General Methods


Short-Term Load Forecasts (STLF)
o Hourly and sub-hourly load forecasts for ~7 days.
o Two types (generally): Artificial Intelligence (AI) vs. Time Series/Statistical
 AI
 Neural Networks
o EPRI’s Artificial Neural Network STLF (ANNSTLF)
o Itron’s Neural Network Capabilities.1
 PTR’s Similar Day (SimDay) forecast. 2
 Other forecasts use fuzzy logic, ANN, Genetic
Algorithms/Evolutionary Computing.
 Software chooses best forecast or combines forecasts
to create final forecast.
o Other Methods: Expert System, Particle Swarm, Support
Vector Machines , Neuro-Fuzzy3
 Time Series/Statistical4
 Regression
o Simple linear, usually with temperature modeled non-linearly.
o Times Series (Box-Jenkins)
 ARMA/ARIMA
Long Term Load Forecasts (LTLF)
o Econometric Models
 The econometric approach combines economic theory and statistical techniques
for forecasting electricity demand. The approach estimates the relationships
between energy consumption (dependent variables) and factors influencing
consumption. The relationships are estimated by the least-squares method or
time series methods.
 See, for example, PJM’s or NE-ISO’s long-term load forecast models.
o End-Use Models
 Descriptions of appliances used by customers, the sizes of the houses, the age of
equipment, technology changes, customer behavior, and population dynamics
are usually included in the statistical and simulation models based on the socalled end-use approach.5
 CEC, for example, uses end-use models: it’s presentation discusses 3 ways of
incorporating energy efficiency in demand forecasts (reconstitute loads,
historical EE as explanatory variable, forecast with changes in EE trends).67
 Statistically-Adjusted End-Use Models (Itron specializes)8
 The data and support services provided to EFG members enables you to
develop end-use forecasts even if you don't have the historical data.
With a resurgence of demand-side planning (DSM) programs and rate
case filings, you may need to incorporate DSM into your long-term
forecast. Through the EFG, Itron provides you with the necessary tools.
 The SAE method embodies end-use concepts and trends into a monthly
econometric forecasting framework. Itron works closely with the
See Itron’s Metrix ND overview.
PRT, “On-Line Load Forecasting Services,” January 24, 2007.
3
Zuhairi Baharudin, “AUTOREGRESSIVE MODELS IN SHORT TERM LOAD
FORECAST: A COMPARISON OF AR AND ARMA”
4
Hahn, Pickl, Meyer-Nieberg (2009)
5
Feinberg and Genethliou (2005), “Load Forecasting.”
1
2
6
http://74.125.113.132/search?q=cache:m7URrclnODsJ:www.energy.ca.gov/2009_energypolicy/documents/2009-0521_workshop/presentations/05_CEC_Gorin_May_21_refinements_F.pdf+Tom+Gorin+CEC&cd=1&hl=en&ct=clnk&gl=us&client=fi
refox-a
8
http://www.itron.com/pages/news_events_overview.asp?id=5F045D9F-9032-4394-BC28-522CB31AFBFD
7

Energy Information Administration (EIA) to embed their latest
equipment saturation and efficiency trend forecasts in these models.
EFG members receive regional versions of the SAE models (MetrixND
project files) and the associated regional databases. Residential and
commercial electric and gas SAE models are available to members
along with a technology option for electric residential appliances
including lighting.
The SAE model integrates end-use saturation and efficiency trends with
economic drivers, price, and weather. 9
Overview of Forecast Models for Various ISOs

NE-ISO10
o Forecast Energy Model – econometric model uses 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 (yearly).
 This model allows for flexible price elasticities.
o Peak Forecast Model – econometric model uses load data, weather data, and
economic data (personal income and retail electricity price) to forecast weekly,
monthly, and seasonal 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.
o Process for ISO-NE Forecaster running short term forecasts:11
 Run Similar Day (Simday) program to determine a similar-day load forecast for
the day.
 Run Metrix ND (Itron)
 The Metrix ND program automatically utilizes either the EFF
(Effective Termperature) during the heating months and the THI
(Humidity Index) during the summer months as the input driver for the
development of the hourly demand values. The Metrix ND program
calculates load for a 7-day period, Day 1through Day 7. This procedure
develops the load forecast for Day 2 and is eventually compared to the
Artificial Neural Network (ANN) model and Simday outputs for Day 2.
The Metrix ND program will develop a single load forecast model.
 Run Artificial Neural Networks Model (ANN)
 The ANN program integrates current day Log 7 loads and either the
EFF during the heating months and the THI during the summer months
as the input drivers for the development of the hourly demand values.
The ANN program calculates load for a 7-day period, Day 1 through
Day 7. This procedure develops the load forecast for Day 2 and is
eventually compared to the Simday and Metrix ND outputs for Day 2.
The ANN will develop multiple load forecast models. Two of these
models are „fast‟ learners, leaning more heavily on the most recent

historical load and weather data. The other two models are „regular‟
learners, weighing the most recent past load and weather data evenly)
The forecaster uses the output of these models and experience to create a final
load forecast by applying weights to each of the forecasts.
http://docs.google.com/gview?a=v&q=cache:VOaOWyUW2LYJ:www.icc.illinois.gov/edocket/reports/view_file.asp%3FintIdFile%3D229392%26strC%3Dbd+%22Statistically+Adjusted+End-Use%22&hl=en&gl=us
10
NE ISO, Inc., General Discussion of Forecast Model Structures; Energy Forecast Model 2009; Peak Forecast Model 2009.
11
http://74.125.155.132/search?q=cache:wwIH3AuMvTgJ:www.isone.com/rules_proceds/operating/sysop/out_sched/sop_outsch_0040_0010.pdf+ISO+%22load+forecast+model%22&cd=1&hl=en&ct=
clnk&gl=us&client=firefox-a
9

ERCOT (Texas)12
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.
 Uses hourly historical load data by weather zone, as well as economic data
(obtained monthly from Moody’s).13
 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 enduse 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}

(See Equations p. 34)
CA ISO14
 Forecasting Overview
o Forecasts conforming (temperature sensitive) load for 5 climatic zones:
ERCOT 2009 Planning Hourly Peak Demand and Energy Forecast. May 1, 2009.
http://docs.google.com/gview?a=v&q=cache:TkZ5Oy1B1w0J:www.ercot.com/content/meetings/board/keydocs/2009/0616/Item_09
_-_Long-Term_Demand_%26_Energy_Forecast_%26_Capacity,_Dem.pdf+long+term+load+forecast+2009&hl=en&gl=us
14
Overview of Electric Load Forecasting at CAISO (Power Point Presentation, August 10, 2007)
12
13


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)
Very Short Term Load Forecast Predictor (VSTLP)15
o The Very Short Term Load Predictor (VSTLP) is an application based on neural
network training. It uses five-minute averages of actual Load from the State Estimator
for the last 13 months as input. The VSTLP can generate four different Load forecasts:
on-line, off-line, automatic, and manual. In addition, the CAISO Operator can enter a
Load bias at any time, except for the manual forecast.
 California Energy Commission – 2009 Integrated Energy Policy Report (IEPR) 16
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. (See FERC 714 Data)
 PJM17
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.
o PJM reports aggregated hourly data each month in a report to the LSE/EDC.
o 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.
 SPP18
http://74.125.155.132/search?q=cache:_VGN4I2pZf4J:https://bpm.caiso.com/bpm/bpm/doc/000000000000007+%22CAISO%22+%
22neural+network%22&cd=5&hl=en&ct=clnk&gl=us&client=firefox-a
16
CEC, DRAFT DEMAND FORECAST FORMS AND INSTRUCTIONS FOR THE 2009 INTEGRATED ENERGY POLICY
REPORT (2009).
17
PJM, Manual 19, Load Forecasting and Analysis.
18
http://www.spp.org/glossary.asp?letter=S
15
o
o
o
STLF is a load forecast on the Emergency Management System (EMS) that uses CA
telemetry data (MW flow) and data from the Mid-Term Load Forecast (MTLF). STLF
does not incorporate weather data. STLF predicts one hour ahead on five-minute
intervals and runs every fifteen minutes to collect data needed to output predictions
every five minutes.
The MTLF is a load forecast on the Energy Management System (EMS) that reads the
weather data files that enter SPP every hour, uses telemetry data for CA load (i.e., MW
flow), integrates this data and SCADA data to output an hourly load forecast. MTLF
updates hourly, updates its own model every day and adapts based on its input
variables. The MTLF load curve shape is interpolated to use with STLF.
Discussion of ANNSTLF and economic dispatch methodologies. 19
Industry Players
 Itron20 (According to Tom Gorin of the CEC, most of the ISOs and many utilities use Itron’s
Metrix software)
o MetrixND – general load-forecasting platform.
 MetrixND allows rapid development of accurate forecasts, releasing your
valuable time for making decisions and communicating results. Designed to
take advantage of advanced Microsoft Windows® capabilities, the intuitive
user interface and drag-and-drop architecture streamline the development of
forecasting variables and models. Powerful forecasting techniques, such as
neural networks, multivariate regression, ARIMA and exponential smoothing
make MetrixND the only tool you need to forecast monthly sales and revenue,
long-term energy demand, gas send-out, short-term hourly and sub-hourly
loads, market prices, retail load schedules and dynamic load profiles.
 Exponential Smoothing
o Ideal for projecting customer growth trends that support
monthly sales and peak forecasting applications.
 ARIMA
o For seasoned time series professionals who want to
visualize how historical data patterns extend into the future.
 Regression
o Regression is the workhorse of the energy forecasting
professional. No other tool lets you build multi-variate
models faster.
 Neural Networks
o Essential for short-term forecasting where modeling the
nonlinear response between loads and weather matters the
most.
 When you use MetrixND for your forecasting needs, you can:
 Use existing sources of meter and other data for more accurate
forecasts. MetrixND works with Excel® spreadsheets and a variety
of databases, including Microsoft Access®, SQL Server®,
ORACLE®, MV-90, and MV-Star.
 Model all data frequencies: sub-hourly, hourly, daily, weekly,
monthly, quarterly and annual data.
 Display all aspects of your forecasts with effective, easy-to-produce
graphics.
 Create analysis variables on the fly using spreadsheet-like formulas
that allow you to try different things without having to learn a
programming language.
KITTIPONG METHAPRAYOON, “GENERATION PLANNING AND MARKET OPERATION STRATEGIES IN THE
SOUTHWEST POWER POOL ENERGY IMBALANCE SERVICE MARKET.”
20
http://www.itron.com/pages/products_detail.asp?id=itr_000485.xml
19


21
Model hourly loads and calibrate automatically to forecasts of daily
energy and peak demand.
 Compare alternative model specifications by selecting from
competing models quickly and efficiently.
 Improve your understanding of historical outcomes by dragging and
dropping alternative forecast drivers into your model to view the
impact on your forecast.
 Improve your forecast by including a time series model of the
residuals.
 Customize forecasting models by writing powerful macros using Microsoft
Visual Basic® for Applications.
o MetrixLT – long term forecasting
 MetrixLT is designed specifically for developing load shape forecasts by end
use, class of service, system total or other user defined segments. With
MetrixLT, it’s easy to create “bottom-up” system load forecasts that build
from the end-use, rate class, or revenue class level.
 Incorporates end use models into monthly econometric models.
 When you use MetrixLT for your long-term forecasting solution, you
can:
 Import data from a variety of data sources, including Microsoft
Excel®, Microsoft Access®, Microsoft SQL Server®, Oracle®, EEI
files, or MetrixND®.
 Define and create day type shapes.
 Create normal, mild and extreme weather patterns that support longterm energy forecast under alternative weather scenarios.
 Compute billing-cycle weighted heating and cooling degree variables
for user-defined temperature break points.
 Calibrate load shapes to actual or forecasted energy on an annual or
monthly basis.
 Calibrate load shapes to actual or forecasted peak demand.
 Adjust load shapes for losses and DSM impacts.
 Aggregate end-use, rate class, or revenue class load shapes to the
system level.
 Calibrate long-term load forecasts to actual system loads or shortterm system load forecasts.
 Create annual or monthly reports that summarize sales and peaks.
 Export load shapes forecasts to Microsoft Excel®, Microsoft
Access®, or formatted text files.
o MetrixIDR – Short-term load forecasting.
 MetrixIDR System Operations uses Itron’s industry-leading MetrixND®
software as the forecasting engine to generate accurate sub-hourly, hourly or
daily short-term forecasts automatically, for electricity and natural gas
markets. By combining historical load data with weather and calendar
information, MetrixIDR provides system operators and energy traders with
the real-time load forecasts required to operate successfully in today’s
dynamic real-time markets.
Advanced Control Systems (ACS) – STLF21
o PRISM STLF utilizes historical load and weather data to forecast the system load
automatically every hour, for a 168-hour (7-day) rolling forecast. You can also request
a 15-day forecast at any time.
o User can add weights to weather adaptive or similar day forecasting, making it entirely
one or the other (in the extreme case).
o Uses ARIMA and Exponential Smoothing Models.
http://www.acsatlanta.com/pages/scada_stlf.html



Pattern Recognition Technologies, Inc. (PRT)22
o This company advises (at least through 2006-7) all 8 ISOs (e-ISOForecast platform) –
as well as utilities and other customers and EPRI – on load forecasting for electricity
and natural gas.
o PRT products and services are all based on state-of-the-art intelligent systems concepts
emphasizing such technologies as artificial neural networks, fuzzy logic, and
evolutionary computing/genetic algorithms.
MAISY/Jackson Associates23
o Jackson Associates (JA) has provided energy and hourly load forecasting models and
forecasting services since 1982. Modeling methodologies include:

Econometric Models - statistical single and multi equation models
 End-Use Models - models with end-use detail such as space heating, air
conditioning, lighting, etc.
o JA models have been applied to provide short-term, mid-term and long-term energy,
revenue and hourly load forecasts as well as analysis of energy efficiency, DSM,
demand response, distributed generation technology choice, customer acquisition and
other energy-related analysis.
o Performed Study with BIG DATABASE of hourly loads – see its website.
EPRI
o Developed ANNSTLF model.24
http://www.prt-inc.com/index.htm
http://www.maisy.com/menergy.htm
24
http://my.epri.com/portal/server.pt?Abstract_id=000000000001018873
22
23