong correlation between network features and resilience. We found that the strong correlation of link-related features such as diameter could be due to their role in capacity and have a common cause with resilience.

Browsing by Author "Jazizadeh, Farrokh"

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