APPENDIX A
PACIFICORP HOURLY FORECAST PROCESS
This Appendix presents an overview of the hourly forecasting process developed by Itron and
PacifiCorp. Itron documented the procedures in a 13-page report entitled “PaciCorp Hourly
Forecast Process”. The report was provided to GDS Associates through discovery request CCS
1.1. This Appendix summarizes the methodology as presented in the report and provides GDS’
assessment and recommendations. This Appendix is fairly technical in nature since our
recommendations are based on technical specifics within the methodology.
SUMMARY OF METHODOLOGY
The purpose of the hourly forecasting process is twofold: to generate projected load duration
curves and to calculate projected coincident peak demands. There are three basic steps to the
hourly forecasting process: develop a typical-year weather pattern, generate hourly load
projections based on the weather pattern, and calibrate the hourly projections to the monthly
energy and demand projections developed in other models. Each of the three steps is summarized
in this appendix. The final outputs of the demand forecasting process are load projections by year
and hour (8760 hours in a year) for each jurisdiction. These hourly numbers are aggregated to
determine a coincident peak demand for each jurisdiction.
The first step is to develop a typical year of daily heating and cooling degree days (HDD and CDD,
respectively). For each of twenty years, the Company first sorts the actual daily degree days in
rank order by month. Then, they average the first rank day, the second rank day, etc. across all
twenty years. The result is a twenty-year average daily degree day but sorted in rank order. The
next step, then, is to rearrange these rank average weather days into a calendar scheme. Itron
does this by selecting a specific year and using the ranks in that year as the order for the
assignment of the typical weather days to a calendar scheme. In order to select the historical year,
they compare the average diversity factor (coincident peak demand divided by non-coincident peak
demand) to actual diversity factors for each of the last fifteen to twenty years for each jurisdiction.
They then sum the absolute difference in diversity factor for each year across jurisdictions to
calculate a “mean absolute deviation”. The year selected to reassign the normalized weather days
is the one with the lowest mean absolute deviation. The figure below, taken from the Itron report,
demonstrates this methodology for July. Once the historical year is chosen, the weather is
assigned to calendar-day order and a typical weather year is developed.
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APPENDIX A
PACIFICORP HOURLY FORECAST PROCESS
FIGURE 1
Historical Years Select for Assignment Pattern - July
Note: Selection based on smallest MAD. MAD = Σ|Deviations from Average|
Once the projected hourly weather data is developed, regression models are used to project hourly
energy based on those weather patterns. The first model projects energy for the entire day. The
daily energy model has calendar, weather, and SAE appliance index data as independent
variables. Then, the projected daily energy becomes an input into each of twenty four models that
project the energy for each hour of the day. The hourly models also have calendar, weather, and
SAE appliance index variables as independent variables. This series of twenty five regression
models develop 8760 hourly load projections based on the typical weather produced in the first
step of the modeling process.
The final step is to calibrate the hourly load projections so (1) the sums total to the monthly energy
projections developed from the individual energy sales models and (2) the jurisdictional NCP
demand matches the peak forecast from the monthly demand model. The calibration is a two-step
process. In the first step, the data is sorted into a load duration curve (LDC), and then the entire
curve is shifted up to match the peak demand to the calibration target demand. However, it is
highly likely that this resultant LDC will compute some amount of energy that is different from the
target energy amount. In order to make the second adjustment, the LDC is “pivoted” with the fixed
pivot being the peak demand (see the figure below from the Itron report that demonstrates this
effect). Basically, the further away from the peak a particular load falls, the larger an adjustment it
will get relative to other hours. Therefore, the minimum load gets the largest adjustment. After this
pivot adjustment, the hourly forecast is complete and jurisdictional coincident peak demands can
be calculated.
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APPENDIX A
PACIFICORP HOURLY FORECAST PROCESS
FIGURE 2
Calibration of Hourly Curve to Modeled Energy – Pivoting the LDC
GDS ASSESSMENT & RECOMMENDATIONS
The procedure used by PacifiCorp to generate hourly load projections can be difficult to follow and
seems cumbersome. However, generating hourly projections from monthly demand and energy
forecasts is not a simple process. Overall, PacifiCorp’s methodology should produce as
reasonable results as can be expected when trying to predict hourly loads. However, GDS offers
several recommendations regarding the procedure. As stated in the main report, these
recommendations would probably not effect CP demand projections enough to impact the IRP
planning process. However, it is our opinion that these recommendations improve the theoretical
construct of the methodlogy.
General Assessment

The method of rank averaging to produce a typical weather year is a good one. There are
no issues with this procedure. The use of twenty years of historical weather is consistent
with the rest of the forecasting process and is a sufficient time-period for normalizing
weather. A rolling average as used in the current forecasting process is preferable to a
fixed average.

The daily energy and hourly energy models used to predict un-calibrated hourly load
projections meet or exceed industry standard. These models exhibit strong diagnostics
and predictive power over the study period.

In general, the calibration methodology is acceptable, with one caveat as listed in the
recommendation section below. The step of actually calibrating the hourly energies is an
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APPENDIX A
PACIFICORP HOURLY FORECAST PROCESS
important one and can be a difficult step to accomplish. The Company’s methodology
generally meets industry standards.
Recommendations

There is no clear evidence that using deviation from an average diversity factor is the
best methodology for selecting a year for the weather assignment pattern (refer to
Figure 1 above). Diversity factors can be impacted by non-weather related variables.
Instead, PacifiCorp might consider using the deviation from normal heating and
cooling degree days.

When selecting the historical year for a weather assignment pattern (refer to Figure 1
above), PacifiCorp should use a weighted average absolute deviation instead of a
straight sum (or average). The weights should be based on jurisdictional energy
and/or demand, meaning the selection criteria should be weighted more toward years
that are typical for Oregon and Utah.

GDS concludes the LDC pivot methodology of calibration (assigning a greater amount
of adjustment to the minimum loads) can distort the projected load duration curve in a
manner that is detrimental to power supply planning. By adjusting minimum loads
more, base and intermediate resource requirements may be over- or under-stated due
to the calibration method. The Itron documentation does indicate they are aware of
this issue. On Page 11 of their documentation, they state that the California
adjustment “placed excessive downward pressure on the minimum values”. They took
steps to correct for the potential problem. It is important for PacifiCorp to remember to
observe the minimum values that result from the calibration to ensure that they are
indeed reasonable, especially with respect to the Integrated Resource Plan. GDS
recommends a method that spreads the adjustment out more evenly to intermediate
and base hours of the load duration curve.
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