MATLAB: Water demand forecasting using neural network

Deep Learning Toolboxneural network

my project is to develope and ANN network which will forecast water demand depending on previous consumption and temperatuer.i have per day consumption data from year 2005-2011 i want to use this data and predict cnsumption for 2011 so how can i design a neural network for my application. plz help.regarding the same as i am new to neural network.

Best Answer

Start by reading the documentation and try to reproduce the timeseries forecasting examples. Additional information can be obtained by searching the Newsgroup using the search words

 timeseries and/or forecasting.

Hope this helps,

Greg

Related Solutions

MATLAB: How to train a feedfordward neural network with error weights

 Use the command window
 help mse
 doc mse

Hope this helps.

Thank you for formally accepting my answer

Greg

MATLAB: Not getting good results while simulation in NNTOOL

continued ...

 You should scale and center the data.  zero-mean/unit-variance inputs  with tansig hidden node activation units are recommended..
 help prestd
 doc prestd
 similarly for trastd and poststd.
 >%creating network
 Initialize the uniform random number generator RAND so that 
 you can duplicate the results if necessary.
 > net=newff(minmax(x),[3,12,1],{'logsig','logsig','purelin'},'trainlm');
 Do not include the number of input nodes. That info is obtained 
 from minmax(x)
 >net.performFcn = 'mse';
 Delete. It is a default value.
 >net.trainParam.goal = 1e-4;
 Use a better informed choice that takes the data scaling into account..
 >net.trainParam.min_grad = 1e-20;
 >net.trainParam.epochs = 5000;
 Where did you get these values?
 Use as many defaults of newff  as possible.
 help newff
 doc newff
 >net=init(net);
 Delete. NEWFF is self-initializing.
 >net=train(net,x,y);
 >z=sim(net,x);
 Use the multiple output option.
 >test=sim(net,a);
 %%%%%%%%%%%%%%%%%%%%%%
 MSEtrn00    = mse(y-mean(y))    % Naive Constant Model
 MSEgoal      = MSEtrn00/100           % R^2 >= 0.99
 state0      = 0                             % Any integer < 2^32-1
 rand('state',state0)                     % help RAND
 net = newff(minmax(x),[12 3]);
 net.trainParam.goal = MSEgoal;
 [ net tr Y E] = train(net,x,y);       % help TRAIN, help TRAINLM
 MSEtrn = tr.perf(end)                 % = mse(y-Y)
 R2trn    = 1-MSEtrn/MSEtrn00
 MSEtst00 = mse(b-mean(b))
 tst             = sim(net,a);                   % test is not a good name
 MSEtst      = mse(b-tst);
 R2tst         = 1-MSEtst/MSEtst00
 % %evaluating errors
 -----SNIP
 >i tried with 10-30 neurons but the result is not good 
 >can you tell where its going wrong
 Implement a double loop over choice of H and creation of a 
 number of nets for each value of H.
 Hope this helps.
 Greg

Best Answer

Related Solutions

MATLAB: How to train a feedfordward neural network with error weights

MATLAB: Not getting good results while simulation in NNTOOL

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