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008 130314s2011 th uu|m rtt 0| a1eng d
035 _a.b12116166
099 9 _aAIT Thesis no.ET-11-24
100 0 _aChonlapat Leewarinpanich
245 1 0 _aMonthly electricity demand forecast for Provincial Electricity Authority using autoregressive integreted moving average (ARIMA) and artificial neural network (ANN):
_ba case study of Chiangmai
260 _aPathum Thani, Thailand :
_bAsian Institute of Technology,
_c2011
300 _a66 leaves :
_bill. +
_e1 online resource
490 1 _aThesis ;
_vno. ET-11-24
500 _aA thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Energy
502 _aThesis (M.Sc.) - Asian Institute of Technology, 2011
520 _aThis study analyzes and forecasts monthly electricity demand for Provincial Electricity Authority in Chingmai with two approaches. They are Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN). The study focuses on monthly historical data of electricity consumption from 2003 to 2010. ARIMA method used in the study will use SPSS software. Artificial Neural Network (ANN) approach applied the multi - layer perceptron and backpropagation algorithm and MATLAB program has been used for the study. This study involves not only past electricity consumption pattern, but also local socio - economic and climatic factors influencing electricity demand in the model such as monthly average temperature, monthly average maximum temperature, monthly average minimum temperature, number of customers and Ft charge. According to the study, various error measures (MPE, MAPE, MAE and RMSE) are used to evaluate the model performance. All of the error measures confirm that forecasted consumption results from ANN are closer to the actual data than forecasted demand from ARIMA. The MPE, MAPE, MAE and RMSE of testing data from ARIMA model are - 2.42%, 4.53%, 9.16 and 12.28, respectively. The MPE, MAPE, MAE and RMSE of testing data from ANN model are - 0.21%, 1.91%, 3.58 and 4.68, respectively. Therefore, ANN is an attractive technique applied for electricity demand forecast in Chiangmai.
650 0 _aElectric power consumptionr
_zThailand
_zChiang Mai
700 1 _aMarpaung, Charles O.P.,
_eChairperson
700 0 _aWeerakorn Ongsakul,
_eExamination Committee
700 1 _aSingh, Jai Govind,
_eExamination Committee
710 2 _aPEA,
_eScholarship donor
710 2 _aAsian Institute of Technology Education Cooperation Project,
_eScholarship donor
710 2 _aRoyal Thai Government Fellowship,
_eScholarship donor
810 2 _aAsian Institute of Technology.
_tThesis ;
_vno. ET-11-24
856 _3Full-Text
_uhttp://203.159.5.9/ait-thesis/detail.php?q=B02253
907 _a.b12116166
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945 _lmnait
945 _lmnait
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