TY - JOUR
T1 - Very Short Term Load Forecasting Using Hybrid Regression and Interval Type -1 Fuzzy Inference
AU - Jamaaluddin, J.
AU - Robandi, I.
N1 - Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
PY - 2018/12/5
Y1 - 2018/12/5
N2 - The growth of electricity consumption in this world is getting higher. The operation of the electric power starts from the generation system, Transmission system, and distribution system up to the load. All systems must be well integrated. Power generation settings should be appropriate. Therefore, load forecasting is important to do in generation system so that it is not too high from the existing load. There are two kinds of load forecasting; Short Term and Very Short Term. The very short term load forecasting is to forecast the load amount in every 30 minutes on one day before the day of loading. This research aims to discuss very short term load forecasting which uses hybrid regression method in the primary data of its loading history forecasting with Interval Type - 1 Fuzzy Inference System (IT-1 FIS). The finding indicates that the forecasting in 2015 obtained error of 0,9558%, and 1,4226% in 2016.
AB - The growth of electricity consumption in this world is getting higher. The operation of the electric power starts from the generation system, Transmission system, and distribution system up to the load. All systems must be well integrated. Power generation settings should be appropriate. Therefore, load forecasting is important to do in generation system so that it is not too high from the existing load. There are two kinds of load forecasting; Short Term and Very Short Term. The very short term load forecasting is to forecast the load amount in every 30 minutes on one day before the day of loading. This research aims to discuss very short term load forecasting which uses hybrid regression method in the primary data of its loading history forecasting with Interval Type - 1 Fuzzy Inference System (IT-1 FIS). The finding indicates that the forecasting in 2015 obtained error of 0,9558%, and 1,4226% in 2016.
UR - http://www.scopus.com/inward/record.url?scp=85058308431&partnerID=8YFLogxK
U2 - 10.1088/1757-899X/434/1/012209
DO - 10.1088/1757-899X/434/1/012209
M3 - Conference article
AN - SCOPUS:85058308431
SN - 1757-8981
VL - 434
JO - IOP Conference Series: Materials Science and Engineering
JF - IOP Conference Series: Materials Science and Engineering
IS - 1
M1 - 012209
T2 - 3rd Annual Applied Science and Engineering Conference, AASEC 2018
Y2 - 18 April 2018
ER -
