Overview of Forecast Models for Various ISOs

NE-ISO1
o Forecast Energy Model – econometric model uses hourly load data, weather data, and
economic data (personal income and retail electricity price) for a period of 20 years
(approx.) to forecast LR energy consumption BY STATE.
o Peak Forecast Model – econometric model uses hourly load data, weather data, and
economic data (personal income and retail electricity price) to forecast monthly peaks BY
STATE.
o See excel spreadsheets with models and statistical tests, as well as specifications of
models in the “Discussions” file starting on p. 6 and statistical tests on p. 19.

ERCOT (Texas)2
o Long Term Demand and Energy Forecast (as of May 2009) - a set of econometric models
that use weather, economic and demographic data and calendar variables to capture and
project the long- term trends in the historical load data for the past six years.
 The models are run on 8 different weather zones.
 In general, the economic variables used in the models throughout the eight
weather zones in the ERCOT electric grid, are various forms of employment
indicators, such as total non-farm employment and total employed, real personal
per-capita personal income, gross domestic product and population.
o Appendix 3: Methodology***
 There are a wide variety of methods that can be used to forecast system peak
and energy consumption. Such methods range from simple trending methods to
more complex ones such as end-use forecasting or hybrid end-use and
econometric techniques, sophisticated Box-Jenkins Transfer function (Dynamic
Regression) models and neural network models that can be adapted to produce
long-term forecasts.
 ERCOT Staff decided to produce long-term forecasts for eight major areas in
Texas where weather data was available and coincided with the available data
appropriate for load analysis. Thus, from ERCOT‘s standpoint, weather zones
were the logical choice. In addition, these zones also coincided with the major
areas of interest for the analysis of transmission projects. In summary, the total
load by weather zone was chosen as meeting the objective of the forecast needs.
These forecasts then could be aggregated to a system level.
 A regression with capabilities of performing a correction for autocorrelated
errors was deemed as the most appropriate choice available to meet ERCOT’s
objectives. This methodology is unique in that it directly and successfully
forecasts an hourly load shape using a regression model estimated by seasons.
This methodology could potentially be applied to other entities facing similar
requirements.
 The general formulation of the energy equations include the following variables:
Energy Month i = f {CDD, HDD, Income, Population, Employment, GDP, Monthly
Indicators, AR terms}

The general formulation of the load shape equations include selected variables
from some of the following:
Load hour i =f {Max Temps, Lagged Temps, Heat Index, Non-Linear Temp
Components (square and cube), Temp Gains (diff between daily high and low temps),
Temp Build-up, Dew Point, Month*Temp Interactions, CDD, HDD, Hour of Day
Indicators, Weekday/Weekend, Holidays, AR terms}
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2
NE ISO, Inc., General Discussion of Forecast Model Structures; Energy Forecast Model 2009; Peak Forecast Model 2009.
ERCOT 2009 Planning Hourly Peak Demand and Energy Forecast. May 1, 2009.
 CA ISO3
 Forecasting Overview
o Forecasts conforming (temperature sensitive) load for 5 climatic zones:
 PGE Bay Area
 PGE Non Bay Area
 SCE Coastal
 SCE Inland
 SDGE
o Forecasts non-conforming load (pumping) for PGE Non Bay Area and SCE Inland
separately
o Zonal forecasts are summed to obtain ISO forecast
o Combination of neural network and regression models
o Utilize approximately 25 calendar variables and 25 weather variables per model
 Inputs to model
o Hourly weather forecasts for next 9 days for 24 weather stations
o Weather forecasts include temperature, dew point, wind speed, and cloud cover
o Half-hourly actual zonal loads
 Automated Load Forecasting System (ALFS)
o Loads for all zones are forecasted every half-hour for next 9 days
o Weather forecast is updated every hour
o Forecast will adapt every half-hour to load forecast error
o Hour-ahead forecast is published every hour for three hours ahead (will be 75 minutes
ahead for MRTU)
o Day-ahead forecast is published once per day after market closes by 1PM (will be
10AM for MRTU)
 California Energy Commission – 2009 Integrated Energy Policy Report (IEPR) 4
o Requires filing of hourly loads (and other info) from all load-serving entities whose
annual peak load exceeded 200MW  the data exists at a granular level, but it does
not appear to be public.

PJM5
o
o
o
Load Forecasting uses hourly eMTR data entered by EDCs to the system to create
monthly and seasonal forecasts for each weather zone, region, and the entire RTO.
PJM reports hourly data each month in a report to the LSE/EDC.
Multivariate regressions to predict Load using the following variables:
 Calendar Effects
 Weather Effects (heating/cooling days, wind, humidity, etc.)
 “Economic Drivers”: gross metropolitan product.
 Non-Coincident Base Load forecast: Uses the median result of Monte Carlo
simulations as the 50/50 forecast.
 Coincident Base, behind-the-meter forecasts developed after input from EDCs
and LSEs.
 P. 21 – there should be more than 15 series…not 100 though.
 P. 22 – “weather normalization” procedure.
Overview of Electric Load Forecasting at CAISO (Power Point Presentation, August 10, 2007)
CEC, DRAFT DEMAND FORECAST FORMS AND INSTRUCTIONS FOR THE 2009 INTEGRATED ENERGY POLICY
REPORT (2009).
5
PJM, Manual 19, Load Forecasting and Analysis.
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