19-24.txt

bp神经网络对短期电力负荷的预测

P=[0.752873563	0.75862069	0.83908046	0.833333333	0.74137931	0.66091954	0.465517241	0.367816092	0.298850575	0.327586207	0.275862069	0.379310345	0.1004;
0.666666667	0.735632184	0.695402299	0.816091954	0.689655172	0.528735632	0.373563218	0.229885057	0.229885057	0.16091954	0.206896552	0.350574713	0.1009;
0.793103448	0.862068966	0.83908046	0.936781609	0.787356322	0.609195402	0.402298851	0.281609195	0.287356322	0.24137931	0.24137931	0.379310345	0.0983;
0.816091954	0.833333333	0.885057471	1	0.833333333	0.66091954	0.488505747	0.454022989	0.431034483	0.396551724	0.379310345	0.534482759	0.0968]';
T=[0.666666667	0.735632184	0.695402299	0.816091954	0.689655172	0.528735632	0.373563218	0.229885057	0.229885057	0.16091954	0.206896552	0.350574713;
0.793103448	0.862068966	0.83908046	0.936781609	0.787356322	0.609195402	0.402298851	0.281609195	0.287356322	0.24137931	0.24137931	0.379310345;
0.816091954	0.833333333	0.885057471	1	0.833333333	0.66091954	0.488505747	0.454022989	0.431034483	0.396551724	0.379310345	0.534482759;
0.83908046	0.844827586	0.856321839	0.862068966	0.718390805	0.568965517	0.540229885	0.517241379	0.517241379	0.477011494	0.402298851	0.58045977]';
threshold=[0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1];
net=newff(threshold,[27,12],{'tansig','logsig'},'trainlm');
net.trainParam.epochs=1000;
net.trainParam.goal=0.01;
LP.lr=0.1;
net=train(net,P,T);

P_test=[0.83908046
	0.844827586
	0.856321839
	0.862068966
	0.718390805
	0.568965517
	0.540229885
	0.517241379
	0.517241379
	0.477011494
	0.402298851
	0.58045977
	0.0996];
Out=sim(net,P_test);