TY - JOUR
T1 - Short-run forecasts of electricity loads and peaks
AU - Ramanathan, Ramu
AU - Engle, Robert
AU - Granger, Clive W.J.
AU - Vahid-Araghi, Farshid
AU - Brace, Casey
PY - 1997/1/1
Y1 - 1997/1/1
N2 - This paper reports on the design and implementation of a short-run forecasting model of hourly system loads and an evaluation of the forecast performance. The model was applied to historical data for the Puget Sound Power and Light Company, who did a comparative evaluation of various approaches to forecasting hourly loads, for two years in a row. The results of that evaluation are also presented here. The approach is a multiple regression model, one for each hour of the day (with weekends modelled separately), with a dynamic error structure as well as adaptive adjustments to correct for forecast errors of previous hours. The results show that it has performed extremely well in tightly controlled experiments against a wide range of alternative models. Even when the participants were allowed to revise their models after the first year, many of their models were still unable to equal the performance of the authors' models.
AB - This paper reports on the design and implementation of a short-run forecasting model of hourly system loads and an evaluation of the forecast performance. The model was applied to historical data for the Puget Sound Power and Light Company, who did a comparative evaluation of various approaches to forecasting hourly loads, for two years in a row. The results of that evaluation are also presented here. The approach is a multiple regression model, one for each hour of the day (with weekends modelled separately), with a dynamic error structure as well as adaptive adjustments to correct for forecast errors of previous hours. The results show that it has performed extremely well in tightly controlled experiments against a wide range of alternative models. Even when the participants were allowed to revise their models after the first year, many of their models were still unable to equal the performance of the authors' models.
KW - Comparative methods
KW - Energy forecasting
KW - Exponential smoothing
KW - Forecasting competitions
KW - Regression methods
UR - http://www.scopus.com/inward/record.url?scp=0031168758&partnerID=8YFLogxK
U2 - 10.1016/S0169-2070(97)00015-0
DO - 10.1016/S0169-2070(97)00015-0
M3 - Article
AN - SCOPUS:0031168758
SN - 0169-2070
VL - 13
SP - 161
EP - 174
JO - International Journal of Forecasting
JF - International Journal of Forecasting
IS - 2
ER -