Electricity load forecasting using hybrid of multiplicative double seasonal exponential smoothing model with artificial neural network

• Main Author: Fadhil, Naam Salem
• Format: Thesis
• Published: 2013
• Subjects: QC Physics
• Online Access: http://eprints.utm.my/id/eprint/42112/; http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:81860

Electricity load forecasting often has many properties such as the nonlinearity, double seasonal cycles, and others those may be obstacles for accurate forecasting using some classical statistical models. Many papers in this field have proposed using double seasonal (DS) exponential smoothing model to forecast. These were found that electricity load forecasting using DS exponential smoothing model will be fitted, since this model studies the double seasonal effects those are in the studied data. Using artificial neural network (ANN) as a modern approach may also enable for more fitted forecasting, since this approach can deal with the non-linearity components of load data .The purpose of this study is improving the electricity load forecasting by building the hybrid model which includes a double seasonal exponential smoothing with an artificial neural network .This hybrid model will be studied the double seasonal effects and non-linearity components together those are in the electricity load data .The strategy of building this hybrid model is by entering ANN output as an input in double seasonal exponential smoothing model. The data sets are taken from three stations with different electricity load characteristics such as a residential, industrial, and the city center .The electricity load testing forecast of DS exponential smoothing-ANN hybrid gave the most minimum mean absolute percentage error (MAPE) measurement comparing with the electricity load testing forecasts of DS exponential smoothing and ANN for all electricity load data sets. In conclusion, DS exponential smoothing-ANN hybrid model are the most fitted for every electricity load data which contains the double seasonal effects and non-linearity components.