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 Model Description
The Tesla Model
Components of Load
Information Intensity
Tesla Output
Customized Reports
Operating Environment
Variables
Fee Structure
The Tesla load forecasting model is an extremely accurate menu-driven system
for energy load analysis and forecasting. It consists of two major components:
Forecasting module
Weather correction module
The forecasting module predicts load over a wide range of time horizons. It can
provide very timely operational forecasts based on near term weather forecasts,
and -- in the same modeling environment -- deliver medium- and long-term
estimates based on economic projections and alternative weather scenarios. Tesla
also can be used for long-term strategic planning simulations involving
alternative projections of both weather and economic conditions.
The weather correction module decomposes the observed load based on observed
weather, seasonal normal weather, and other causal variables. The result is a
decomposition of load into the portion that would have occurred under normal
weather and that which occurred due to weather deviations from seasonal norms.
Both modules are packaged with an operator interface that provides a standard
set of Windows-type controls for using and maintaining the model. It automates
initialization of forecasts and analyses and displays the results on an hourly,
sub-hourly, or summarized basis, in either tabular or graphical form. It also
automates data maintenance tasks.
The accuracy, power, and flexibility of the Tesla system have led to its
application to several management problems in the power industry, and Tesla,
Inc. has developed derivative products from the basic design.
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Our modeling philosophy is to use as much relevant information as possible.
To apply this philosophy to the estimation of electricity or gas industries, we
view load as arising from multiple processes. These processes can be grouped
into four categories, each of which must be approached with different analytical
tools:
Identifiable – load arising from identifiable, quantifiable influences, mainly
consisting of human behaviour within a physical environment that can be
predicted based on the clock and calendar, weather effects, and effects arising
from macroeconomic and demographic considerations. The appropriate techniques
for forecasting this component of load are very large scale,
highly-parameterized regression analysis, time-varying parameter estimation, and
Bayesian estimation.
Latent – load arising from slow moving processes which can be readily observed
in the data, particularly once the identifiable load is removed, but which
cannot reliably be attributed directly to any particular factor. The techniques
we use for this component are Box-Jenkins analysis with time-varying
autocorrelation parameters, path analysis, principal components analysis,
response surface estimation and other latent variable techniques.
Exceptional – load arising from exceptional events, where the events can be
predicted, or are known in advance, and there is a body of experience from
similar events. Appropriate techniques for this component of load include neural
nets, and intensive close client contact and reporting.
Unpredictable – load arising from unforeseeable events, or from foreseeable
events for which there is no body of experience. Useful techniques for this
component include fuzzy logic systems, contingency analyses, and Monte Carlo
simulation.
The Tesla model is more accurate than any other load model of which we are
aware. We believe that no other load model can be as accurate as Tesla without
employing the same range of tools as Tesla.
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In the aggregate, many factors contribute to load. Consequently, 2000 or more
parameters in a particular model is not unusual. To handle this data load, we
have developed a process we refer to as very large linearized systems (VLLS).
The VLLS approach allows us to build a model that is responsive,
information-intensive, and conformal to complex, irregular response functions.
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Modelling of load can readily be done for a client's entire service area, for
larger areas (e.g. a region or country), or for smaller areas or units
(franchise sub-areas, busses, feeders and stations, customer classes, and large
individual commercial/industrial users). A variety of different statistics can
be derived and displayed. The precise layout of each type of display, and
computational variations in how certain statistics are to be derived, are
customized for each user. Depending on selections made by the user during
implementation, any or all of the following statistics, along with many others,
can be provided:
- Load forecast
- Every hour or sub hour
- Scale and location of peak in the day
- In-sample comparisons of actual versus forecasted
- Standard error of estimate measurements
- Weather effects decomposition
- Temperature response profiles (and other weather response profiles)
- Shape of the temporal load profile
- Around peak / “peak duration”
- Deformations in shape over time, and by day type and season
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In addition to the standard Tesla output, we can supply customized reports to
fulfil any need. Tesla is also available integrated with the SORITEC
programmable econometric package, which allows the user to extend and modify the
system. SORITEC has presentation-quality graphics capability, a spreadsheet tool
that facilitates data exchange with other software, and a report writer/table
generator, as well as a large array of statistical capabilities.
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Tesla can be delivered on several computer platforms and
configurations, including Windows 9x/NT/2000/XP/2003, UNIX workstations, or on
mainframe systems. The basic compute engine can be accessed through a
menu-driven graphical interface, directly by other system programs (e.g. an
energy management system), or from Microsoft Excel.
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Variables
A discussion on Tesla Variables can be found here.
Tesla is available either under a fixed-price contract or on a
pay-for-performance basis.
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