TESLA, Inc.

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Description

We're proud of our Model. Here we offer a full discussion of our modelling philosophy for those who want to understand the details. The TESLA Model is an extremely accurate menu-driven system for energy load analysis and forecasting. It consists of two major components: the forecasting module and the 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 modelling environment deliver medium and long-term estimates based on economic projections and alternative weather scenarios. TESLA can also 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 breakdown 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 solve several management problems in the power industry. Our modelling philosophy is to use as much information as possible. Since load is determined as a consequence of various factors and multiple decisions by many different people, we believe that accuracy in forecasting requires that we take into account a large number of relevant factors. 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 is the 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 arises 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.

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