APPLICATION OF LEAST ABSOLUTE VALUE PARAMETER-ESTIMATION BASED ON LINEAR-PROGRAMMING TO SHORT-TERM LOAD FORECASTING

Short term load forecasting employs load models that express the effec ts of influential variables on system load. The model coefficients are found by fitting the load model to a data base of previous loads and observations of the variables, and then solving the resulting overdete rmined system of equations. The coefficients thus obtained are critica l to the forecasting process, as they directly affect its final predic tive accuracy. This study compares two linear static parameter estimat ion techniques as they apply to the twenty-four hour off-line forecast ing problem. Here a least squares and a least absolute value based lin ear programming algorithm will be used to simulate the forecast respon se of three twenty-four hour off-line load models. The three load mode ls are (I) a multiple linear regression model, (2) a harmonic decompos ition model and (3) a hybrid multiple linear regression/harmonic decom position model. These models are simplistic in nature and their primar y purpose is to provide a basis for comparing the two parameter estima tion techniques. The results obtained for each estimation algorithm vi a each load model, using the same data bases and forecasting periods, are presented and form the basis for comparisons presented in the pape r. (C) 1997 Elsevier Science Ltd