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BỘ GIÁO DỤC VÀ ĐÀO TẠO TRƯỜNG ĐẠI HỌC SƯ PHẠM KỸ THUẬT THÀNH PHỐ HỒ CHÍ MINH LUẬN VĂN THẠC SĨ TRỊNH TIẾN UY DỰ BÁO PHỤ TẢI CHO TỈNH KIÊN GIANG SỬ DỤNG NEURAL NETWORK NGÀNH: KỸ THUẬT ĐIỆN - 60520202 SKC007528 Tp Hồ Chí Minh, tháng 10/2017 BỘ GIÁO DỤC VÀ ĐÀO TẠO TRƢỜNG ĐẠI HỌC SƢ PHẠM KỸ THUẬT THÀNH PHỐ HỒ CHÍ MINH LUẬN VĂN THẠC SĨ TRỊNH TIẾN UY DỰ BÁO PHỤ TẢI CHO TỈNH KIÊN GIANG SỬ DỤNG NEURAL NETWORK NGÀNH: KỸ THUẬT ĐIỆN - 60520202 Hướng dẫn khoa học: PGS.TS QUYỀN HUY ÁNH Tp Hồ Chí Minh, tháng 10 năm 2017 Luận văn thạc sĩ GVHD: PGS.TS Quyền Huy Ánh C=[A,B] xlswrite(ten,C); Saved function nhapdb_Callback(hObject, eventdata, handles) load Config.mat; za0=transpose(za0); n=[nx;za0]; HC3 load Data.mat data=transpose(n/1e6) dataz=data(:,size(data,2)); hide=5; ep=1000; dta=data; [m,nh]=size(dta); p=dta(:,1:nh-1); t=dta(:,2:nh); net=feedforwardnet([hide],'trainrp'); net.trainParam.show = NaN; net.trainParam.epochs = ep; net.trainParam.goal = 0; net.trainParam.min_grad=1e-5; net.trainParam.max_fail=25; [net,tr]=train(net,p,t); za0=sim(net,dataz)*1e6; nx=n; ck=0 bngay=bngay+1 save('Config.mat','za0','n','nx','bngay','ck','y'); set(handles.ra1,'string',' '); set(handles.ra3,'string',' '); set(handles.ra4,'string',' '); set(handles.ra2,'string',' '); Data load Data.mat filename = 'Update data month.xlsx'; xlswrite(filename,C) function nhaptt_Callback(hObject, eventdata, handles) load Config.mat; datax=datax' n=[nx;datax]; HC3 load Data.mat data=transpose(n/1e6) dataz=data(:,size(data,2)); hide=5; ep=1000; HVTH: Trịnh Tiến Uy - 1620632 128 Luận văn thạc sĩ GVHD: PGS.TS Quyền Huy Ánh dta=data; [m,nh]=size(dta); p=dta(:,1:nh-1); t=dta(:,2:nh); net=feedforwardnet([hide],'trainrp'); net.trainParam.show = NaN; net.trainParam.epochs = ep; net.trainParam.goal = 0; net.trainParam.min_grad=1e-5; net.trainParam.max_fail=25; [net,tr]=train(net,p,t); za0=sim(net,dataz)*1e6; nx=n; ck=0 bngay=bngay+1 save('Config.mat','za0','n','nx','bngay','ck','y'); set(handles.ra1,'string',' '); set(handles.ra3,'string',' '); set(handles.ra4,'string',' '); set(handles.ra2,'string',' '); Data load Data.mat filename = 'Update data month.xlsx'; xlswrite(filename,C) DATA.m function varargout = Data(varargin) gui_Singleton = 1; gui_State = struct('gui_Name', mfilename, 'gui_Singleton', gui_Singleton, 'gui_OpeningFcn', @Data_OpeningFcn, 'gui_OutputFcn', @Data_OutputFcn, 'gui_LayoutFcn', [] , 'gui_Callback', []); if nargin && ischar(varargin{1}) gui_State.gui_Callback = str2func(varargin{1}); end if nargout [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:}); else gui_mainfcn(gui_State, varargin{:}); end function Data_OpeningFcn(hObject, eventdata, handles, varargin) handles.output = hObject; guidata(hObject, handles); HVTH: Trịnh Tiến Uy - 1620632 129 Luận văn thạc sĩ GVHD: PGS.TS Quyền Huy Ánh s=0; y=0; load Config.mat mh=size(n,1); if y>0 for i=1:mh if i==1 y1=y else y1=[y1;(y+i-1)] end end C=[y1 n] end if s==1 A=cell(mh,1) for i=1:mh A{i,1} = datestr(ngay+i-1,'dd/mm/yyyy') end B=num2cell(n) C=[A,B] end if s==2 A=cell(mh,1) for i=1:mh A{i,1} = datestr(ngay+7*i-7,'dd/mm/yyyy') end B=num2cell(n) C=[A,B] end set(handles.bang,'data',C); save ('Data.mat','C'); function varargout = Data_OutputFcn(hObject, eventdata, handles) varargout{1} = handles.output; function ngay_Callback(hObject, eventdata, handles) function ngay_CreateFcn(hObject, eventdata, handles) if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor')) set(hObject,'BackgroundColor','white'); end ERRORS.m function varargout = Errors(varargin) gui_Singleton = 1; gui_State = struct('gui_Name', mfilename, HVTH: Trịnh Tiến Uy - 1620632 130 Luận văn thạc sĩ GVHD: PGS.TS Quyền Huy Ánh 'gui_Singleton', gui_Singleton, 'gui_OpeningFcn', @Errors_OpeningFcn, 'gui_OutputFcn', @Errors_OutputFcn, 'gui_LayoutFcn', [] , 'gui_Callback', []); if nargin && ischar(varargin{1}) gui_State.gui_Callback = str2func(varargin{1}); end if nargout [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:}); else gui_mainfcn(gui_State, varargin{:}); end function Errors_OpeningFcn(hObject, eventdata, handles, varargin) handles.output = hObject; guidata(hObject, handles); function varargout = Errors_OutputFcn(hObject, eventdata, handles) varargout{1} = handles.output; function pushbutton1_Callback(hObject, eventdata, handles) Closereq SAVED.m function varargout = Saved(varargin) gui_Singleton = 1; gui_State = struct('gui_Name', mfilename, 'gui_Singleton', gui_Singleton, 'gui_OpeningFcn', @Saved_OpeningFcn, 'gui_OutputFcn', @Saved_OutputFcn, 'gui_LayoutFcn', [] , 'gui_Callback', []); if nargin && ischar(varargin{1}) gui_State.gui_Callback = str2func(varargin{1}); end if nargout [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:}); else gui_mainfcn(gui_State, varargin{:}); end function Saved_OpeningFcn(hObject, eventdata, handles, varargin) handles.output = hObject; guidata(hObject, handles); function varargout = Saved_OutputFcn(hObject, eventdata, handles) varargout{1} = handles.output; function pushbutton1_Callback(hObject, eventdata, handles) closereq HVTH: Trịnh Tiến Uy - 1620632 131 Luận văn thạc sĩ GVHD: PGS.TS Quyền Huy Ánh PHỤ LỤC SỐ LIỆU PHỤ TẢI TỈNH KIÊN GIANG Giờ 1h 2h 3h 4h 5h 6h 7h 8h 9h 10h 11h 12h 13h 14h 15h 16h 17h 18h 19h 20h 21h 22h 23h 24h Ngày 11/10/2014 184.4 185.4 182.1 182.4 192.5 178.4 179.8 222.8 234.1 209.2 201.9 218.0 234.3 246.8 246.3 235.2 224.1 238.3 252.6 243.6 261.6 244.6 223.0 213.4 11/11/2014 208.0 200.1 198.0 197.6 201.8 182.7 181.0 228.7 237.2 216.6 202.3 206.2 219.3 241.7 244.1 248.4 219.7 232.0 243.8 244.0 269.1 249.5 233.3 222.0 11/12/2014 215.3 207.4 202.7 203.7 209.2 188.0 179.5 221.8 236.1 215.7 207.7 218.7 230.2 241.9 246.7 248.4 211.8 241.3 236.4 238.7 264.2 235.6 222.5 213.7 11/13/2014 206.8 196.0 195.4 196.6 204.2 188.3 181.0 228.9 239.6 217.1 207.0 232.3 248.4 248.3 254.7 259.2 216.7 239.2 243.9 248.4 275.4 252.6 237.5 225.1 11/14/2014 214.6 208.7 204.8 197.4 199.0 189.1 180.5 225.0 234.8 212.9 197.4 219.7 226.1 233.7 237.8 237.8 222.8 226.6 230.0 233.1 246.6 227.4 217.6 207.4 11/15/2014 201.1 192.0 192.0 193.5 198.1 180.4 180.5 225.2 230.5 208.1 192.6 214.4 225.4 233.5 242.2 245.8 184.3 215.2 223.2 226.9 248.8 234.7 223.4 199.1 11/16/2014 201.5 195.2 190.8 191.3 189.6 170.8 166.7 207.3 202.4 205.9 203.7 203.0 200.6 205.4 219.2 197.7 197.2 217.1 226.6 231.2 238.5 225.3 210.9 199.2 11/17/2014 190.3 187.0 183.5 181.5 190.3 172.9 170.7 222.2 228.0 210.5 204.6 224.4 235.6 243.0 248.9 251.1 217.5 233.8 238.6 238.0 248.5 235.1 227.5 218.3 11/18/2014 205.8 200.4 197.0 198.3 201.9 179.2 179.1 227.6 241.1 214.8 207.7 229.0 244.7 250.7 254.3 253.9 222.1 236.7 227.4 221.8 247.8 233.1 220.6 211.4 11/19/2014 209.4 200.2 196.7 193.4 199.1 182.0 179.0 208.1 217.5 205.3 203.6 221.6 237.8 237.2 246.0 252.8 231.5 228.8 226.2 228.9 239.2 228.7 212.9 205.7 11/20/2014 184.3 181.0 183.6 190.0 199.3 185.7 180.4 216.2 218.2 206.1 193.0 206.0 219.8 226.4 235.8 245.1 213.3 229.5 229.4 226.6 243.4 225.4 216.7 210.0 11/21/2014 200.0 196.6 191.9 188.8 199.5 181.8 149.6 223.3 220.7 208.2 195.6 216.2 228.6 238.4 242.5 245.8 219.1 228.5 240.6 237.8 254.1 230.5 210.7 208.5 11/22/2014 203.3 195.2 191.8 193.0 199.6 184.1 183.0 224.5 231.1 210.4 196.5 207.9 225.9 231.7 237.2 241.9 201.0 214.0 224.2 231.2 248.7 233.8 220.0 208.3 11/23/2014 198.9 192.0 188.4 186.1 191.8 174.9 172.4 200.0 203.2 208.5 191.2 193.1 196.5 202.2 209.6 212.3 192.5 219.1 227.1 234.2 236.8 223.2 208.8 197.0 11/24/2014 187.8 184.4 179.4 180.7 194.4 180.2 182.3 228.1 242.7 222.6 205.7 229.3 237.7 247.2 250.5 252.0 220.1 233.8 235.6 235.8 252.9 237.8 222.1 213.1 11/25/2014 207.5 199.7 198.2 202.0 209.7 193.0 187.4 236.0 250.8 227.2 209.3 229.4 239.0 253.3 259.9 266.8 235.0 238.9 244.9 245.2 258.2 248.1 235.0 214.3 11/26/2014 207.2 191.9 190.0 196.4 198.3 194.3 190.3 220.6 238.3 217.5 202.2 224.8 235.1 243.3 247.7 252.5 218.6 237.4 236.3 231.4 246.9 227.2 215.9 201.5 11/27/2014 194.4 189.3 188.8 188.1 195.3 185.4 184.1 229.0 240.0 219.1 207.8 227.0 236.0 243.2 246.6 251.9 234.6 238.2 237.2 229.3 242.4 227.7 224.5 213.7 11/28/2014 203.0 197.2 195.0 191.4 206.7 194.0 184.8 225.6 239.2 215.8 201.5 219.2 233.0 247.3 250.6 258.3 220.1 236.5 235.3 233.2 250.5 239.8 219.2 212.4 11/29/2014 198.8 184.8 186.2 188.7 197.4 189.4 188.5 232.3 238.2 217.2 200.3 221.3 231.2 242.6 238.8 251.7 199.9 224.3 226.6 225.3 245.6 234.1 219.7 207.2 11/30/2014 199.1 191.1 187.2 187.9 194.6 178.8 177.7 197.4 206.4 211.8 195.7 199.8 197.9 208.9 210.4 221.0 193.0 225.8 233.1 233.2 235.6 225.2 213.3 202.8 12/1/2014 188.7 184.9 180.8 182.3 194.5 188.7 181.8 221.4 236.4 213.0 200.8 215.1 231.2 243.9 251.3 261.6 230.3 239.3 237.0 230.8 248.4 235.3 221.7 210.6 HVTH: Trịnh Tiến Uy - 1620632 132 Luận văn thạc sĩ GVHD: PGS.TS Quyền Huy Ánh 12/2/2014 199.6 194.6 193.4 198.1 204.0 193.3 182.4 232.8 244.2 220.9 192.7 220.2 239.0 250.4 258.1 265.4 230.7 242.2 240.6 240.6 259.1 228.1 210.1 190.3 12/3/2014 211.1 207.2 204.2 205.9 212.4 197.9 193.8 237.2 243.8 220.3 205.1 226.6 239.4 243.3 243.0 262.9 231.9 219.8 216.6 236.8 256.6 238.8 228.4 213.2 12/4/2014 206.7 203.9 202.0 199.9 207.0 194.8 189.7 233.9 238.8 224.0 207.2 225.8 242.0 248.5 255.4 264.9 227.4 234.3 241.0 238.1 252.6 239.8 225.6 216.7 12/5/2014 210.7 203.8 202.3 201.2 212.5 195.7 188.7 222.7 241.7 225.7 209.8 225.1 239.9 248.9 254.7 255.8 229.2 240.8 239.2 235.4 254.1 239.7 229.7 224.1 12/6/2014 204.3 201.3 197.4 197.2 205.2 187.4 184.8 224.2 231.6 209.4 193.7 217.8 227.2 239.0 246.1 249.9 205.9 218.3 229.6 226.5 245.2 233.0 219.1 210.8 12/7/2014 200.9 191.9 188.7 187.7 192.8 176.6 174.6 191.6 202.7 206.1 201.2 198.6 199.3 206.9 212.3 215.8 206.7 221.3 211.6 235.3 235.1 223.8 208.4 201.2 12/8/2014 185.3 182.3 178.7 184.5 193.5 183.5 185.5 231.3 244.0 227.2 212.6 228.2 228.5 245.8 253.5 263.6 219.1 241.5 245.7 239.7 257.1 246.8 225.3 214.9 12/9/2014 203.6 199.6 196.2 204.4 207.1 192.3 189.3 234.8 248.8 227.0 217.2 235.4 240.2 256.2 265.4 267.5 235.3 237.5 240.9 240.7 262.6 239.0 220.6 214.1 12/10/2014 206.9 204.1 199.6 202.2 209.2 192.9 192.9 242.3 248.3 226.4 212.1 232.6 244.4 255.1 261.6 265.2 226.6 238.1 243.3 238.0 258.5 241.0 230.3 217.2 12/11/2014 207.2 204.8 202.5 203.0 210.3 195.2 190.1 235.8 241.9 222.3 210.1 227.8 242.4 250.5 260.5 268.9 229.2 236.0 241.4 240.1 256.0 237.1 224.9 214.5 12/12/2014 205.4 205.5 202.9 201.0 210.9 196.8 192.2 230.0 246.5 223.6 211.8 226.9 245.5 255.1 260.0 265.7 234.8 243.6 246.5 245.6 261.3 247.9 232.3 224.5 12/13/2014 214.5 205.4 195.1 198.0 198.4 190.4 186.6 229.9 238.6 214.4 201.4 224.6 233.7 240.5 247.4 254.5 211.2 224.0 227.9 230.7 250.5 242.2 222.6 214.5 12/14/2014 199.3 195.2 192.0 197.8 199.2 181.4 179.1 190.8 202.7 207.3 188.4 184.3 187.5 193.5 201.2 208.1 184.7 226.2 233.6 241.4 245.4 231.6 216.3 202.1 12/15/2014 195.0 187.3 187.2 188.7 199.0 189.5 191.3 232.9 245.9 226.8 216.7 233.4 248.6 261.6 264.9 272.8 233.6 242.0 248.0 245.7 261.2 242.6 231.4 216.2 12/16/2014 216.3 212.5 208.6 210.7 218.7 199.8 195.7 242.6 247.9 229.4 217.7 235.9 244.8 253.5 260.0 258.2 222.0 229.6 244.1 243.5 256.8 235.4 226.2 215.0 12/17/2014 208.4 200.5 198.8 197.4 205.5 192.4 189.1 230.5 240.8 217.0 203.7 214.7 221.0 236.5 245.0 252.7 219.2 231.8 238.0 230.7 247.9 236.1 221.1 209.9 12/18/2014 204.8 195.4 196.5 198.6 207.1 190.5 187.7 227.3 237.3 217.6 204.0 220.9 234.1 245.5 255.8 263.5 230.1 229.9 226.1 217.0 246.5 230.9 223.9 215.5 12/19/2014 206.5 196.3 194.6 192.8 198.0 186.9 181.6 222.9 225.6 205.0 189.2 207.6 219.3 226.5 232.4 239.0 212.5 225.1 228.1 222.3 238.2 226.1 211.7 201.0 12/20/2014 189.1 188.3 188.8 189.9 194.7 179.0 183.2 216.9 220.2 205.7 191.1 200.0 215.6 226.9 228.0 235.3 201.4 217.4 221.9 220.1 240.6 225.5 214.6 200.4 12/21/2014 193.8 192.3 188.4 187.1 186.7 171.3 169.0 188.7 195.5 196.5 190.6 186.1 182.7 185.3 192.2 202.7 189.3 195.0 215.7 231.5 229.0 212.0 199.8 191.7 12/22/2014 181.5 178.2 178.5 176.9 186.8 176.6 174.0 217.7 228.7 212.5 199.0 209.6 225.8 231.8 240.5 246.1 214.5 228.9 240.7 237.4 250.7 213.4 200.7 207.0 12/23/2014 197.7 193.0 190.5 196.2 205.1 188.5 182.0 223.3 229.2 213.3 197.4 207.5 219.1 236.2 244.5 244.6 214.1 224.9 226.6 226.9 239.9 225.8 215.7 204.9 12/24/2014 200.4 198.8 193.1 197.3 199.3 185.4 179.9 224.1 236.0 214.9 197.1 206.6 226.8 230.9 238.6 247.0 219.0 222.8 233.7 228.6 244.3 224.1 214.3 212.7 12/25/2014 197.5 191.5 186.1 189.8 196.1 182.8 170.8 211.4 217.7 203.7 195.2 205.9 218.0 228.5 233.8 242.1 204.4 223.5 234.3 231.1 241.7 228.7 222.8 200.0 12/26/2014 199.7 195.8 192.5 192.5 198.3 183.0 172.8 202.7 208.9 205.8 195.3 197.0 202.1 210.8 220.0 228.7 216.5 229.0 239.1 235.0 236.3 222.6 195.1 168.9 12/27/2014 166.5 166.0 185.2 189.1 198.3 181.9 178.1 209.7 215.3 197.5 189.2 203.8 210.4 214.8 225.5 236.1 192.0 209.5 224.8 227.8 230.3 221.1 210.7 178.4 12/28/2014 172.3 176.5 179.2 179.1 183.5 167.4 157.8 175.5 179.3 183.0 172.1 166.2 166.7 177.4 186.9 198.0 179.1 204.1 220.4 220.1 218.9 193.9 193.4 183.3 12/29/2014 175.7 172.6 168.7 170.8 184.0 176.6 167.3 207.8 221.1 202.5 190.0 204.5 217.3 225.8 235.0 245.7 220.0 223.5 237.6 234.2 238.6 223.5 203.3 179.5 12/30/2014 168.0 172.9 170.4 189.3 194.6 178.2 170.6 203.9 215.4 199.3 187.3 191.6 200.4 220.7 229.1 233.7 197.6 210.8 227.1 228.1 237.3 219.5 203.3 191.6 12/31/2014 186.9 180.8 177.9 175.7 177.8 158.5 149.4 169.5 171.7 153.5 143.0 161.3 164.9 171.6 180.5 189.1 165.9 183.6 202.3 205.9 216.4 200.3 185.5 173.6 HVTH: Trịnh Tiến Uy - 1620632 133 Luận văn thạc sĩ GVHD: PGS.TS Quyền Huy Ánh 1/1/2015 167.8 163.8 162.5 168.3 179.0 171.9 167.4 207.5 217.9 199.7 188.1 204.9 216.2 225.8 233.8 240.0 216.5 215.4 242.2 237.9 244.0 228.7 215.4 207.1 1/2/2015 199.0 176.8 173.9 177.0 179.3 180.2 175.3 222.4 233.4 217.4 203.0 219.6 232.8 241.3 247.4 254.9 215.8 228.0 251.1 246.8 253.1 237.8 219.8 212.8 1/3/2015 201.7 196.4 194.5 197.2 202.3 191.9 186.1 231.6 235.3 214.2 201.7 221.6 226.0 231.5 239.2 249.0 198.6 214.4 228.7 235.2 249.4 218.1 202.7 204.5 1/4/2015 199.6 197.1 194.2 195.7 201.3 185.4 178.8 197.3 201.2 208.4 201.3 203.0 189.3 199.9 206.2 213.7 192.4 205.6 235.1 229.7 233.2 216.6 202.0 193.8 1/5/2015 187.9 183.1 179.9 183.7 196.7 187.2 175.8 220.4 232.4 213.2 203.4 216.5 223.9 238.0 242.8 252.4 217.8 229.8 241.9 239.4 252.0 229.1 213.4 203.6 1/6/2015 204.4 197.8 197.3 201.6 204.1 192.6 179.1 224.8 232.2 210.6 201.3 219.7 229.9 238.6 242.7 249.2 214.8 228.1 247.4 245.8 253.0 232.8 193.1 186.3 1/7/2015 208.0 202.1 195.4 199.8 201.8 195.5 177.8 220.3 231.1 221.0 202.1 207.1 222.1 229.7 238.6 244.9 210.5 222.3 242.4 247.5 254.4 237.4 223.4 211.2 1/8/2015 208.0 202.1 195.4 199.8 201.8 195.5 177.8 220.3 231.1 221.0 202.1 207.1 222.1 229.7 238.6 244.9 210.5 222.3 242.4 247.5 254.4 237.4 223.4 211.2 1/9/2015 205.5 201.0 199.2 198.3 207.7 200.0 188.0 217.0 224.6 204.8 193.5 208.4 219.0 230.2 232.4 243.8 217.7 222.7 241.4 245.6 258.6 246.3 231.2 219.3 1/10/2015 214.9 203.5 199.7 198.1 198.8 187.2 180.2 218.1 225.0 205.9 189.2 205.4 226.6 235.2 240.8 245.2 201.8 202.3 233.1 233.2 249.8 234.4 219.3 210.6 1/11/2015 200.6 195.7 195.2 195.9 197.5 180.2 171.5 193.6 199.6 202.2 190.8 189.3 189.6 197.8 209.1 221.0 197.5 207.6 240.2 227.3 214.5 212.6 204.3 200.2 1/12/2015 193.6 187.1 188.9 191.0 194.2 185.8 169.1 215.8 230.6 211.1 200.0 214.1 230.3 239.9 247.3 250.0 216.4 215.6 245.3 246.0 251.0 233.3 217.8 210.8 1/13/2015 204.9 198.8 194.8 192.5 202.7 190.0 179.8 219.8 228.2 207.8 197.7 214.9 228.3 235.6 241.9 254.9 214.4 226.0 247.5 230.8 253.4 234.9 221.9 212.4 1/14/2015 203.7 196.6 196.8 195.5 203.8 188.1 179.4 215.0 219.0 201.0 188.2 206.1 218.6 224.1 236.6 244.0 205.1 218.8 247.2 243.8 252.8 231.5 222.9 202.8 1/15/2015 203.1 196.3 191.4 197.0 204.2 193.9 179.5 217.7 224.8 204.0 194.5 208.3 217.4 224.1 236.6 244.5 209.0 215.5 241.1 236.2 248.5 232.5 219.0 211.5 1/16/2015 205.0 199.7 197.9 203.3 207.0 200.6 183.4 219.9 226.4 200.9 193.7 208.0 218.2 222.1 232.7 242.3 213.3 214.1 241.0 242.8 254.3 239.8 244.8 239.7 1/17/2015 203.8 201.2 198.6 202.3 205.7 191.3 178.2 211.1 219.9 203.2 187.8 204.6 219.9 220.3 228.1 238.3 200.5 205.3 236.2 235.6 256.5 240.9 220.9 215.3 1/18/2015 200.5 195.2 192.6 199.7 203.2 178.6 156.3 170.8 173.9 174.9 171.2 163.1 165.1 172.5 181.2 191.0 180.7 185.1 242.5 245.9 248.7 228.2 212.5 202.3 1/19/2015 194.8 190.0 185.4 189.2 199.3 191.6 170.5 212.8 220.5 203.3 191.2 207.8 219.2 228.3 237.1 241.5 215.2 213.6 247.2 248.5 260.9 246.3 228.1 217.8 1/20/2015 213.4 209.8 208.1 211.0 222.2 206.1 183.1 222.5 231.0 212.1 202.2 217.2 226.5 236.5 243.2 255.1 219.7 225.4 245.8 246.8 267.0 245.7 233.9 224.1 1/21/2015 210.1 205.7 201.6 205.0 208.8 194.9 179.8 217.2 225.9 204.9 194.5 212.0 220.6 226.7 237.3 246.6 213.0 211.5 242.1 235.0 252.9 236.7 227.5 218.7 1/22/2015 211.4 202.9 201.3 203.2 209.3 198.3 175.5 211.1 223.1 203.8 192.5 190.1 215.1 223.7 234.8 251.5 214.4 212.5 236.0 231.2 250.6 240.9 217.8 210.1 1/23/2015 202.6 198.5 199.1 202.7 207.1 196.7 182.5 217.1 221.2 211.1 199.6 205.8 215.5 219.5 231.6 239.7 201.2 204.2 219.7 222.7 244.6 227.2 206.9 207.0 1/24/2015 199.9 195.8 195.7 194.5 202.6 193.2 171.7 199.9 182.2 171.1 162.8 173.0 180.2 187.2 193.7 196.1 164.6 201.5 221.4 221.6 243.5 229.4 213.6 205.1 1/25/2015 193.9 188.4 188.1 193.0 197.3 185.6 169.4 184.5 187.0 192.4 180.6 178.0 183.7 188.1 196.7 208.1 187.2 194.3 214.2 214.0 222.0 213.1 197.7 186.6 HVTH: Trịnh Tiến Uy - 1620632 134 DỰ BÁO PHỤ TẢI CHO TỈNH KIÊN GIANG SỬ DỤNG NEURAL NETWORK APPLICATION OF NEURAL NETWORK TO LOAD FORECASTING FOR KIEN GIANG PROVINCE Quyền Huy Ánh(1,a); Trịnh Tiến Uy(2,b) Đại học Sư Phạm Kỹ Thuật Tp Hồ Chí Minh Học viên cao học Trường ĐHSPKT TP.HCM (a) anhspkt@yahoo.com, (b)uythinh@gmail.com TĨM TẮT Bài báo trình bày phương pháp dự báo phụ tải điện hàng giờ, ngày, tháng hàng năm cho tỉnh Kiên Giang sở áp dụng mạng nơ rơn; giao diện chương trình dự báo phụ tải xây dựng môi trường Matlab, có tính thân thiện, dễ sử dụng Chương trình dự báo phụ tải điện đề xuất áp dụng cho tỉnh Kiên Giang có sai số nằm phạm vi cho phép (

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Tài liệu tham khảo Loại Chi tiết
[1]. Yan Yan and Aimin Yang, “Fuzzy Load Forecasting of Electric Power System”, Journal of Computers, Vol.7, Issue 8, pp: 1903-1910, August 2012 Sách, tạp chí
Tiêu đề: Fuzzy Load Forecasting of Electric Power System
[2]. Patel Parth Manoj, Ashish Pravinchandra Shah, “Fuzzy logic methodology for short-term load forecasting”, International Journal of Research in Engineering and Technology, Vol. 3, Issue 4, pp: 2321-7308, April 2014 Sách, tạp chí
Tiêu đề: Fuzzy logic methodology for short-term load forecasting
[3]. Hasan H.Cevik and Mehmet Cunkas, “A Fuzzy Logic Based Short-term Load Forecast for the Holidays”, International Journal of Machine Learning and Computing,Vol. 6, Issue 1, pp: 57, February.2016 Sách, tạp chí
Tiêu đề: A Fuzzy Logic Based Short-term Load Forecast for the Holidays
[4]. Mahmuda Akter Monne and Kazi Saifui Alam, “Application of Fuzzy logic to Electric Load Forecasting”, International Journal of Science and Advanced Technology, Vol. 3, Issue 12, pp: 2221-8386, December.2013 Sách, tạp chí
Tiêu đề: Application of Fuzzy logic to Electric Load Forecasting
[5]. Badri A, Ameli Z, Birjandi A.M, “Application of Artificial Neural Networks and Fuzzy logic Methods for Short Term Load Forecasting”, Energy Procedia 2012, 14, pp.1883–1888, 2012 Sách, tạp chí
Tiêu đề: Application of Artificial Neural Networks and Fuzzy logic Methods for Short Term Load Forecasting
[6]. Webberley.A, D.W.Gao, “Study of articial neural network based short term load forecasting”, Power and Energy Society General Meeting (PES), IEEE, Vancouver, BC, pp. 1-4, 2013 Sách, tạp chí
Tiêu đề: Study of articial neural network based short term load forecasting
[7]. Yokoyama.J, H.D.Chiang, “Short Term Load Forecasting improved by ensemble and its variations”, Power and Energy Society General Meeting (PES), 2012 IEEE, pp. 1-6, November.2012 Sách, tạp chí
Tiêu đề: Short Term Load Forecasting improved by ensemble and its variations
[8]. M.Buhari, S.S.Adamu, “Short-Term Load Forecasting Using Artificial Neural Network,” IMECS, pp. 83-88, Mar.2012 Sách, tạp chí
Tiêu đề: Short-Term Load Forecasting Using Artificial Neural Network,” "IMECS
[9]. M.De Felice, Y.Xin, "Short-term load forecasting with neural network ensembles: A comparative study", IEEE Computational Intell. Mag, vol 6, pp. 47-56, 2013 Sách, tạp chí
Tiêu đề: Short-term load forecasting with neural network ensembles: A comparative study
[10]. S.Li, P.Wang, L.Goel, "Short-term load forecasting by wavelet transform and evolutionary extreme learning machine", Electric Power System Research, vol. 122, pp. 96-103, 2015 Sách, tạp chí
Tiêu đề: Short-term load forecasting by wavelet transform and evolutionary extreme learning machine
[11]. N.Amjady, F.Keynia, "Short-term load forecasting of power systems by combination of wavelet transform and neuro-evolutionary algorithm", Energy, vol. 34, pp. 46-57, 2012 Sách, tạp chí
Tiêu đề: Short-term load forecasting of power systems by combination of wavelet transform and neuro-evolutionary algorithm
[12]. Liu Dong, Li Li, “Embed expert system short-term load forecasting of wavelet neural network”, Shanxi Electric Power, 37(10):44-48, 2012 Sách, tạp chí
Tiêu đề: Embed expert system short-term load forecasting of wavelet neural network
[13]. Che Gua, Peter.B.Luh, Laurent.D.Michel, Yuting Wang, Peter.B.Friendland, “Very Short-Term Load Forecasting; Wavelet Neural Network With Data Pre-Filtering”, IEEE Trans, Power System, Vol 28, No.1, pp. 30-41, Feb 2013 Sách, tạp chí
Tiêu đề: Very Short-Term Load Forecasting; Wavelet Neural Network With Data Pre-Filtering
[14]. D. Chaturvedi , S. Anad, and A. Chandiok, "Short Term load Forecasting Using Nero - Fuzzy - Wavelet Approach", International Journal of Computing Academic Research, Vol.2, No.1, p.p.36-48, February 2013 Sách, tạp chí
Tiêu đề: Short Term load Forecasting Using Nero - Fuzzy - Wavelet Approach
[15]. Medha Joshi, Rajiv Singh, “Short-term load forecasting approaches: A review”, International Journal of Recent Engineering Research and Development (IJRERD), Volume No. 01 – Issue No. 03, ISSN: 2455- 8761, pp. 09-17, 2013 Sách, tạp chí
Tiêu đề: Short-term load forecasting approaches: A review
[16]. Medha Joshi, Rajiv Singh , “An Intelligent ANN Approach for Short Term Electric Load Forecasting”, International Journal of Scientific Engineering and Research (IJSER), ISSN: 2347-3878, 2015 Sách, tạp chí
Tiêu đề: An Intelligent ANN Approach for Short Term Electric Load Forecasting
[17]. Nazih Abu-Shikhah, Fawwaz Elkarmi, Osama M. Aloquili “Medium-Term Electric Load Forecasting Using Multivariable Linear and Non-Linear Regression”, Smart Grid and Renewable Energy, vol 2, pp: 126-135, 2011 Sách, tạp chí
Tiêu đề: Medium-Term Electric Load Forecasting Using Multivariable Linear and Non-Linear Regression”, "Smart Grid and Renewable Energy
[18]. Manoj Kumar, “Short-term load forecasting using artificical neural network techniques”, Department of Electrical Engineering National Institute of Technology Rourkela, 2009 Sách, tạp chí
Tiêu đề: Short-term load forecasting using artificical neural network techniques
[19]. Firas M. Tuaimah, Huda M. Abdul Abass, “Short-Term Electrical Load Forecasting for Iraqi Power System based on Multiple Linear Regression Method”, International Journal of Computer Applications, Vol 100–No.1, August 2014 Sách, tạp chí
Tiêu đề: Short-Term Electrical Load Forecasting for Iraqi Power System based on Multiple Linear Regression Method
[20]. Wagdy Mansour, Mohamed Moenes, Hassan Mahmoud, Ahmed Ghareeb, “Long-term load forecasting for the Egyptian network using ANN and regression models”, 21st International Conference on Electricity Distribution, Frankfurt, pp: 1-5, June 2011 Sách, tạp chí
Tiêu đề: Long-term load forecasting for the Egyptian network using ANN and regression models

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