Spatial load analysis
44
Spatial Electric Load Analysis for Substation Siting and Load Balancing At United Power, the engineering and GIS groups were tasked with answering the following question, “Will we have the infrastructure to support future demand in 5 or 10 years?” We turned to spatial technologies to provide management with an accurate and detailed GIS-generated load density forecast. Demand and energy readings from CIS were integrated with GIS to produce base grids for summer and winter peaks. The analysts combined base load grids with 2 forecast sources to produce long-range forecast raster grids. The complex analysis process was performed with multiple Model Builder models for consistency and repeatability. ESRI’s Spatial Analyst extension performed raster analysis. By maintaining a spatial history of power consumption, accurate data is readily available for a plethora of statistical studies and testing what- if scenarios. Using GIS technologies for planning purposes increases forecast accuracy and efficiency and creates a roadmap for future land and ROW acquisitions.
description
Proyección de demanda espacial
Transcript of Spatial load analysis
Spatial Electric Load Analysis for Substation Siting and Load Balancing
At United Power, the engineering and GIS groups were tasked with answering the following question, “Will we have the infrastructure to support future demand in 5 or 10 years?” We turned to spatial technologies to provide management with an accurate and detailed GIS-generated load density forecast. Demand and energy readings from CIS were integrated with GIS to producebase grids for summer and winter peaks. The analysts combined base load grids with 2 forecast sources to produce long-range forecast raster grids. The complex analysis process was performed with multiple Model Builder models for consistency and repeatability.ESRI’s Spatial Analyst extension performed raster analysis. By maintaining a spatial history of power consumption, accurate data is readily available for a plethora of statistical studies and testing what-if scenarios. Using GIS technologies for planning purposes increases forecast accuracy and efficiency and creates a roadmap for future land and ROW acquisitions.
Spatial Electric Load Analysis for Substation Siting and Load Balancing
David Hollema – GIS AnalystJared Weeks – Electrical Engineer
United Power, Inc.Brighton, Colorado
ESRI EGUG 2008
Today’s agenda
▪ Who we are▪ Long range forecast goals▪ Spatial load analysis basics▪ Components of spatial load forecasting▪ Load center prediction▪ Results and looking ahead
United Power Facts
Rural electric cooperative headquartered in Brighton, CO Incorporated in October of 1938 Wires hot in 1940 to 750 customersNearly 65,000 customers today covering 900 square milesHistorically fast growing – up to 5000 new accounts per yearAmong the top 10 fastest growing coops nationwide
Long range forecast goals and tasks
▪ Spatially project coincidental peak load over thenext 10 years
▪ Define substationinfluence areas
▪ Forecast and locate future load centers for substation placement
▪ Determine substation transformerupgrades
▪ Update every 3 years (modular)
What is spatial load analysis?
▪ A process of looking at historical electric power consumption with a spatial (e.g. mapping) component– Seasonal peak focus– Demand (kW) or energy (kWh)
▪ Typically includes forecasting for substation siting▪ Change detection analysis▪ Temporal study▪ Used to optimize current electric distribution system
Monthly Demand - 1998 to 2008
0
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eak
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Monthly Demand - 1998 to 2008
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eak
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(kW
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NO SPATIAL COMPONENTWinter Peak
Summer Peak
Summer Peak Demand Linear Forecast
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Peak
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Summer Peak Demand Linear Forecast
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Peak
Dem
and
(kW
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5yr trendHistoric
NO SPATIAL COMPONENT
Spatial Load Forecast Approach – 3 components
1. Base Map▪ Historical snapshot of peak seasonal load▪ Peak load density map
2. Metrostudy Data (www.metrostudy.com)
▪ Provider of housing data▪ Used to forecast residential growth
3. Point Loads▪ Internal knowledge of future large commercial loads from district reps
20022002
Meter Reading Extraction
Base map preparation
Load Table Preparation
Spatial Join
Rasterization
– “need to know where you’ve been to know where you’re going”
Must be repeatable and modular!
Base Map Preparation
Meter Reading Extraction
▪ Primary input for entire analysis
▪ Interested in 1 moment in time with coincident peak demand, settle for 30 day data (billed monthly)
▪ Oracle view used to extract CIS data to SDE instance
▪ Revolving billing cycle makes capturing monthly peak difficult
Meter Read-type Breakdown
55%35%
10%
Manual ReadCarrier Line AMRDrive-by AMR
Base Map Preparation
Base Map Preparation
SDE Oracle view into CIS databaseSELECT MAX(BI_CONSUMER.BI_ACCT) AS ACCT_NBR, MAX(BI_TYPE_SERVICE.BI_SRV_STAT_CD) AS SRV_STAT_CD,
/* BI_CONSUMER_VIEW_1.BI_ADDR_TYPE,*/MAX(BI_CONSUMER_VIEW_1.BI_SORT_NAME) AS NAME,MAX(BI_CONSUMER_VIEW_1.BI_LNAME) AS LAST_NAME, MAX(BI_CONSUMER_VIEW_1.BI_FNAME) AS FIRST_NAME, BI_SRV_LOC.BI_SRV_MAP_LOC AS SERVLOC,
/* BI_HIST_USAGE.BI_CUR_HIST_SW, */MIN(BI_HIST_USAGE.BI_RATE_SCHED),MAX(BI_HIST_USAGE.BI_PRES_READ_DT) AS READ_DATE, SUM(BI_HIST_USAGE.BI_USAGE) AS KWH,MAX(BI_HIST_USAGE.BI_BILL_DMD_HIST) AS DEMAND_KW,MAX(BI_HIST_USAGE.BI_REV_YRMO)
/* BI_HIST_USAGE.BI_PRES_MTR_RDG AS KWH */
FROM ((((([email protected] BI_CONSUMER INNER JOIN [email protected] BI_AR ON BI_CONSUMER.BI_ACCT=BI_AR.BI_ACCT) INNER JOIN [email protected] BI_CONSUMER_VIEW_1 ON BI_CONSUMER.BI_ACCT=BI_CONSUMER_VIEW_1.BI_VWN_CO_ACCT) INNER JOIN [email protected] BI_TYPE_SERVICE ON (BI_AR.BI_ACCT=BI_TYPE_SERVICE.BI_ACCT) AND (BI_AR.BI_TYPE_SRV=BI_TYPE_SERVICE.BI_TYPE_SRV)) INNER JOIN [email protected] BI_HISTORY ON ((BI_TYPE_SERVICE.BI_ACCT=BI_HISTORY.BI_ACCT) AND (BI_TYPE_SERVICE.BI_TYPE_SRV=BI_HISTORY.BI_TYPE_SRV)) AND (BI_TYPE_SERVICE.BI_SRV_LOC_NBR=BI_HISTORY.BI_SRV_LOC_NBR))INNER JOIN [email protected] BI_SRV_LOC ON BI_TYPE_SERVICE.BI_SRV_LOC_NBR=BI_SRV_LOC.BI_SRV_LOC_NBR) INNER JOIN [email protected] BI_HIST_USAGE ON ((((BI_HISTORY.BI_ACCT=BI_HIST_USAGE.BI_ACCT) AND (BI_HISTORY.BI_TYPE_SRV=BI_HIST_USAGE.BI_TYPE_SRV)) AND (BI_HISTORY.BI_SRV_LOC_NBR=BI_HIST_USAGE.BI_SRV_LOC_NBR)) AND (BI_HISTORY.BI_HIST_CD=BI_HIST_USAGE.BI_HIST_CD)) AND (BI_HISTORY.BI_BILL_DT_TM=BI_HIST_USAGE.BI_BILL_DT_TM)
WHERE BI_CONSUMER_VIEW_1.BI_ADDR_TYPE=N' ' AND BI_HIST_USAGE.BI_CUR_HIST_SW=N'Y' AND BI_HIST_USAGE.BI_REV_YRMO=200807AND BI_RATE_SCHED NOT IN ('SC0','SC1','SC5') --removes Golden Aluminum (fed off transmission) and Frederick/Evanston area primary meters--such as 3236-2652-0 and 3224-4552-0 (double-counted load)AND SUBSTR(BI_SRV_LOC.BI_SRV_MAP_LOC,5,1) !='6'AND BI_SRV_LOC.BI_SRV_MAP_LOC !='333305250' --removes Spindle Hill Energy Peak Plant fed off transmission
GROUP BY BI_SRV_LOC.BI_SRV_MAP_LOC
CIS GIS
Base Map Preparation
Base Map Preparation
Load table preparation
▪ Engineering calculation fields added▪ Some meters billed by usage (kWh), others by
usage and demand (kW)▪ Estimate coincident peak kW using kWh
Monthly Demand & Energy- July 1998 to July 2008
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Energy (kWh) to Coincidental Power (kW)
Base Map Preparation
PeriodBiling
PeriodBiling
HourskWh
PowerAverage_
__ =
Average Power
Energy
Typical United Power System
Peak
Peak Load - Coincidental
entalNoncoincidalCoincident PowerPeakCFPowerPeak _*_ =
Peak Load - Noncoincidental
LFLoadAveragePowerPeak entalNoncoincid
__ =
Peak PowerAverage PowerEnergyPeak Power -Coincidental
1 Peak PowerAverage PowerEnergyPeak Power -Coincidental2 Peak PowerAverage PowerEnergyPeak Power -Coincidental3
Understanding Load Factors and Coincident Factors
1 .6 0
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Lo ad Fa cto r (L F) = .3 2
C oin cid ent Factor (CF ) = .5
Avera ge Po wer
1 .6 0
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Tim e (hr)
Pow
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Lo ad Fa cto r (L F) = .3 2
C oin cid ent Factor (CF ) = .5
Avera ge Po wer
Base Map Preparation
Use the most accurate sources of information
▪ Residential
▪ IndustrialCFPowerPeakPowerPeak IalCoincident *__ =−
CFPowerAverageLFPowerPeak RalCoincident *_*_ =−
Base Map Preparation
Peak PowerAverage PowerEnergyPeak Power -Coincidental
CF
LF
Load table preparation ModelBuilder model
Base Map Preparation
Load table preparation ModelBuilder model continued…
Base Map Preparation
Base Map Preparation
Spatial Join
▪ GIS + iVUE load data + vector grid
▪ Summarizes for each grid cell, number of customers, usage (kWh), demand (kW), and coincident peak demand (kW)
3307-2508-0
3307-2407-0
3307-2405-1
3307-1912-0
3307-2305-03307-2105-0
3307-2110-1
3307-2110-0
3307-2108-0
3307-1911-0
3307-1808-1
3307-1709-1
3307-1706-0
3307-2605-0
3307-2212-1
3307-1812-0
3307-1513-0
3307-2210-0
3307-2107-0
3307-1910-1
3307-1611-13307-1511-0
3307-1313-0
3307-1310-0
3307-2510-0
3307-2408-0
3307-2410-0
3307-2013-0
3307-2303-0
3307-2205-0
3307-2109-1
3307-1809-0
3307-1806-0
3307-1611-0
3307-1606-0
3307-1505-1
3307-1506-0
3307-1411-0
3307-1312-1
3307-1405-0
3307-1209-1
3307-1208-0
3307-1207-1
3307-2510-1
3307-2406-0
3307-2213-0
3307-2209-0
3307-2208-0
3307-2109-0
3307-2007-03307-1906-0
3307-1905-0
3307-1711-0
3307-1705-0
3307-1708-0
3307-1606-1
3307-1410-0
3307-1409-0
3307-1311-0
3307-1205-0
3307-2410-1
3307-2309-0
3307-2106-0
3307-1805-0
3307-1408-1
3307-2613-0
3307-2612-0
3307-2513-0
3307-2512-03307-2412-03307-2212-0
3307-1713-0
3307-1612-1
3307-2409-0
3307-2306-0
3307-2310-0
3307-2111-0
3307-2003-0
3307-1808-0
3307-1811-0
3307-1710-0
3307-1605-0
3307-1608-0
3307-1510-1
3307-1510-0
3307-1410-1
3307-1213-0
3307-1408-0
3307-1210-0
3307-2611-0
3307-2505-0
3307-2411-0
3307-2012-03307-1812-13307-1512-1
3307-2405-0
3307-2308-0
3307-1910-0
3307-1809-1
3307-1406-03307-1306-0
3307-1209-0
3307-2509-0
3307-1911-1
3307-1813-0
3307-1612-03307-1512-0
3307-2206-1
3307-2208-1
3307-2005-0
3307-1708-1
3307-1709-03307-1609-0
3307-1508-0
3307-1505-0
3307-1312-0
3307-1305-0
3307-1206-0
3307-1207-0
SAGE STREET
SAIN
T V
RAIN
RAN
CH
BOU
LEVA
RD
SANDY RIDGE COURT
SHENANDOAH STREET
3307-2508-0
3307-2407-0
3307-2405-1
3307-1912-0
3307-2305-03307-2105-0
3307-2110-1
3307-2110-0
3307-2108-0
3307-1911-0
3307-1808-1
3307-1709-1
3307-1706-0
3307-2605-0
3307-2212-1
3307-1812-0
3307-1513-0
3307-2210-0
3307-2107-0
3307-1910-1
3307-1611-13307-1511-0
3307-1313-0
3307-1310-0
3307-2510-0
3307-2408-0
3307-2410-0
3307-2013-0
3307-2303-0
3307-2205-0
3307-2109-1
3307-1809-0
3307-1806-0
3307-1611-0
3307-1606-0
3307-1505-1
3307-1506-0
3307-1411-0
3307-1312-1
3307-1405-0
3307-1209-1
3307-1208-0
3307-1207-1
3307-2510-1
3307-2406-0
3307-2213-0
3307-2209-0
3307-2208-0
3307-2109-0
3307-2007-03307-1906-0
3307-1905-0
3307-1711-0
3307-1705-0
3307-1708-0
3307-1606-1
3307-1410-0
3307-1409-0
3307-1311-0
3307-1205-0
3307-2410-1
3307-2309-0
3307-2106-0
3307-1805-0
3307-1408-1
3307-2613-0
3307-2612-0
3307-2513-0
3307-2512-03307-2412-03307-2212-0
3307-1713-0
3307-1612-1
3307-2409-0
3307-2306-0
3307-2310-0
3307-2111-0
3307-2003-0
3307-1808-0
3307-1811-0
3307-1710-0
3307-1605-0
3307-1608-0
3307-1510-1
3307-1510-0
3307-1410-1
3307-1213-0
3307-1408-0
3307-1210-0
3307-2611-0
3307-2505-0
3307-2411-0
3307-2012-03307-1812-13307-1512-1
3307-2405-0
3307-2308-0
3307-1910-0
3307-1809-1
3307-1406-03307-1306-0
3307-1209-0
3307-2509-0
3307-1911-1
3307-1813-0
3307-1612-03307-1512-0
3307-2206-1
3307-2208-1
3307-2005-0
3307-1708-1
3307-1709-03307-1609-0
3307-1508-0
3307-1505-0
3307-1312-0
3307-1305-0
3307-1206-0
3307-1207-0
SAGE STREET
SAIN
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RAN
CH
BOU
LEVA
RD
SANDY RIDGE COURT
SHENANDOAH STREET
+
+250, 500, and
1000 ft grid cells
Base Map Preparation
Spatial join ModelBuilder model
Base Map Preparation
Spatial join ModelBuilder model continued…
Base Map Preparation
Rasterization
▪ ArcToolbox tool “Polygon to Raster”converts vector polygon feature class to geotif
▪ Raster files are much easier to analyze, manipulate, and perform algebraic computations
▪ ESRI’s Spatial Analyst used for raster manipulation
vector
raster
Base Map Preparation
Raster Results
Mead
Erie
Niwot
Dacono
Leyden
Hudson
GoldenEmpire
Eldora
Dupont
Arvada
Boulder
Watkins
Valmont
Hygiene
Bennett
Lochbuie
Thornton
Wondervu
SuperiorMarshall
Longmont
Gilcrest
EastlakeCrescent
Brighton
Berthoud
Firestone
Frederick
Nederland
Lafayette
Henderson
Edgewater
Wattenberg
Pinecliffe
Northglenn
Louisvi lle
Keenesburg
Broomfield
Adams City
Wheat Ridge
Westminster
Platteville
Fort Lupton
East Portal
Rollinsville
Central City
Commerce City
Western Hills
Federal Heights
Eldorado Springs
Denver International Airport
N0 5 10 Miles
Coincident Demand (kW)-9 - 01 - 2021 - 3536 - 5556 - 7071 - 8586 - 100
101 - 115116 - 132133 - 150151 - 250251 - 500501 - 1,0001,001 - 20,00020,001 - 40,000
2007 Peak Demand
Winter - January
Summer - July
Base Map Preparation
Base Map Preparation
2002-2008 coincident peak timeline
2002 2003 2004
2005 2006 2007
2008
2002 versus 2008 coincident peak – Plains territory
20022002 20082008
Base Map Preparation
Metrostudy Data
0500
1,0001,5002,0002,5003,0003,5004,0004,5005,000
4Q03 4Q04 4Q05 4Q06 4Q07 4Q08 4Q09 4Q10 4Q11 4Q12 4Q13 4Q14 4Q15
Quarter (Q)
Num
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Function f it to AnnualaveragesRolling Annual Closings
0500
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Quarter (Q)
Num
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Function f it to AnnualaveragesRolling Annual Closings
▪ Survey of subdivision data▪ Updated quarterly▪ Spatial reference▪ Annual closings fit to a 5 year sine wave
Metrostudy Forecast
Metrostudy Forecast
Metrostudy – 2017 forecast
Mead
Erie
wot
Dacono
en
Hudson
Dupont
ene
Lochbuie
Thorntonuperior
Longmont
Gilcrest
Eastlake
Brighton
Berthoud
Firestone
Frederick
Lafayette
Henderson
Wattenberg
Northglenn
Louisville
Keenesburg
Broomfield
Westminster
Platteville
Fort Lupton
Commerce CityFederal Heights
Denver International Airport
§̈¦76
§̈¦25
Æ·52
Æ·7
Æ·66
Æ·470
Æ
Æ·2Æ·121
Æ·60
Æ·95
£¤287
£¤85
É44
É42
Mead
Erie
wot
Dacono
en
Hudson
Dupont
ene
Lochbuie
Thorntonuperior
Longmont
Gilcrest
Eastlake
Brighton
Berthoud
Firestone
Frederick
Lafayette
Henderson
Wattenberg
Northglenn
Louisville
Keenesburg
Broomfield
Westminster
Platteville
Fort Lupton
Commerce CityFederal Heights
Denver International Airport
§̈¦76
§̈¦25
Æ·52
Æ·7
Æ·66
Æ·470
Æ
Æ·2Æ·121
Æ·60
Æ·95
£¤287
£¤85
É44
É42
▪ MetroStudy provieds point data
▪ Circular shapes are from multiplying the estimated area of a lot by the number of lots and converting it into the area of a circle.
▪ Future platted subdivisions forecasted to go online based on onsite activity.
QtrActive
LotFront
FutureLot Prelim Record Vacant
LandSurveyStakes
Equipon Site Excavation Street
PavingFuture 0'-80' 4,800 4,257 543 0 0 543 4,257 0Future 0'-0' 400 400 0 0 0 0 0 400Future 70'-80' 104 0 104 0 0 0 104 0Future 0'-0' 429 429 0 0 429 0 0 0Future 0'-0' 952 952 0 952 0 0 0 0
Point Loads
▪ Information gleaned by the in-house staff about large commercial/industrial customers
▪ 3-5 year accuracy▪ Spatially represented▪ Forecasted annually
Point Load Forecast
Point loads – 2007 forecast
Hotels750kW
Walmart540kW
Vestas18000kW
Warehouse300kW
Mead H.S.750kW
High Rise500kW
Semcrude2000kW
Otho Stuart0kW
Life Bridge500kW
Mc Whinney7000kW
AVA Solar10000kW
Aurora Dairy500kW
Aspen Reserve372kW
THF Firestone1000kW
Palisade Park2000kW
Adams Crossing2375kW
Anadarko No. 110000kW
Anadarko No. 240000kW
Pioneer Village1748kW
Suncor Energy Upgrade0kW
Boulder Scientific3000kW
LEED's Manufacturing2500kW
WCR 34 & HW 25 - 220 LLC0kW
Adams County Qt House1800kW
Northland Village PUD5000kW
Brighton High school No. 3500kW
Denver Water Pump Facility2000kW
Waste Water Treatment Plant500kW
Hudson Correctional Facility1600kW
South Adams Water - Pumping Station1192.5kW
Mead
Erie
Niwot
Dacono
Roggen
Leyden
Hudson
Dupont
Boulder
Valmont
Hygiene
ThorntonSuperiorMarshall
Longmont
Gilcrest
Eastlake
Brighton
Berthoud
Firestone
Frederick
Lafayette
Henderson
Wattenberg
Northglenn
Louisville
Keenesburg
Broomfield
Westminster
Platteville
Fort Lupton Prospect Valley
Federal Heights
Eldorado Springs
Denver International Airport
§̈¦25
Æ·52
Æ·7
Æ·79
Æ·66
Æ·93
Æ·119
Æ·470
Æ·72
Æ·128
Æ·2
Æ·121
Æ·60
Æ·95
£¤287£¤36
£¤85
£¤34
É44
É42
É157
Point Load Forecast
Map Algebra Forecast Process
2007 Base Map
Metrostudy
Point Loads++++++
x (scale factor dependent on year)
Explanation of scale factor
▪ Scale factor based of a linear trend of historic data.
▪ No double counting!– Commercial -
Industrial Point loads– Metro Study
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Load
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Linearly Trended Grow thHybrid Forcast (Plains)Metrostudy - pre-intersect w ith gridForecasted Com/Ind LoadsHistoric Data (Plains)
281
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+++ +++ ===
2017 Plains Forecast
Mead
Erie
Niwot
Dacono
Leyden
Hudson
Dupont
Arvada
Boulder
Valmont
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Heavily loaded feeders no more than 4 miles in lengthPhysical Barriers
InterstatesRailroadsRivers
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Load Center Forecast
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Load Center Forecast
Find Load Center ModelBuilder Model
Influence Areas and Load Centers
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201720122007
NOAA DMSP Nighttime Lights
NOAA DMSP Nighttime Lights
Mead
Erie
Niwot
Dacono
Leyden
Hudson
GoldenEmpire
Eldora
Dupont
Denver Aurora
Arvada
Boulder
Watkins
Valmont
Hygiene
Bennett
Lochbuie
ThorntonWondervu
SuperiorMarshall
Longmont
Gilcrest
Edgemont
EastlakeCrescent
Brighton
Berthoud
Firestone
Frederick
Nederland
Lafayette
Henderson
Edgewater
Wattenberg
Pinecl iffe
Northglenn
Louisville
Keenesburg
Broomfield
Adams City
Wheat Ridge
Westminster
Platteville
Fort Lupton
East PortalRollinsville
Central City
Commerce City
Western Hills
Pleasant View
Mountain View
Idaho Springs
Prospect Valley
Federal Heights
Eldorado Springs
Denver International Airport
Looking ahead…
▪ Make better use of ArcInfo level tools and Spatial Analyst to simplify the process– Use “Point to Raster” tool to convert service points
with load directly to raster▪ Interested in spatial electric load forecasting class
for formal education▪ Explore other forecasting methods including land
use classification▪ Create more empirical models independent of
rate structure.– take advantage of data loggers
Acknowledgements and References
▪ Various folks at United Power▪ Software systems: ArcGIS 9.2, ArcGIS Spatial Analyst,
Oracle 10g with ArcSDE 9.2, CIS NISC iVUE with Oracle back-end
▪ Valenti, Jessica. Spatial Load Forecasting, presented at ESRI EGUG, October 2006, Albuquerque, NM. http://gis.esri.com/library/userconf/egug2006/papers/spatial-load.pdf
▪ Willis, Lee. Spatial Electric Load Forecasting. Second Edition. New York: Marcel Dekker, 2002.
▪ Image and Data processing by NOAA's National Geophysical Data Center. DMSP data collected by the US Air Force Weather Agency.
