02865nas a2200265 a 450000500170000000800410001703500150005810000290007324501960010226000670029830000440036549000270040950001120043650200570054852015130060565000540211870000410217270000470221370000460226071000280230671000840233471000570241881000590247585600650253420260818155749.0130314s2011    th uu|m rtt   0|  a1eng d  a.b121161660 aChonlapat Leewarinpanich10aMonthly electricity demand forecast for Provincial Electricity Authority using autoregressive integreted moving average (ARIMA) and artificial neural network (ANN):ba case study of Chiangmai  aPathum Thani, Thailand :bAsian Institute of Technology,c2011  a66 leaves :bill.  +e1 online resource1 aThesis ;vno. ET-11-24  aA thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Energy  aThesis (M.Sc.) - Asian Institute of Technology, 2011  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. 0aElectric power consumptionrzThailandzChiang Mai1 aMarpaung, Charles O.P.,eChairperson0 aWeerakorn Ongsakul,eExamination Committee1 aSingh, Jai Govind,eExamination Committee2 aPEA,eScholarship donor2 aAsian Institute of Technology Education Cooperation Project,eScholarship donor2 aRoyal Thai Government Fellowship,eScholarship donor2 aAsian Institute of Technology.tThesis ;vno. ET-11-24  3Full-Textuhttp://203.159.5.9/ait-thesis/detail.php?q=B02253