Improve the performance of combined cycle power plants using Neural Designer

A combined-cycle power plant is composed of gas turbines, steam turbines, and heat recovery steam generators.

In this type of plant, the electricity is generated by gas and steam turbines combined in one cycle. Then, it is transferred from one turbine to another.

The ambient variables affect the gas turbine performance while the vacuum is collected from and affects the steam turbine.

This example aims to model the energy generated as a function of exhaust vacuum and ambient variables and use that model to improve the plant's performance.

Contents:

  1. Application type.
  2. Data set.
  3. Neural network.
  4. Training strategy.
  5. Model selection.
  6. Testing analysis.
  7. Model deployment.
  8. Tutorial Video.

This example is solved with Neural Designer. To follow it step by step, you can use the free trial.

1. Application type

This is an approximation project since the variable to be predicted is continuous (energy production).

The primary goal here is to model energy production as a function of environmental and control variables.

2. Data set

The data set contains three concepts:

The data file combined_cycle_power_plant.csv contains 9568 samples with 5 variables collected from a combined cycle power plant over six years when the power plant was set to work with a full load. The measurements were taken every second.

The variables, or features, are the following:

The instances are divided into training, selection, and testing subsets. They represent 60%, 20%, and 20% of the original instances, respectively, and are split at random.

Calculating the data distributions helps us to check for the correctness of the available information and detect anomalies. The following chart shows the histogram for the variable energy_output.

As we can see, there are more scenarios where the energy produced is small than where it is big.

It is also interesting to look for dependencies between a single input and single target variables. To do that, we can plot an inputs-targets correlations chart.

The highest correlation is the yield for the temperature (in general, the more temperature, the less energy production).

Next, we plot a scatter chart for the energy output and the exhaust vacuum.

As we can see, the energy output is highly correlated with the exhaust vacuum. In general, the more exhaust vacuum, the less energy production.

3. Neural network

The second step is to build a neural network that represents the approximation function. For approximation problems, it is usually composed by:

The neural network has 4 inputs (temperature, exhaust vacuum, ambient pressure, and relative humidity) and 1 output (energy output).

The scaling layer contains the statistics of the inputs. As all inputs have normal distributions, we use the mean and standard deviation scaling method.

We use 2 perceptron layers here:

The unscaling layer contains the statistics of the outputs. As the output has a normal distribution, we use the mean and standard deviation unscaling method.

The next graph represents the neural network for this example.

4. Training strategy

The fourth step is to select an appropriate training strategy. It is composed of two things:

The loss index defines what the neural network will learn. It is composed of an error term and a regularization term.

The error term chosen is the normalized squared error. A normalization coefficient divides the squared error between the outputs from the neural network and the targets in the data set. If the normalized squared error has a value of 1, then the neural network predicts the data 'in the mean', while a value of zero means a perfect prediction of the data. This error term does not have any parameters to set.

The regularization term is the L2 regularization. It is applied to control the complexity of the neural network by reducing the value of the parameters. We use a weak weight for this regularization term.

The optimization algorithm is in charge of searching for the neural network parameters that minimize the loss index. Here we chose the quasi-Newton method as optimization algorithm.

The following chart shows how the training (blue) and selection (orange) errors decrease with the epochs during the training process. The final values are training error = 0.057 NSE and selection error = 0.067 NSE, respectively.

5. Model selection

Model selection algorithms are used to improve the generalization performance of the neural network.

As the selection error that we have achieved is minimal (0.067 NSE), this algorithm is unnecessary.

6. Testing analysis

The purpose of the testing analysis is to validate the generalization capabilities of the neural network. We use the testing instances in the data set, which have never been used before.

A standard testing method in approximation applications is to perform a linear regression analysis between the predicted and the real energy output values.

For a perfect fit, the correlation coefficient R2 would be 1. As we have R2 = 0.968, the neural network is predicting very well the testing data.

7. Model deployment

In the model deployment phase, the neural network is used to predict outputs for inputs that it has never seen.

We can calculate the neural network outputs for a given set of inputs:

Directional outputs plot the neural network outputs through some reference points.

The next list shows the reference point for the plots.

Next, we define a reference point and see how the energy production varies with the exhaust vacuum around that point.

As we can see, reducing the exhaust vacuum increases energy output.

The mathematical expression represented by the predictive model is listed next:

scaled_temperature = (temperature-19.6512)/7.45247;
scaled_exhaust_vacuum = 2*(exhaust_vacuum-25.36)/(81.56-25.36)-1;
scaled_ambient_pressure = (ambient_pressure-1013.26)/5.93878;
scaled_relative_humidity = (relative_humidity-73.309)/14.6003;

y_1_1 = tanh(-0.158471 + (scaled_temperature*0.200864) + (scaled_exhaust_vacuum*0.73313) 
                       + (scaled_ambient_pressure*-0.19189) + (scaled_relative_humidity*0.0133642));

y_1_2 = tanh(-0.290828 + (scaled_temperature*-0.020375) + (scaled_exhaust_vacuum*-0.263848) 
                       + (scaled_ambient_pressure*-0.227397)+ (scaled_relative_humidity*0.337468));

y_1_3 = tanh(0.574054 + (scaled_temperature*0.572764) + (scaled_exhaust_vacuum*-0.0264721) 
                      + (scaled_ambient_pressure*0.109944)+ (scaled_relative_humidity*0.00934301));

scaled_energy_output =  (0.162012+ (y_1_1*-0.382654) + (y_1_2*-0.126065) + (y_1_3*-0.748958));

energy_output = (0.5*(scaled_energy_output+1.0)*(495.76-420.26)+420.26);
        

8. Tutorial video

You can watch the step-by-step tutorial video below to help you complete this Machine Learning example for free using the easy-to-use machine learning software Neural Designer.

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