Title :
Two-level seasonal model based on hybrid ARIMA-ANFIS for forecasting short-term electricity load in Indonesia
Author :
Suhartono ; Puspitasari, Indah ; Akbar, M.S. ; Lee, Moon Ho
Author_Institution :
Dept. of Stat., Inst. Teknol. Sepuluh Nopember, Surabaya, Indonesia
Abstract :
The aim of this research is to develop a forecasting model for half-hourly electricity load in Java-Bali Indonesia by using two-level seasonal model based on hybrid ARIMA-ANFIS. This two-level forecasting model is developed based on the ARIMA model at the first level and ANFIS for the second level. The forecast accuracy is compared to the results of the individual approach of ARIMA and ANFIS. Data about half-hourly electricity load for Java-Bali on 1st January 2009 to 31st December 2010 period are used as case study. The results show that two-level seasonal hybrid ARIMA-ANFIS model with Gaussian membership function yields more accurate forecast values than individual approach of ARIMA and ANFIS model for predicting half-hourly electricity load, particularly up to 2 days ahead. This hybrid ARIMA-ANFIS model yields MAPE 1.78% for forecasting 7 days ahead and it is less than 2% as a benchmark value from Indonesian Electricity Company.
Keywords :
Gaussian processes; autoregressive moving average processes; fuzzy reasoning; load forecasting; neural nets; power engineering computing; Gaussian membership function; Java-Bali Indonesia; adaptive neuro-fuzzy inference system; autoregressive integrated moving average; half-hourly electricity load; hybrid ARIMA-ANFIS; short-term electricity load forecasting; two-level seasonal model; Data models; Electricity; Forecasting; Load forecasting; Load modeling; Mathematical model; Predictive models; ANFIS; ARIMA; electricity load; hybrid; two level;
Conference_Titel :
Statistics in Science, Business, and Engineering (ICSSBE), 2012 International Conference on
Conference_Location :
Langkawi
Print_ISBN :
978-1-4673-1581-4
DOI :
10.1109/ICSSBE.2012.6396642