@article{pesantez_wackerman_stillwell_2023, title={Analysis of single- and multi-family residential electricity consumption in a large urban environment: Evidence from Chicago, IL}, url={https://doi.org/10.1016/j.scs.2022.104250}, DOI={10.1016/j.scs.2022.104250}, abstractNote={Natural and human-caused extreme events can alter residential electricity demand in urban areas and stress the electricity grid, with different types of residential electricity consumers exhibiting different consumption patterns. Residential electricity demands have been widely analyzed considering single-family consumers; however, multi-family consumption patterns remain comparatively understudied. The deployment of smart electricity meters enables the identification of single- and multi-family residential electricity consumption patterns at high temporal resolution. Using smart electricity meter data for the greater Chicago area, we compare electricity demand profiles reported by smart meters from single- and multi-family consumers in a large and diverse urban environment to understand residential electricity patterns better. Our study comprehensively analyzes the daily electricity demand profiles of these two types of residential consumers to identify peak electricity consumption times and magnitudes. Results show that the electricity demand of both residential end-users follows similar time of use patterns, and single-family users approximately double the demand of multi-family users on a per household basis. We also present predictive models of the electricity demand with socioeconomic data at the zip code level. Predictive model results show that multiple linear regression models explain up to 62% and 41% of the mean daily electricity (MDE) demand of single- and multi-family users, respectively. The median age of occupants, percent age 65 and older, mean commute time, and percent high school or higher education are statistically significant predictors of the MDE demand of single-family users, with percent high school or higher education having the highest relative importance. Similarly, median building age, percent multi-family, percent female, median age of occupants, and mean commute time are statistically significant predictors of multi-family electricity consumption, with median age of occupants having the highest relative importance. Modeling electricity demand to uncover differences between single- and multi-family residential electricity demands can assist city planners and utility managers to develop tailored demand management strategies.}, journal={Sustainable Cities and Society}, author={Pesantez, Jorge E. and Wackerman, Grace E. and Stillwell, Ashlynn S.}, year={2023}, month={Jan} }
 @article{daniel_pesantez_letzgus_fasaee_alghamdi_berglund_mahinthakumar_cominola_2022, title={A Sequential Pressure-Based Algorithm for Data-Driven Leakage Identification and Model-Based Localization in Water Distribution Networks}, volume={148}, ISSN={["1943-5452"]}, url={https://doi.org/10.1061/(ASCE)WR.1943-5452.0001535}, DOI={10.1061/(ASCE)WR.1943-5452.0001535}, abstractNote={: Leakages in water distribution networks (WDNs) are estimated to globally cost 39 billion USD = year and cause water and revenue losses, infrastructure degradation, and other cascading effects. Their impacts can be prevented and mitigated with prompt identification and accurate leak localization. In this work, we propose the leakage identification and localization algorithm (LILA), a pressure-based algorithm for data-driven leakage identification and model-based localization in WDNs. First, LILA identifies potential leakages via semisupervised linear regression of pairwise sensor pressure data and provides the location of their nearest sensors. Second, LILA locates leaky pipes relying on an initial set of candidate pipes and a simulation-based optimization framework with iterative linear and mixed-integer linear programming. LILA is tested on data from the L-Town network devised for the Battle of Leakage Detection and Isolation Methods. Results show that LILA can identify all leakages included in the data set and locate them within a maximum distance of 374 m from their real location. Abrupt leakages are identified immediately or within 2 h, while more time is required to raise alarms on incipient leakages. DOI: 10.1061/(ASCE) WR.1943-5452.0001535. © 2022 American Society of Civil Engineers.}, number={6}, journal={JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT}, publisher={American Society of Civil Engineers (ASCE)}, author={Daniel, Ivo and Pesantez, Jorge and Letzgus, Simon and Fasaee, Mohammad Ali Khaksar and Alghamdi, Faisal and Berglund, Emily and Mahinthakumar, G. and Cominola, Andrea}, year={2022}, month={Jun} }
 @article{fasaee_pesantez_pieper_ling_benham_edwards_berglund_2022, title={Developing early warning systems to predict water lead levels in tap water for private systems}, volume={221}, ISSN={["1879-2448"]}, url={https://doi.org/10.1016/j.watres.2022.118787}, DOI={10.1016/j.watres.2022.118787}, abstractNote={Lead is a chemical contaminant that threatens public health, and high levels of lead have been identified in drinking water at locations across the globe. Under-served populations that use private systems for drinking water supplies may be at an elevated level of risk because utilities and governing agencies are not responsible for ensuring that lead levels meet the Lead and Copper Rule at these systems. Predictive models that can be used by residents to assess water quality threats in their households can create awareness of water lead levels (WLLs). This research explores and compares the use of statistical models (i.e., Bayesian Belief classifiers) and machine learning models (i.e., ensemble of decision trees) for predicting WLLs. Models are developed using a dataset collected by the Virginia Household Water Quality Program (VAHWQP) at approximately 8000 households in Virginia during 2012-2017. The dataset reports laboratory-tested water quality parameters at households, location information, and household and plumbing characteristics, including observations of water odor, taste, discoloration. Some water quality parameters, such as pH, iron, and copper, can be measured at low resolution by residents using at-home water test kits and can be used to predict risk of WLLs. The use of at-home water quality test kits was simulated through the discretization of water quality parameter measurements to match the resolution of at-home water quality test kits and the introduction of error in water quality readings. Using this approach, this research demonstrates that low-resolution data collected by residents can be used as input for models to estimate WLLs. Model predictability was explored for a set of at-home water quality test kits that observe a variety of water quality parameters and report parameters at a range of resolutions. The effects of the timing of water sampling (e.g., first-draw vs. flushed samples) and error in kits on model error were tested through simulations. The prediction models developed through this research provide a set of tools for private well users to assess the risk of lead contamination. Models can be implemented as early warning systems in citizen science and online platforms to improve awareness of drinking water threats.}, journal={WATER RESEARCH}, author={Fasaee, Mohammad Ali Khaksar and Pesantez, Jorge and Pieper, Kelsey J. and Ling, Erin and Benham, Brian and Edwards, Marc and Berglund, Emily}, year={2022}, month={Aug} }
 @article{berglund_buchberger_cunha_faust_giacomoni_goharian_kleiner_lee_ostfeld_pasha_et al._2022, title={Effects of the COVID-19 Pandemic on Water Utility Operations and Vulnerability}, volume={148}, ISSN={["1943-5452"]}, DOI={10.1061/(ASCE)WR.1943-5452.0001560}, abstractNote={The COVID-19 pandemic affected the operation of water utilities across the world. In the context of utilities, new protocols were needed to ensure that employees can work safely, and that water service is not interrupted. This study reports on how the operations of 27 water utilities worldwide were affected by the COVID-19 pandemic. Interviews were conducted between June and October 2020;respondents represent utilities that varied in population size, location, and customer composition (e.g., residential, industrial, commercial, institutional, and university customers). Survey questions focused on the effects of the pandemic on water system operation, demand, revenues, system vulnerabilities, and the use and development of emergency response plans (ERPs). Responses indicate that significant changes in water system operations were implemented to ensure that water utility employees could continue working while maintaining safe social distancing or alternatively working from home. A total of 23 of 27 utilities reported small changes in demand volumes and patterns, which can lead to some changes in water infrastructure operations and water quality. Utilities experienced a range of impacts on finances, where most utilities discussed small decreases in revenues, with a few reporting more drastic impacts. The pandemic revealed new system vulnerabilities, including supply chain management, capacity of staff to perform certain functions remotely, and finances. Some utilities applied existing guidance developed through ERPs with slight modifications, other utilities developed new ERPs to specifically address unique conditions induced by the pandemic, and a few utilities did not use or reference their existing ERPs to change operations. Many utilities suggested that lessons learned would be used in future ERPs, such as personnel training on pandemic risk management or annual mock exercises for preparing employees to better respond to emergencies.}, number={6}, journal={JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT}, author={Berglund, Emily Zechman and Buchberger, Steven and Cunha, Maria and Faust, Kasey M. and Giacomoni, Marcio and Goharian, Erfan and Kleiner, Yehuda and Lee, Juneseok and Ostfeld, Avi and Pasha, Fayzul and et al.}, year={2022}, month={Jun} }
 @article{pesantez_behr_sciaudone_2022, title={Importance of Pre-Storm Morphological Factors in Determination of Coastal Highway Vulnerability}, volume={10}, ISSN={["2077-1312"]}, url={https://doi.org/10.3390/jmse10081158}, DOI={10.3390/jmse10081158}, abstractNote={This work considers a database of pre-storm morphological factors and documented impacts along a coastal roadway. Impacts from seven storms, including sand overwash and pavement damage, were documented via aerial photography. Pre-storm topography was examined to parameterize the pre-storm morphological factors likely to control whether stormwater levels and waves impact the road. Two machine learning techniques, K-nearest neighbors (KNN) and ensemble of decision trees (EDT), were employed to identify the most critical pre-storm morphological factors in determining the road vulnerability, expressed as a binary variable to impact storms. Pre-processing analysis was conducted with a correlation analysis of the predictors’ data set and feature selection subroutine for the KNN classifier. The EDTs were built directly from the data set, and feature importance estimates were reported for all storm events. Both classifiers report the distances from roadway edge-of-pavement to the dune toe and ocean as the most important predictors of most storms. For storms approaching from the bayside, the width of the barrier island was the second most important factor. Other factors of importance included elevation of the dune toe, distance from the edge of pavement to the ocean shoreline, shoreline orientation (relative to predominant wave angle), and beach slope. Compared to previously reported optimization techniques, both machine learning methods improved using pre-storm morphological data to classify highway vulnerability based on storm impacts.}, number={8}, journal={JOURNAL OF MARINE SCIENCE AND ENGINEERING}, publisher={MDPI AG}, author={Pesantez, Jorge E. and Behr, Adam and Sciaudone, Elizabeth}, year={2022}, month={Aug} }
 @article{pesantez_alghamdi_sabu_mahinthakumar_berglund_2022, title={Using a digital twin to explore water infrastructure impacts during the COVID-19 pandemic}, volume={77}, ISSN={["2210-6715"]}, DOI={10.1016/j.scs.2021.103520}, abstractNote={During the coronavirus disease 2019 (COVID-19) pandemic, the daily pattern of activities changed dramatically for people across the globe, as they socially distanced and worked remotely. Changes in daily routines created changes in water consumption patterns. Significant changes in water demands can affect the operation of water distribution systems, resulting in new patterns of flow, with implications for water age, pressure, and energy consumption. This research develops a digital twin to couple Advanced Metering Infrastructure (AMI) data with a hydraulic model to assess impacts on infrastructure due to changes in water demands associated with the COVID-19 pandemic for a case study. Using 2019 and COVID-19 modeling scenarios, the hydraulic model was executed to evaluate changes to water quality based on water age, pressure across nodes in the network, and the energy required by the system to distribute potable water. A water supply interruption event was modeled as a water main break to assess network resiliency for 2019 and COVID-19 demands. A digital twin provides the capabilities to explore and visualize emerging consumption patterns and their effects on the functioning of water systems, providing valuable analyses for water utility managers and insight for optimizing infrastructure operations and planning for long-term impacts.}, journal={SUSTAINABLE CITIES AND SOCIETY}, author={Pesantez, Jorge E. and Alghamdi, Faisal and Sabu, Shreya and Mahinthakumar, G. and Berglund, Emily Zechman}, year={2022}, month={Feb} }
 @article{pesantez_wackerman_stillwell_2021, title={Smart Meter Data to Analyze Electricity Demand from Single- and Multi-family Consumers in a Diverse Urban Environment}, url={https://doi.org/10.1002/essoar.10508531.1}, DOI={10.1002/essoar.10508531.1}, abstractNote={Earth and Space Science Open Archive Presented WorkOpen AccessYou are viewing the latest version by default [v1]Smart Meter Data to Analyze Electricity Demand from Single- and Multi-family Consumers in a Diverse Urban EnvironmentAuthorsJorgePesanteziDGraceWackermanAshlynnStillwellSee all authors Jorge PesanteziDCorresponding Author• Submitting AuthorUniversity of Illinois at Urbana-ChampaigniDhttps://orcid.org/0000-0002-1537-6006view email addressThe email was not providedcopy email addressGrace WackermanUniversity of Illinois at Urbana Champaignview email addressThe email was not providedcopy email addressAshlynn StillwellUniversity of Illinois at Urbana Champaignview email addressThe email was not providedcopy email address}, author={Pesantez, Jorge and Wackerman, Grace and Stillwell, Ashlynn}, year={2021}, month={Oct} }
 @article{ramsey_pesantez_fasaee_dicarlo_monroe_berglund_2020, title={A Smart Water Grid for Micro-Trading Rainwater: Hydraulic Feasibility Analysis}, url={https://doi.org/10.3390/w12113075}, DOI={10.3390/w12113075}, abstractNote={Water availability is increasingly stressed in cities across the world due to population growth, which increases demands, and climate change, which can decrease supply. Novel water markets and water supply paradigms are emerging to address water shortages in the urban environment. This research develops a new peer-to-peer non-potable water market that allows households to capture, use, sell, and buy rainwater within a network of water users. A peer-to-peer non-potable water market, as envisioned in this research, would be enabled by existing and emerging technologies. A dual reticulation system, which circulates non-potable water, serves as the backbone for the water trading network by receiving water from residential rainwater tanks and distributing water to households for irrigation purposes. Prosumer households produce rainwater by using cisterns to collect and store rainwater and household pumps to inject rainwater into the network at sufficiently high pressures. The smart water grid would be enabled through an array of information and communication technologies that provide capabilities for automated and real-time metering of water flow, control of infrastructure, and trading between households. The goal of this manuscript is to explore and test the hydraulic feasibility of a micro-trading system through an agent-based modeling approach. Prosumer households are represented as agents that store rainwater and pump rainwater into the network; consumer households are represented as agents that withdraw water from the network for irrigation demands. An all-pipe hydraulic model is constructed and loosely coupled with the agent-based model to simulate network hydraulics. A set of scenarios are analyzed to explore how micro-trading performs based on the level of irrigation demands that could realistically be met through decentralized trading; pressure and energy requirements at prosumer households; pressure and water quality in the pipe network.}, journal={Water}, author={Ramsey, Elizabeth and Pesantez, Jorge and Fasaee, Mohammad Ali Khaksar and DiCarlo, Morgan and Monroe, Jacob and Berglund, Emily Zechman}, year={2020}, month={Nov} }
 @article{pesantez_berglund_2020, title={Demand-side Management of Peak Water Demands using Advanced Metering Infrastructure and Persuasive Games}, volume={3}, url={https://doi.org/10.5194/egusphere-egu2020-5876}, DOI={10.5194/egusphere-egu2020-5876}, abstractNote={
 <p>Residential water demands vary with a diurnal pattern, and peak hour demands lead to inefficiencies in the operation and management of urban water distribution systems. Peak demands generate immediate costs due to the energy requirements of pumping large volumes of water. If peak demands are not mitigated, large investments in infrastructure expansion are needed to support urban growth and economic development. Through data collection and communication approaches available through advanced metering infrastructure (AMI), demand-side management approaches could reduce peak demands. AMI data can be disaggregated to identify end uses that contribute to peak demands, and feedback about hourly use can be used to encourage demand shifting behaviors. Demand-side management implements technical approaches, such as retrofitting households with smart and water-efficient devices, and social approaches, such as dynamic water pricing, mandatory restrictions, and persuasive games that encourage voluntary participation. A community of households that shift demands can distribute the volume of water provision evenly over the hours of a day and reduce peak demands. While demand-side management strategies can reduce energy requirements associated with water supply and the need for new infrastructure development, demand management relies on the behaviors and decision-making of individuals, creating uncertainty in the emergent cost savings and infrastructure impacts. This research develops an agent-based modeling methodology to simulate the performance of demand-management approaches to reduce peak water demands. A persuasive game is simulated that implements a leaderboard to encourage cooperation and competition within and among neighborhoods of water users. Household agents receive points for shifting end-uses, based on the difficulty and water savings associated with end-user behaviors. Opinion dynamics simulate agents&#8217; information exchange using a leaderboard, which provides motivation for agents to increase individual and team scores. The methodology is applied for AMI data to test the effects of persuasive games on reducing peak demands.</p>
}, publisher={Copernicus GmbH}, author={Pesantez, Jorge and Berglund, Emily Zechman}, year={2020}, month={Mar} }
 @article{pesantez_berglund_mahinthakumar_2020, title={Geospatial and Hydraulic Simulation to Design District Metered Areas for Large Water Distribution Networks}, url={https://doi.org/10.1061/(ASCE)WR.1943-5452.0001243}, DOI={10.1061/(ASCE)WR.1943-5452.0001243}, abstractNote={AbstractWater distribution systems can be divided into district metered areas (DMAs) to improve their management. DMAs are individual service regions within a distribution system that have a define...}, journal={Journal of Water Resources Planning and Management}, author={Pesantez, Jorge E. and Berglund, Emily Zechman and Mahinthakumar, G.}, year={2020}, month={Jul} }
 @article{berglund_pesantez_rasekh_shafiee_sela_haxton_2020, title={Review of Modeling Methodologies for Managing Water Distribution Security}, volume={146}, url={https://doi.org/10.1061/(ASCE)WR.1943-5452.0001265}, DOI={10.1061/(ASCE)WR.1943-5452.0001265}, abstractNote={Water distribution systems are vulnerable to hazards that threaten water delivery, water quality, and physical and cybernetic infrastructure. Water utilities and managers are responsible for assessing and preparing for these hazards, and researchers have developed a range of computational frameworks to explore and identify strategies for what-if scenarios. This manuscript conducts a review of the literature to report on the state of the art in modeling methodologies that have been developed to support the security of water distribution systems. First, the major activities outlined in the emergency management framework are reviewed; the activities include risk assessment, mitigation, emergency preparedness, response, and recovery. Simulation approaches and prototype software tools are reviewed that have been developed by government agencies and researchers for assessing and mitigating four threat modes, including contamination events, physical destruction, interconnected infrastructure cascading failures, and cybernetic attacks. Modeling tools are mapped to emergency management activities, and an analysis of the research is conducted to group studies based on methodologies that are used and developed to support emergency management activities. Recommendations are made for research needs that will contribute to the enhancement of the security of water distribution systems.}, number={8}, journal={Journal of Water Resources Planning and Management}, publisher={American Society of Civil Engineers (ASCE)}, author={Berglund, Emily Zechman and Pesantez, Jorge E. and Rasekh, Amin and Shafiee, M. Ehsan and Sela, Lina and Haxton, Terranna}, year={2020}, month={Aug} }
 @article{berglund_monroe_ahmed_noghabaei_do_pesantez_khaksar fasaee_bardaka_han_proestos_et al._2020, title={Smart Infrastructure: A Vision for the Role of the Civil Engineering Profession in Smart Cities}, volume={26}, ISSN={1076-0342 1943-555X}, url={http://dx.doi.org/10.1061/(ASCE)IS.1943-555X.0000549}, DOI={10.1061/(ASCE)IS.1943-555X.0000549}, abstractNote={AbstractSmart city programs provide a range of technologies that can be applied to solve infrastructure problems associated with ageing infrastructure and increasing demands. The potential for infr...}, number={2}, journal={Journal of Infrastructure Systems}, publisher={American Society of Civil Engineers (ASCE)}, author={Berglund, Emily Zechman and Monroe, Jacob G. and Ahmed, Ishtiak and Noghabaei, Mojtaba and Do, Jinung and Pesantez, Jorge E. and Khaksar Fasaee, Mohammad Ali and Bardaka, Eleni and Han, Kevin and Proestos, Giorgio T. and et al.}, year={2020}, month={Jun}, pages={03120001} }
 @article{pesantez_berglund_kaza_2020, title={Smart meters data for modeling and forecasting water demand at the user-level}, volume={125}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85078400063&partnerID=MN8TOARS}, DOI={10.1016/j.envsoft.2020.104633}, abstractNote={Smart meters installed at the user-level provide a new data source for managing water infrastructure. This research explores the use of machine learning methods, including Random Forests (RFs), Artificial Neural Networks (ANNs), and Support Vector Regression (SVR) to forecast hourly water demand at 90 accounts using smart-metered data. Demands are predicted using lagged demand, seasonality, weather, and household characteristics. Time-series clustering is applied to delineate data based on the time of day and day of the week, which improves model performance. Two modeling approaches are compared. Individual models are developed separately for each meter, and a Group model is trained using a data set of multiple meters. Individual models predict demands at meters in the original data set with lower error than Group models, while the Group model predicts demands at new meters with lower error than Individual models. Results demonstrate that RF and ANN perform better than SVR across all scenarios.}, journal={Environmental Modelling and Software}, author={Pesantez, J.E. and Berglund, E.Z. and Kaza, N.}, year={2020} }
 @article{pesantez_berglund_mahinthakumar_2019, title={Multiphase Procedure to Design District Metered Areas for Water Distribution Networks}, volume={145}, ISSN={["1943-5452"]}, url={https://doi.org/10.1061/(ASCE)WR.1943-5452.0001095}, DOI={10.1061/(ASCE)WR.1943-5452.0001095}, abstractNote={AbstractDividing a water distribution network into subsystems can improve the efficiency and ease of achieving management goals. Subsystems or district metered areas (DMAs) are isolated control zon...}, number={8}, journal={JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT}, publisher={American Society of Civil Engineers (ASCE)}, author={Pesantez, Jorge E. and Berglund, Emily Zechman and Mahinthakumar, G.}, year={2019}, month={Aug} }