IBM T. J. Watson Research Center © 2010 IBM Corporation A Statistical Model for Risk Management of Electric Outage Forecasts Hongfei Li IBM Workshop, September

IBM T. J. Watson Research Center

© 2010 IBM Corporation

A Statistical Model for Risk Management of Electric Outage Forecasts

Hongfei Li

IBM Workshop, September 17, 2010


© 2010 IBM Corporation2

Outline

Overview of statistical applications in Smarter Planet

Project of damage forecasting



Overview of Statistical Applications in Smarter Planet

The world is becoming more instrumented and interconnected.

A huge amount of information is created through much denser sensor measurements extending over large space and tracking over time.

Existing methodologies are not sufficient to meet the demand coming from complex problems involving time and space.

Particular spatio-temporal statistical models need to be developed to solve the challenging areas which also follows current IBM business direction as well as professional research direction.



Analytics Driven Asset Management (ADAM)

Cities are a key focus area in the IBM Smarter Planet Strategy

Traffic & Transportation

Water availability & purity

Building & Energy

Safety



Key Challenges in Water Management

Water usage has increased at twice the rate of population grow.

Threefold issues: quantity, quality and energy

Interesting Questions:

– How can we better schedule our crews to reduce “windshield time”?

– How to prevent pipes from breaking?

– How can we effectively use capital $ to replace the right bits of our infrastructure?

– How can we understand water usage patterns and manage demand?

– How can prevent pollution during storm events?



Statistical Analysis in ADAM

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Water usage analysis

Pressure zone analysis

Failure prediction analysis



Green Exploratory ResearchSpatial-temporal causal modeling for climate change attribution

Motivation: the 2003 European heat wave was one of the hottest summers on record in Europe. The heat wave led to health crises in several countries and combined with drought to create a crop shortfall in Southern Europe. Approximately 35,000 people died as a result of the heat wave.

Goal: address the attribution of extreme climate events, such as heat waves.

Method: develop a novel method to infer causality from spatial-temporal data, as well as a procedure to incorporate extreme value modeling into our method.



Environmental Risk

Reference: Lozano, A., Li, H., Niculescu-Mizil, A., Liu, Y., Perlich C., Hosking, J. and Abe, N., “Spatial-temporal Causal Modeling for Climate Change Attribution”, Knowledge Discovery and Data Mining conference 2009

Incorporate extreme value modeling into the causality network analysis inorder to address the attribution of extreme climate events, such asheatwaves.

Figure: Attributing the change in 100-year return level for temperature extremes. Edge thickness represents the causality strength.

Figure: Difference of averaged return level between 1967 and 1980 and between 1981 and 2005. Investigate return levels changing over time.




Goals• Investigate and forecast severe storm impacts on electric infrastructure distribution system




• High resolution numerical weather prediction, advanced data assimilation and visualization with applications for severe storms affecting urban areas

• Quantification of forecast uncertainty caused by various data sources and different modeling structures

• Uncertainty visualization for operational decision making

Weather prediction

Damage prediction

Restoration time prediction

Resource requirement prediction



Challenges

Damage forecast model inputs

– Which weather inputs are important for damage forecast?

– Most weather variables are correlated

– Multicollinearity may cause invalid interpretation of weather predictors

Weather forecast calibration

– Forecasted variables (e.g., wind speed) may differ in meaning vs. observations used in the damage-forecast-model training

– How should physical model outputs be calibrated so that they can be used as the inputs of damage forecast model?



Challenges (cont’d)

Gust speed calculation

– Exploratory data analysis indicates that gust speed has a stronger relationship to damages vs. wind speed

– However, general meteorological models do not provide a direct gust forecast

– How should gust speed be calculated based on limited weather information?

Uncertainty quantification and visualization

– Uncertainties come from various data sources and different model structures



Challenges

Model integration

– How should damage forecasts, multiple spatial resolution interpolations and calibration be integrated in one framework?

Utility Service Area, AWS/WeatherBug Stations and Example NWP Grid



Approach A damage forecast model at the area

substation level is developed using historical weather observations* and outage data** by building a hierarchical Poisson regression model

This damage forecast model is coupled to the meso-g-scale numerical forecasts generated by the “Deep Thunder” (DT) system developed at the IBM Thomas J. Watson Research Center

DT “gust calculation” is developed via a statistical model using time series analysis based on historical DT wind forecast and gust “observations”

Statistical hierarchical modeling integrates various data sources in one model and allows variances or uncertainties analyzed at different levels

Historical Damage Data

DT Damage Forecast Model

Model Training

Calibrated DT RAMS/WRF

Outputs

Historical Weather Data

DT Damage Forecast Outputs

Data Flow for the Deep Thunder Damage Forecast Model



Exploratory Analysis

• Gust speed has a stronger correlation with damage vs. wind speed

• Gust speed is adjusted by leaf coverage and ground saturation

• Number of outages for each substation area is adjusted by infrastructure density



Exploratory Analysis

• Hourly damage/power outage distributions for three major storm events

• The blue curves show hourly gust speeds

• These figures provide tools for damage forecasts of sequential days

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Statistical Hierarchical Modeling

is the baseline number of outages, and reflects the infrastructure density in the substation area.

ikE



Statistical Hierarchical Modeling II



Statistical Hierarchical Modeling III

Procedure II: Hierarchical Model for Gust Calculation



Results

Site-Specific DT Gust Forecast

Damage Forecast vs. Actual Damage



Example Case Study – Retrospective Analysis 02 September 2006 Extra-tropical Event

Remnants of Tropical Storm Ernesto brought heavy rain and gusty winds of 40 to 57 mph across Long Island and southeastern New York, east of the Hudson River, including most of New York City

Numerous trees and power lines down with many power outages reported

There was widespread disruption of transportation systems (e.g., road closures, flooded subways, airport delays) and significant flooding in several regions

Reported Rainfall Westchester 0.5" - 2" Kings 0.5" - 1" Queens 0.5" - 1" Richmond 0.5" - 1"Nassau 0.5" - 0.75" Suffolk 0.5" - 0.75"

Reported Wind Gust Maximums (MPH)Westchester 48 Kings 52Queens 46-51 Richmond 49Nassau 41-57 Suffolk 40-55



DT Weather Forecast

Rainfall Forecast

Wind Forecast



Damage Forecasting Result

Actual Outages Outage Estimate Outage Upper Bound



Probability of Outages per Substation Area for Various Ranges of Outages (Texturing of Outage Color Illustrates Probability with Value)



References:

Li, H., Treinish, L. and Hosking, J., “A Statistical Model for Risk Management of Electric Outage Forecasts”, IBM Journal of Research and Development, vol. 54, no. 3, 2010

Documents

IBM T. J. Watson Research Center © 2010 IBM Corporation A Statistical Model for Risk Management of Electric Outage Forecasts Hongfei Li IBM Workshop, September