A regression-based approach to short-term system load forecasting
A linear regression-based model for the calculation of short-term system load forecasts is described. The model's most significant aspects fall into the following areas: innovative model building, including accurate holiday modeling by using binary variables and temperature modeling by using he...
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Veröffentlicht in: | IEEE transactions on power systems 1990-11, Vol.5 (4), p.1535-1547 |
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container_title | IEEE transactions on power systems |
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creator | Papalexopoulos, A.D. Hesterberg, T.C. |
description | A linear regression-based model for the calculation of short-term system load forecasts is described. The model's most significant aspects fall into the following areas: innovative model building, including accurate holiday modeling by using binary variables and temperature modeling by using heating and cooling degree functions; robust parameter estimation and parameter estimation under heteroskedasticity by using weighted least-squares linear regression techniques; use of 'reverse errors-in-variables' techniques to mitigate the effects on load forecasts of potential errors in the explanatory variables; and distinction between time-independent daily peak load forecasts and the maximum of the hourly load forecasts in order to prevent peak forecasts from being negatively biased. The model was tested under a wide variety of conditioning and is shown to produce excellent results.< > |
doi_str_mv | 10.1109/59.99410 |
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The model's most significant aspects fall into the following areas: innovative model building, including accurate holiday modeling by using binary variables and temperature modeling by using heating and cooling degree functions; robust parameter estimation and parameter estimation under heteroskedasticity by using weighted least-squares linear regression techniques; use of 'reverse errors-in-variables' techniques to mitigate the effects on load forecasts of potential errors in the explanatory variables; and distinction between time-independent daily peak load forecasts and the maximum of the hourly load forecasts in order to prevent peak forecasts from being negatively biased. 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The model's most significant aspects fall into the following areas: innovative model building, including accurate holiday modeling by using binary variables and temperature modeling by using heating and cooling degree functions; robust parameter estimation and parameter estimation under heteroskedasticity by using weighted least-squares linear regression techniques; use of 'reverse errors-in-variables' techniques to mitigate the effects on load forecasts of potential errors in the explanatory variables; and distinction between time-independent daily peak load forecasts and the maximum of the hourly load forecasts in order to prevent peak forecasts from being negatively biased. The model was tested under a wide variety of conditioning and is shown to produce excellent results.< ></abstract><cop>United States</cop><pub>IEEE</pub><doi>10.1109/59.99410</doi><tpages>13</tpages></addata></record> |
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subjects | 290100 - Energy Planning & Policy- Energy Analysis & Modeling 292000 - Energy Planning & Policy- Supply, Demand & Forecasting 296000 - Energy Planning & Policy- Electric Power ACCURACY Economic forecasting ELECTRIC POWER INDUSTRY ENERGY MODELS ENERGY PLANNING, POLICY AND ECONOMY FORECASTING INDUSTRY Load flow Load forecasting LOAD MANAGEMENT Load modeling MANAGEMENT MATHEMATICS Parameter estimation Power system control Power system modeling Power system security Power systems Predictive models REGRESSION ANALYSIS STATISTICS |
title | A regression-based approach to short-term system load forecasting |
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