The Experts below are selected from a list of 23199 Experts worldwide ranked by ideXlab platform
Steven M. Quiring - One of the best experts on this subject based on the ideXlab platform.
-
predicting thunderstorm induced Power Outages to support utility restoration
IEEE Transactions on Power Systems, 2019Co-Authors: Elnaz Kabir, Seth D Guikema, Steven M. QuiringAbstract:Strong thunderstorms have substantial impacts on Power systems, posing risks and inconveniences due to Power Outages. Developing models predicting the Outages before a storm is a high priority to support restoration planning. However, most Power Outage data are zero-inflated, which results in some challenges in predictive modeling such as bias and inaccuracy. Power Outages are also stochastic and there always exists irreducible variability in Outage predictions. The goal is to develop models to overcome the challenges caused by zero-inflation and to accurately estimate Power Outages in terms of probability distributions to better address inherent stochasticity and uncertainty in predictions. This paper proposes a novel approach integrating mixture models with resampling and cost-sensitive learning for predicting the probability distribution for the number of Outages. Validating the models using Power Outage data, we demonstrate that our approach offers more accurate point and probabilistic predictions compared to traditional approaches, better supporting utility restoration planning.
-
Improving Hurricane Power Outage Prediction Models Through the Inclusion of Local Environmental Factors.
Risk analysis : an official publication of the Society for Risk Analysis, 2016Co-Authors: D. Brent Mcroberts, Steven M. Quiring, Seth D GuikemaAbstract:Tropical cyclones can significantly damage the electrical Power system, so an accurate spatiotemporal forecast of Outages prior to landfall can help utilities to optimize the Power restoration process. The purpose of this article is to enhance the predictive accuracy of the Spatially Generalized Hurricane Outage Prediction Model (SGHOPM) developed by Guikema et al. (2014). In this version of the SGHOPM, we introduce a new two-step prediction procedure and increase the number of predictor variables. The first model step predicts whether or not Outages will occur in each location and the second step predicts the number of Outages. The SGHOPM environmental variables of Guikema et al. (2014) were limited to the wind characteristics (speed and duration of strong winds) of the tropical cyclones. This version of the model adds elevation, land cover, soil, precipitation, and vegetation characteristics in each location. Our results demonstrate that the use of a new two-step Outage prediction model and the inclusion of these additional environmental variables increase the overall accuracy of the SGHOPM by approximately 17%.
-
Forecasting hurricane-induced Power Outage durations
Natural Hazards, 2014Co-Authors: Roshanak Nateghi, Seth D Guikema, Steven M. QuiringAbstract:Accurate estimates of the duration of Power Outages caused by hurricanes prior to landfall are valuable for utility companies and government agencies that wish to plan and optimize their restoration efforts. Accurate pre-storm estimates are also important information for customers and operators of other infrastructures systems, who rely heavily on electricity. Traditionally, utilities make restoration plans based on managerial judgment and experience. However, skillful Outage forecast models are conducive to improved decision-making practices by utilities and can greatly enhance storm preparation and restoration management procedures of Power companies and emergency managers. This paper presents a novel statistical approach for estimating Power Outage durations that is 87 % more accurate than existing models in the literature. The Power Outage duration models are developed and carefully validated for Outages caused by Hurricanes Dennis, Katrina, and Ivan in a central Gulf Coast state. This paper identifies the key variables in predicting hurricane-induced Outage durations and their degree of influence on predicting Outage restoration for the utility company service area used as our case study.
-
Power Outage Estimation for Tropical Cyclones: Improved Accuracy with Simpler Models
Risk Analysis, 2014Co-Authors: Roshanak Nateghi, Seth D Guikema, Steven M. QuiringAbstract:In this article, we discuss an Outage-forecasting model that we have developed. This model uses very few input variables to estimate hurricane-induced Outages prior to landfall with great predictive accuracy. We also show the results for a series of simpler models that use only publicly available data and can still estimate Outages with reasonable accuracy. The intended users of these models are emergency response planners within Power utilities and related government agencies. We developed our models based on the method of random forest, using data from a Power distribution system serving two states in the Gulf Coast region of the United States. We also show that estimates of system reliability based on wind speed alone are not sufficient for adequately capturing the reliability of system components. We demonstrate that a multivariate approach can produce more accurate Power Outage predictions.
-
incorporating hurricane forecast uncertainty into a decision support application for Power Outage modeling
Bulletin of the American Meteorological Society, 2014Co-Authors: Steven M. Quiring, Andrea B Schumacher, Seth D GuikemaAbstract:A variety of decision-support systems, such as those employed by energy and utility companies, use the National Hurricane Center (NHC) forecasts of track and intensity to inform operational decision making as a hurricane approaches. Track and intensity forecast errors, especially just prior to landfall, can substantially impact the accuracy of these decision-support systems. This study quantifies how forecast errors can influence the results of a Power Outage model, highlighting the importance of considering uncertainty when using hurricane forecasts in decision-support applications. An ensemble of 1,000 forecast realizations is generated using the Monte Carlo wind speed probability model for Hurricanes Dennis, Ivan, and Katrina. The Power Outage model was run for each forecast realization to predict the spatial distribution of Power Outages. Based on observed Power Outage data from a Gulf Coast utility company, the authors found that in all three cases the ensemble average was a better predictor of Power...
Seth D Guikema - One of the best experts on this subject based on the ideXlab platform.
-
predicting thunderstorm induced Power Outages to support utility restoration
IEEE Transactions on Power Systems, 2019Co-Authors: Elnaz Kabir, Seth D Guikema, Steven M. QuiringAbstract:Strong thunderstorms have substantial impacts on Power systems, posing risks and inconveniences due to Power Outages. Developing models predicting the Outages before a storm is a high priority to support restoration planning. However, most Power Outage data are zero-inflated, which results in some challenges in predictive modeling such as bias and inaccuracy. Power Outages are also stochastic and there always exists irreducible variability in Outage predictions. The goal is to develop models to overcome the challenges caused by zero-inflation and to accurately estimate Power Outages in terms of probability distributions to better address inherent stochasticity and uncertainty in predictions. This paper proposes a novel approach integrating mixture models with resampling and cost-sensitive learning for predicting the probability distribution for the number of Outages. Validating the models using Power Outage data, we demonstrate that our approach offers more accurate point and probabilistic predictions compared to traditional approaches, better supporting utility restoration planning.
-
Improving Hurricane Power Outage Prediction Models Through the Inclusion of Local Environmental Factors.
Risk analysis : an official publication of the Society for Risk Analysis, 2016Co-Authors: D. Brent Mcroberts, Steven M. Quiring, Seth D GuikemaAbstract:Tropical cyclones can significantly damage the electrical Power system, so an accurate spatiotemporal forecast of Outages prior to landfall can help utilities to optimize the Power restoration process. The purpose of this article is to enhance the predictive accuracy of the Spatially Generalized Hurricane Outage Prediction Model (SGHOPM) developed by Guikema et al. (2014). In this version of the SGHOPM, we introduce a new two-step prediction procedure and increase the number of predictor variables. The first model step predicts whether or not Outages will occur in each location and the second step predicts the number of Outages. The SGHOPM environmental variables of Guikema et al. (2014) were limited to the wind characteristics (speed and duration of strong winds) of the tropical cyclones. This version of the model adds elevation, land cover, soil, precipitation, and vegetation characteristics in each location. Our results demonstrate that the use of a new two-step Outage prediction model and the inclusion of these additional environmental variables increase the overall accuracy of the SGHOPM by approximately 17%.
-
Forecasting hurricane-induced Power Outage durations
Natural Hazards, 2014Co-Authors: Roshanak Nateghi, Seth D Guikema, Steven M. QuiringAbstract:Accurate estimates of the duration of Power Outages caused by hurricanes prior to landfall are valuable for utility companies and government agencies that wish to plan and optimize their restoration efforts. Accurate pre-storm estimates are also important information for customers and operators of other infrastructures systems, who rely heavily on electricity. Traditionally, utilities make restoration plans based on managerial judgment and experience. However, skillful Outage forecast models are conducive to improved decision-making practices by utilities and can greatly enhance storm preparation and restoration management procedures of Power companies and emergency managers. This paper presents a novel statistical approach for estimating Power Outage durations that is 87 % more accurate than existing models in the literature. The Power Outage duration models are developed and carefully validated for Outages caused by Hurricanes Dennis, Katrina, and Ivan in a central Gulf Coast state. This paper identifies the key variables in predicting hurricane-induced Outage durations and their degree of influence on predicting Outage restoration for the utility company service area used as our case study.
-
Power Outage Estimation for Tropical Cyclones: Improved Accuracy with Simpler Models
Risk Analysis, 2014Co-Authors: Roshanak Nateghi, Seth D Guikema, Steven M. QuiringAbstract:In this article, we discuss an Outage-forecasting model that we have developed. This model uses very few input variables to estimate hurricane-induced Outages prior to landfall with great predictive accuracy. We also show the results for a series of simpler models that use only publicly available data and can still estimate Outages with reasonable accuracy. The intended users of these models are emergency response planners within Power utilities and related government agencies. We developed our models based on the method of random forest, using data from a Power distribution system serving two states in the Gulf Coast region of the United States. We also show that estimates of system reliability based on wind speed alone are not sufficient for adequately capturing the reliability of system components. We demonstrate that a multivariate approach can produce more accurate Power Outage predictions.
-
incorporating hurricane forecast uncertainty into a decision support application for Power Outage modeling
Bulletin of the American Meteorological Society, 2014Co-Authors: Steven M. Quiring, Andrea B Schumacher, Seth D GuikemaAbstract:A variety of decision-support systems, such as those employed by energy and utility companies, use the National Hurricane Center (NHC) forecasts of track and intensity to inform operational decision making as a hurricane approaches. Track and intensity forecast errors, especially just prior to landfall, can substantially impact the accuracy of these decision-support systems. This study quantifies how forecast errors can influence the results of a Power Outage model, highlighting the importance of considering uncertainty when using hurricane forecasts in decision-support applications. An ensemble of 1,000 forecast realizations is generated using the Monte Carlo wind speed probability model for Hurricanes Dennis, Ivan, and Katrina. The Power Outage model was run for each forecast realization to predict the spatial distribution of Power Outages. Based on observed Power Outage data from a Gulf Coast utility company, the authors found that in all three cases the ensemble average was a better predictor of Power...
Roshanak Nateghi - One of the best experts on this subject based on the ideXlab platform.
-
Data on major Power Outage events in the continental U.S.
Data in Brief, 2018Co-Authors: Sayanti Mukherjee, Roshanak Nateghi, Makarand HastakAbstract:Abstract This paper presents the data that is used in the article entitled “A Multi-Hazard Approach to Assess Severe Weather-Induced Major Power Outage Risks in the U.S.” (Mukherjee et al., 2018) [1] . The data described in this article pertains to the major Outages witnessed by different states in the continental U.S. during January 2000–July 2016. As defined by the Department of Energy, the major Outages refer to those that impacted atleast 50,000 customers or caused an unplanned firm load loss of atleast 300 MW. Besides major Outage data, this article also presents data on geographical location of the Outages, date and time of the Outages, regional climatic information, land-use characteristics, electricity consumption patterns and economic characteristics of the states affected by the Outages. This dataset can be used to identify and analyze the historical trends and patterns of the major Outages and identify and assess the risk predictors associated with sustained Power Outages in the continental U.S. as described in Mukherjee et al. [1] .
-
A multi-hazard approach to assess severe weather-induced major Power Outage risks in the U.S.
Reliability Engineering & System Safety, 2018Co-Authors: Sayanti Mukherjee, Roshanak Nateghi, Makarand HastakAbstract:Abstract Severe weather-induced Power Outages affect millions of people and cost billions of dollars of economic losses each year. The National Association of Regulatory Utility Commissioners have recently highlighted the importance of building electricity sector's resilience, and thereby enhancing service-security and long-term economic benefits. In this paper, we propose a multi-hazard approach to characterize the key predictors of severe weather-induced sustained Power Outages. We developed a two-stage hybrid risk estimation model, leveraging algorithmic data-mining techniques. We trained our risk models using publicly available information on historical major Power Outages, socio-economic data, state-level climatological observations, electricity consumption patterns and land-use data. Our results suggest that Power Outage risk is a function of various factors such as the type of natural hazard, expanse of overhead T&D systems, the extent of state-level rural versus urban areas, and potentially the levels of investments in operations/maintenance activities (e.g., tree-trimming, replacing old equipment, etc.). The proposed framework can help state regulatory commissions make risk-informed resilience investment decisions.
-
Forecasting hurricane-induced Power Outage durations
Natural Hazards, 2014Co-Authors: Roshanak Nateghi, Seth D Guikema, Steven M. QuiringAbstract:Accurate estimates of the duration of Power Outages caused by hurricanes prior to landfall are valuable for utility companies and government agencies that wish to plan and optimize their restoration efforts. Accurate pre-storm estimates are also important information for customers and operators of other infrastructures systems, who rely heavily on electricity. Traditionally, utilities make restoration plans based on managerial judgment and experience. However, skillful Outage forecast models are conducive to improved decision-making practices by utilities and can greatly enhance storm preparation and restoration management procedures of Power companies and emergency managers. This paper presents a novel statistical approach for estimating Power Outage durations that is 87 % more accurate than existing models in the literature. The Power Outage duration models are developed and carefully validated for Outages caused by Hurricanes Dennis, Katrina, and Ivan in a central Gulf Coast state. This paper identifies the key variables in predicting hurricane-induced Outage durations and their degree of influence on predicting Outage restoration for the utility company service area used as our case study.
-
Power Outage Estimation for Tropical Cyclones: Improved Accuracy with Simpler Models
Risk Analysis, 2014Co-Authors: Roshanak Nateghi, Seth D Guikema, Steven M. QuiringAbstract:In this article, we discuss an Outage-forecasting model that we have developed. This model uses very few input variables to estimate hurricane-induced Outages prior to landfall with great predictive accuracy. We also show the results for a series of simpler models that use only publicly available data and can still estimate Outages with reasonable accuracy. The intended users of these models are emergency response planners within Power utilities and related government agencies. We developed our models based on the method of random forest, using data from a Power distribution system serving two states in the Gulf Coast region of the United States. We also show that estimates of system reliability based on wind speed alone are not sufficient for adequately capturing the reliability of system components. We demonstrate that a multivariate approach can produce more accurate Power Outage predictions.
-
comparison and validation of statistical methods for predicting Power Outage durations in the event of hurricanes
Risk Analysis, 2011Co-Authors: Roshanak Nateghi, Seth D Guikema, Steven M. QuiringAbstract:This article compares statistical methods for modeling Power Outage durations during hurricanes and examines the predictive accuracy of these methods. Being able to make accurate predictions of Power Outage durations is valuable because the information can be used by utility companies to plan their restoration efforts more efficiently. This information can also help inform customers and public agencies of the expected Outage times, enabling better collective response planning, and coordination of restoration efforts for other critical infrastructures that depend on electricity. In the long run, Outage duration estimates for future storm scenarios may help utilities and public agencies better allocate risk management resources to balance the disruption from hurricanes with the cost of hardening Power systems. We compare the out-of-sample predictive accuracy of five distinct statistical models for estimating Power Outage duration times caused by Hurricane Ivan in 2004. The methods compared include both regression models (accelerated failure time (AFT) and Cox proportional hazard models (Cox PH)) and data mining techniques (regression trees, Bayesian additive regression trees (BART), and multivariate additive regression splines). We then validate our models against two other hurricanes. Our results indicate that BART yields the best prediction accuracy and that it is possible to predict Outage durations with reasonable accuracy. Language: en
Adam Rose - One of the best experts on this subject based on the ideXlab platform.
-
economic consequence analysis of electric Power infrastructure disruptions general equilibrium approaches
Energy Economics, 2020Co-Authors: Ian Sue Wing, Adam RoseAbstract:Abstract We develop a stylized two-sector analytical general equilibrium model that elucidates mechanisms of adjustment to widespread, long-duration electric Power disruptions. Algebraic solutions illustrate the relative importance of resilience through producer and consumer input substitutability and mitigation investment in backup infrastructure capacity in moderating the economy-wide costs of Outages. Simulations of the impacts of a two-week Power Outage on California's Bay Area economy using both the analytical model and a computational general equilibrium model yield welfare losses that are substantially smaller than stated-preference estimates of willingness to pay. Results highlight the role of resilience in moderating consequences of energy supply shocks.
-
economic consequence analysis of electric Power infrastructure disruptions an analytical general equilibrium approach
Social Science Research Network, 2018Co-Authors: Ian Sue Wing, Adam RoseAbstract:We develop a stylized two-sector analytical general equilibrium model of regional economic adjustment to widespread long-duration electric Power Outages. Algebraic solutions highlight the relative importance of costless inherent resilience and deliberate costly investment in backup infrastructure capacity in moderating the economy-wide costs of electricity disruptions. Numerically parameterizing the model to simulate the effects of a two-week Power Outage on California’s Bay Area economy yields modest welfare losses that are substantially smaller than stated-preference estimates of willingness to pay. The model represents a transparent approach to analyzing the tradeoffs between reliability and resilience in electric Power provision and utilization.
Michelle L Bell - One of the best experts on this subject based on the ideXlab platform.
-
lights out impact of the august 2003 Power Outage on mortality in new york ny
Epidemiology, 2012Co-Authors: Brooke G Anderson, Michelle L BellAbstract:Power blackouts are likely to increase as growing energy use stresses aging Power grids.1 Climate change could also increase Power Outages,2 and energy infrastructure may be targeted by national security threats.1 However, we know little about how Power Outages affect health. Studies have reported increases in accidental deaths and injuries, including carbon monoxide (CO) poisoning,3,4 food poisoning,5 and hypothermia3 during Power Outages, as well as increased respiratory hospitalizations.6 Calls to emergency services7 and poison control8 can increase, although this may reflect the inability to contact primary health providers.7 While one article showed graphically that mortality increased during the New York 2003 blackout,6 to our knowledge, there has been no analysis of the effect of blackouts on total mortality, including non-accidental (i.e., disease-related) causes. Furthermore, estimated death tolls for disasters (e.g., hurricanes) generally include only directly attributable deaths (e.g., drowning),9,10 even though disasters often also cause Power Outages. If Outages affect mortality from non-traumatic causes, current fatality estimates for natural disasters could be underestimated, particularly for developed countries where disaster-related deaths are considered relatively rare. We investigated mortality in New York, NY, during the largest blackout in US history (August 14–15, 2003).11 This was the first citywide blackout of New York, NY, since 19777 and constituted a natural experiment—Power was available until 4:11 pm on August 14, and then immediately unavailable throughout the community.7 We compare population mortality on blackout days to days with Power.
-
lights out impact of the august 2003 Power Outage on mortality in new york ny
ISEE Conference Abstracts, 2011Co-Authors: Brooke G Anderson, Michelle L BellAbstract:Background and Aims: In the United States, energy use is increasing, which stresses the aging Power grid, creating frequent possibilities for Power Outages. Energy infrastructure is critical for na...
