{"id":3477,"date":"2026-02-13T11:12:59","date_gmt":"2026-02-13T10:12:59","guid":{"rendered":"https:\/\/neuraldesigner.com\/learning\/combined-cycle-power-plant\/"},"modified":"2026-04-06T13:20:40","modified_gmt":"2026-04-06T11:20:40","slug":"combined-cycle-power-plant","status":"publish","type":"learning","link":"https:\/\/www.neuraldesigner.com\/learning\/examples\/combined-cycle-power-plant\/","title":{"rendered":"Improve the performance of a power plant using machine learning"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"3477\" class=\"elementor elementor-3477\" data-elementor-post-type=\"learning\">\n\t\t\t\t<div class=\"elementor-element elementor-element-3fe6f87 e-flex e-con-boxed e-con e-parent\" data-id=\"3fe6f87\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-17e1c67 elementor-widget elementor-widget-text-editor\" data-id=\"17e1c67\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<section><p>This example uses machine learning to model the energy generated as a function of exhaust vacuum and ambient variables and use that model to improve the power plant performance.<\/p><\/section><section>A combined-cycle power plant comprises 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 and affects the steam turbine.\u00a0<section><p>We use the explainable machine learning platform <a href=\"https:\/\/www.neuraldesigner.com\/\">Neural Designer<\/a> to build this model. You can use the <a href=\"https:\/\/www.neuraldesigner.com\/downloads\">free trial<\/a> to follow the process step by step.<\/p><\/section><\/section>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1c483aa elementor-widget elementor-widget-text-editor\" data-id=\"1c483aa\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: center;\">\u00a0You can download Neural Designer to follow the process step by step.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b0abad6 elementor-align-center elementor-widget elementor-widget-button\" data-id=\"b0abad6\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/www.neuraldesigner.com\/downloads\/\" id=\"download\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-icon\">\n\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-download\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M216 0h80c13.3 0 24 10.7 24 24v168h87.7c17.8 0 26.7 21.5 14.1 34.1L269.7 378.3c-7.5 7.5-19.8 7.5-27.3 0L90.1 226.1c-12.6-12.6-3.7-34.1 14.1-34.1H192V24c0-13.3 10.7-24 24-24zm296 376v112c0 13.3-10.7 24-24 24H24c-13.3 0-24-10.7-24-24V376c0-13.3 10.7-24 24-24h146.7l49 49c20.1 20.1 52.5 20.1 72.6 0l49-49H488c13.3 0 24 10.7 24 24zm-124 88c0-11-9-20-20-20s-20 9-20 20 9 20 20 20 20-9 20-20zm64 0c0-11-9-20-20-20s-20 9-20 20 9 20 20 20 20-9 20-20z\"><\/path><\/svg>\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Download<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-32ab94f elementor-widget elementor-widget-text-editor\" data-id=\"32ab94f\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<section id=\"ApplicationType\"><h2>\u00a0Contents<\/h2><\/section><ol><li><a href=\"#ApplicationType\">Application type<\/a>.<\/li><li><a href=\"#DataSet\">Data set<\/a>.<\/li><li><a href=\"#NeuralNetwork\">Neural network<\/a>.<\/li><li><a href=\"#TrainingStrategy\">Training strategy<\/a>.<\/li><li><a href=\"#ModelSelection\">Model selection<\/a>.<\/li><li><a href=\"#TestingAnalysis\">Testing analysis<\/a>.<\/li><li><a href=\"#ModelDeployment\">Model deployment<\/a>.<\/li><li><a href=\"#TutorialVideo\">Tutorial Video<\/a>.<\/li><\/ol>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e92b578 elementor-widget elementor-widget-text-editor\" data-id=\"e92b578\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<section><h2>1. Application type<\/h2><\/section><section id=\"ApplicationType\"><p>This is an <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-networks-applications#Approximation\">approximation<\/a> project since the variable we want to predict is continuous (energy production).<\/p><p>The primary goal is to model energy production as a function of environmental and control variables.<\/p><\/section>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1008d7a elementor-widget elementor-widget-text-editor\" data-id=\"1008d7a\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h2>2. Data set<\/h2><p>The <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set\">data set<\/a> contains three concepts:<\/p><ul><li>Data source.<\/li><li>Variables.<\/li><li>Instances.<\/li><\/ul><p>The data file <a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/10\/combinedcyclepowerplant.csv\">combined_cycle_power_plant.csv<\/a>\u00a0contains 9568 samples with five 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.<\/p><p>The <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set#Variables\">variables<\/a>, or features, are the following:<\/p><ul><li><b>temperature<\/b>, in degrees Celsius.<\/li><li><b>exhaust_vacuum<\/b>, in cm Hg.<\/li><li><b>ambient_pressure<\/b>, in millibar.<\/li><li><b>relative_humidity<\/b>, in percentage.<\/li><li><b>energy<\/b>, in MW, net hourly electrical energy output.<\/li><\/ul><p>The <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set#Instances\">instances<\/a> are divided into training, selection, and testing subsets. They represent 60%, 20%, and 20% of the original instances, respectively, and are randomly split.<\/p><p>Calculating the data <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set#Distributions\">distributions<\/a> helps us check for the available information&#8217;s correctness and detect anomalies. The following chart shows the histogram for the variable energy_output.<\/p><p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/combined-cycle-power-plant-data-distribution.webp\" \/><\/p><p>As we can see, there are more scenarios where the energy produced is small than where it is significant.<\/p><p>It is also interesting to look for dependencies between a single input and single target variables. To do that, we can plot an <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set#InputsTargetsCorrelations\">inputs-targets correlations<\/a> chart.<\/p><p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/combined-cycle-power-plant-correlations.webp\" \/><\/p><p>The temperature yields the highest correlation (generally, the higher the temperature, the less energy production).<\/p><p>Next, we plot a <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set#ScatterCharts\">scatter chart<\/a> for the energy output and the exhaust vacuum.<\/p><p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/combined-cycle-power-plant-scatter-chart.webp\" \/><\/p><p>As we can see, the energy output is highly correlated with the exhaust vacuum. In general, the more exhaust vacuum, the less energy production.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-80cb9d1 elementor-widget elementor-widget-text-editor\" data-id=\"80cb9d1\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h2>3. Neural network<\/h2><p>The second step is building a <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network\">neural network<\/a> representing\u00a0the approximation function. Approximation models usually contain the following layers:<\/p><ul><li>Scaling layer.<\/li><li>Perceptron layers.<\/li><li>Unscaling layer.<\/li><\/ul><p>The neural network has four inputs (temperature, exhaust vacuum, ambient pressure, and relative humidity) and one output (energy output).<\/p><p>The <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network#ScalingLayer\">scaling layer<\/a> contains the statistics of the inputs. We use the <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network#MeanStandardDeviationScalingMethod\">mean and standard deviation scaling method<\/a> as all inputs have normal distributions.<\/p><p>We use 2 perceptron layers here:<\/p><ul><li>The first perceptron layer has 4 inputs, 3 neurons, and a <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network#HyperbolicTangentActivationFunction\">hyperbolic tangent activation function<\/a>.<\/li><li>The second perceptron layer has 3 inputs, 1 neuron, and a <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network#LinearActivationFunction\">linear activation function<\/a>.<\/li><\/ul><p>The <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network#UnscalingLayer\">unscaling layer<\/a> contains the statistics of the outputs. As the output has a normal distribution, we use the <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network#MeanStandardDeviationUnscalingMethod\">mean and standard deviation unscaling method<\/a>.<\/p><p>The next graph represents the neural network for this example.<\/p><p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/combined-cycle-power-plant-initial-neural-network.webp\" \/><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1107466 elementor-widget elementor-widget-text-editor\" data-id=\"1107466\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h2>4. Training strategy<\/h2><p>The fourth step is to select an appropriate <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/training-strategy\">training strategy<\/a>. It is composed of two things:<\/p><ul><li>A loss index.<\/li><li>An optimization algorithm.<\/li><\/ul><h3>Loss index<\/h3><p>The <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/training-strategy#LossIndex\">loss index<\/a> defines what the neural network will learn. It is composed of an <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/training-strategy#ErrorTerm\">error term<\/a> and a <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/training-strategy#RegularizationTerm\">regularization term<\/a>.<\/p><p>The error term chosen is the <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/training-strategy#NormalizedSquaredError\">normalized squared error<\/a>. A normalization coefficient divides the squared error between the neural network outputs and the data set&#8217;s targets. If the normalized squared error has a value of 1, then the neural network predicts the data &#8216;in the mean&#8217;, while a zero value means a perfect data prediction. This error term does not have any parameters to set.<\/p><p>The regularization term is the <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/training-strategy#L2Regularization\">L2 regularization<\/a>. It is applied to control the neural network&#8217;s complexity by reducing the parameters&#8217; value. We use a weak weight for this regularization term.<\/p><h3>Optimization algorithm<\/h3><p>The <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/training-strategy#OptimizationAlgorithm\">optimization algorithm<\/a> is in charge of searching for the neural network parameters that minimize the loss index. Here, we chose the <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/training-strategy#QuasiNewtonMethod\">quasi-Newton method<\/a> as the optimization algorithm.<\/p><h3>Training process<\/h3><p>The following chart shows how the training (blue) and selection (orange) errors decrease with the epochs during the training process.<\/p><p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/combined-cycle-power-plant-training-history.webp\" \/><\/p><p>The final values are <b>training error = 0.057 NSE<\/b> and <b>selection error = 0.067 NSE<\/b>, respectively.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-77875a2 elementor-widget elementor-widget-text-editor\" data-id=\"77875a2\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<section id=\"TrainingStrategy\"><h2>5. Model selection<\/h2><\/section><section id=\"ModelSelection\"><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-selection\">Model selection<\/a> algorithms improve the generalization performance of the neural network. The selection error that we have achieved is minimal (0.067 NSE), so this algorithm is unnecessary.<\/section>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-5518f6d1 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"5518f6d1\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-47aa4178\" data-id=\"47aa4178\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-61a584f0 elementor-widget elementor-widget-text-editor\" data-id=\"61a584f0\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<section><\/section><section><section><\/section><section id=\"ApplicationType\"><\/section><\/section><section id=\"ApplicationType\"><\/section><section id=\"DataSet\"><\/section><section id=\"NeuralNetwork\"><\/section><section id=\"TrainingStrategy\"><\/section><h2 id=\"ModelSelection\">6. Testing analysis<\/h2><section id=\"TestingAnalysis\"><p>The purpose of the <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis\">testing analysis<\/a> is to validate the generalization capabilities of the neural network. We use the <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set#TestingInstances\">testing instances<\/a> in the data set, which have never been used before.<\/p><p>A standard testing method in approximation applications is to perform a <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis#LinearRegressionAnalysis\">linear regression analysis<\/a> between the predicted and the real energy output values.<\/p><p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/combined-cycle-power-plant-linear-regression-analysis.webp\" \/><\/p><p>For a perfect fit, the correlation coefficient R2 would be 1. As we have <b>R2 = 0.968<\/b>, the neural network is predicting very well the testing data.<\/p><\/section><section id=\"ModelDeployment\"><h2>7. Model deployment<\/h2><p>In the <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-deployment\">model deployment<\/a> phase, the neural network predicts outputs for inputs it has never seen.<\/p><h3>Neural network outputs<\/h3><p>We can calculate the <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-deployment#NeuralNetworkOutputs\">neural network outputs<\/a> for a given set of inputs:<\/p><ul><li><b>temperature<\/b>: 19 degrees Celsius.<\/li><li><b>exhaust_vacuum<\/b>: 54 cm Hg.<\/li><li><b>ambient_pressure<\/b>: 1013 millibar.<\/li><li><b>relative_humidity<\/b>: 73 %.<\/li><\/ul><p>The predicted output for these input values is the following:<\/p><ul><li><b>energy_output<\/b>: 452 MW.<\/li><\/ul><h3>Response optimization<\/h3><p>The objective of the <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-deployment#ResponseOptimization\">Response Optimization algorithm<\/a> is to exploit the mathematical model to look for optimal operating conditions. Indeed, the predictive model allows us to simulate different operating scenarios and adjust the control variables to improve efficiency.<\/p><p>An example is minimizing exhaust vacuum while maintaining energy over the desired value.<\/p><p>The next table resumes the conditions for this problem.<\/p><table><tbody><tr><th>Variable name<\/th><th>Condition<\/th><th>\u00a0<\/th><\/tr><tr><th>Temperature<\/th><td style=\"text-align: right;\">None<\/td><td style=\"text-align: right;\">\u00a0<\/td><\/tr><tr><th>Exhaust vacuum<\/th><td style=\"text-align: right;\">Minimize<\/td><td style=\"text-align: right;\">\u00a0<\/td><\/tr><tr><th>Ambient pressure<\/th><td style=\"text-align: right;\">None<\/td><td style=\"text-align: right;\">\u00a0<\/td><\/tr><tr><th>Relative humidity<\/th><td style=\"text-align: right;\">None<\/td><td style=\"text-align: right;\">\u00a0<\/td><\/tr><tr><th>Energy<\/th><td style=\"text-align: right;\">Greater than or equal to<\/td><td style=\"text-align: right;\">450<\/td><\/tr><\/tbody><\/table><p>The next list shows the optimum values for previous conditions.<\/p><ul><li><b>temperature<\/b>: 19.3846 Celsius degrees.<\/li><li><b>exhaust_vacuum<\/b>: 25.432 cm Hg.<\/li><li><b>ambient_pressure<\/b>: 1021.4 millibar.<\/li><li><b>relative_humidity<\/b>: 60.8 %.<\/li><li><b>energy<\/b>: 462.086 MW.<\/li><\/ul><h3>Directional outputs<\/h3><p><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-deployment#DirectionalOutputs\">Directional outputs<\/a> plot the neural network outputs through some reference points.<\/p><p>The next list shows the reference points for the plots.<\/p><ul><li><b>temperature<\/b>: 19 Celsius degrees.<\/li><li><b>exhaust_vacuum<\/b>: 54 cm Hg.<\/li><li><b>ambient_pressure<\/b>: 1013 millibar.<\/li><li><b>relative_humidity<\/b>: 73 %.<\/li><\/ul><p>Next, we define a reference point and see how the energy production varies with the exhaust vacuum around that point.<\/p><p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/combined-cycle-power-plant-directional-output.webp\" \/><\/p><p>As we can see, reducing the exhaust vacuum increases energy output.<\/p><h3>Mathematical expression<\/h3><p>The <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-deployment#MathematicalExpression\">mathematical expression<\/a> represented by the predictive model is listed next:<\/p><pre>scaled_temperature = (temperature-19.6512)\/7.45247;\nscaled_exhaust_vacuum = 2*(exhaust_vacuum-25.36)\/(81.56-25.36)-1;\nscaled_ambient_pressure = (ambient_pressure-1013.26)\/5.93878;\nscaled_relative_humidity = (relative_humidity-73.309)\/14.6003;\ny_1_1 = tanh(-0.158471 + (scaled_temperature*0.200864) + (scaled_exhaust_vacuum*0.73313) \n                       + (scaled_ambient_pressure*-0.19189) + (scaled_relative_humidity*0.0133642));\ny_1_2 = tanh(-0.290828 + (scaled_temperature*-0.020375) + (scaled_exhaust_vacuum*-0.263848) \n                       + (scaled_ambient_pressure*-0.227397)+ (scaled_relative_humidity*0.337468));\ny_1_3 = tanh(0.574054 + (scaled_temperature*0.572764) + (scaled_exhaust_vacuum*-0.0264721) \n                      + (scaled_ambient_pressure*0.109944)+ (scaled_relative_humidity*0.00934301));\nscaled_energy_output =  (0.162012+ (y_1_1*-0.382654) + (y_1_2*-0.126065) + (y_1_3*-0.748958));\nenergy_output = (0.5*(scaled_energy_output+1.0)*(495.76-420.26)+420.26);<\/pre><\/section><section id=\"TutorialVideo\"><h2>8. Tutorial video<\/h2><p>You can watch the step-by-step tutorial video below to help you complete this Machine Learning example<br \/>for free using the easy-to-use machine learning software <a href=\"https:\/\/www.neuraldesigner.com\/free-trial\">Neural Designer<\/a>.<\/p><p><iframe src=\"https:\/\/www.youtube.com\/embed\/gEZ4etqjDK8\" width=\"560\" height=\"315\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"><\/iframe><\/p><\/section><section><h2>References<\/h2><ul><li>Pinar Tufekci, Prediction of full load electrical power output of a baseload operated combined cycle power plant using machine learning methods, International Journal of Electrical Power &amp; Energy Systems, Volume 60, September 2014, Pages 126-140, ISSN 0142-0615.<\/li><li>Heysem Kaya, Pinar Tufekci, Fikret S. Gurgen: <a href=\"https:\/\/www.researchgate.net\/publication\/269108474_Local_and_Global_Learning_Methods_for_Predicting_Power_of_a_Combined_Gas_Steam_Turbine\">Local and Global Learning Methods for Predicting Power of a Combined Gas &amp; Steam Turbine<\/a>, Proceedings of the International Conference on Emerging Trends in Computer and Electronics Engineering ICETCEE 2012, pp. 13-18 (Mar. 2012, Dubai).<\/li><\/ul><\/section><section><h2>Related post<\/h2><\/section>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"author":13,"featured_media":2325,"template":"","categories":[29],"tags":[44,43],"class_list":["post-3477","learning","type-learning","status-publish","has-post-thumbnail","hentry","category-examples","tag-energy","tag-industry"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Improve the performance of a power plant using machine learning<\/title>\n<meta name=\"description\" content=\"Build a machine learning model to improve the performance in electricity production in a combined cycle power plant.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.neuraldesigner.com\/learning\/examples\/combined-cycle-power-plant\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Combined cycle power plant optimization machine learning example\" \/>\n<meta property=\"og:description\" content=\"A combined cycle power plant is composed of gas turbines, steam turbines, and heat recovery steam generators. 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