000			03841nam a2200433 4500
005			20260818114246.0
008			080998 th eng
035			_a.b11493033
099		9	_aAIT Thesis no.CS-95-12
100	1		_aLee, Yean-shin
245	1	0	_aForecasting of power distribution load in Tainan district
260			_aBangkok : _bAsian Institute of Technology, _c1995
300			_a88 leaves + _e1 online resource
490	1		_aThesis ; _vno. CS-95-12
500			_aA thesis submitted in partial fulfillment of the requirement for the degree of Master of Engineering
502			_aThesis (M.Eng.) - Asian Institute of Technology, 1995
520			_aAlthough considerable work has been done in the past on the application of some type of systematic approach to electric power forecasting, its application to distribution load forecasting has unfortunately been somewhat neglected, especially the short-term distribution load forecasting for only certain areas like Taiwan Power Company (TPC) Tainan District. Recently years, due to the uprising of environmental protection consciousness, the land acquisition of not only power plants but also secondary substations becomes more difficult than before. Most Districts of TPC will face the embarrassment of power shortage and area rationing of power commodity predictably will be seen more often. In these circumstances, reliable short-term distribution load forecasting tools will enable TPC to plan ahead of time for peak demand and inform their customers as earlier as possible when area rationing is inevitable. Three forecasting approaches namely Neural Network, Box-Jenkins and Winters were investigated through the study cin distribution daily peak load and weekly peak load forecasting for TPC in Tainan District. The results showed that the daily peak load forecasting performance by Box-Jenkins model along with standardization method is the best among the approaches considered. For weekly peak load data the Winters' method gives the best forecasting performance with lead times from one to three weeks. For daily peak load data, the accuracy of forecasting with Box-Jenkins seasonal models is much better than that of non-seasonal models. Moreover, with standardized daily peak load data the forecasting performance is improved significantly. The Backpropagation Networks can not give a good forecasting performance when only time series data are used.
650	1	0	_aElectric power-plants _xTaiwan _xTainan _xLoad
700	1		_aHuynh, Ngoc Phien, _eChairperson
700	1		_aDo, Ba Khang, _eExamination Committee
700	1		_aNagarur, Nagendra N., _eExamination Committee
710	2		_aR.O.C. Government, _eScholarship donor
810	2		_aAsian Institute of Technology. _tThesis ; _vno. CS-95-12
856			_3Full-Text _uhttp://203.159.5.9/ait-thesis/detail.php?q=B14996
907			_a.b11493033 _bmnait _cv
902			_a250325
998			_b0 _c960624 _dm _ea _fv _g0
945			_lmnait
945			_lmnait
945			_lmnarc
945			_lmnarc
942			_c20
942			_c40
909			_aBarcode : 30050120675847 _bCREATED : 1996-05-21 _cRECORD # : i11758028 _dLPATRON : 0 _eLCHKIN : - _f# RENEWALS : 0 _g# OVERDUE : 0 _hIUSE3 : 0 _iTOT CHKOUT : 0 _jTOT RENEW : 0
909			_aBarcode : 30050120675706 _bCREATED : 1996-05-21 _cRECORD # : i1175803x _dLPATRON : 1011811 _eLCHKIN : 2000-08-18 _f# RENEWALS : 0 _g# OVERDUE : 0 _hIUSE3 : 0 _iTOT CHKOUT : 1 _jTOT RENEW : 0
909			_aBarcode : 30050120356299 _bCREATED : 2016-06-14 _cRECORD # : i13035903 _dLPATRON : 0 _eLCHKIN : - _f# RENEWALS : 0 _g# OVERDUE : 0 _hIUSE3 : 0 _iTOT CHKOUT : 0 _jTOT RENEW : 0
909			_aBarcode : 30050120914972 _bCREATED : 2019-11-21 _cRECORD # : i13231698 _dLPATRON : 0 _eLCHKIN : - _f# RENEWALS : 0 _g# OVERDUE : 0 _hIUSE3 : 0 _iTOT CHKOUT : 0 _jTOT RENEW : 0
999			_c46462 _d46462