The project included an exploratory data analysis and training and testing of regression models to predict the output of a combined cycle power plant based on a small number of environmental variables.
Dataset source: https://www.kaggle.com/datasets/aagmandeep/combined-cycle-power-plant-dataset-and-prediction
- The dataset comprises 9568 hourly average ambient environmental readings from sensors at a Combined Cycle Power Plant.
- Temperature (T): Measurement Unit: Degrees Celsius (°C), Description: Represents the ambient temperature.
- Ambient Pressure (AP): Measurement Unit: Milibar (mbar), Description: Indicates the atmospheric pressure.
- Relative Humidity (RH): Measurement Unit: Percentage (%), Description: Represents the relative humidity.
- Exhaust Vacuum (V): Measurement Unit: Centimeters of Mercury (cm Hg), Description: Indicates the level of vacuum in the exhaust of the turbine.
- Net Hourly Electrical Energy Output (PE): Measurement Unit: Megawatts (MW), Description: Hourly electrical energy produced by the Combined Cycle Power Plant
- The dataset was aquired form Kaggle user https://www.kaggle.com/aagmandeep
I'm Bill—a power industry professional with 20+ years of experience in power generation. My background as a mechanical engineer led to me a role as project manager where I led the development and execution of power generation projects. These days, I'm diving into data science, visualization, and machine learning with the intention of using it as a tool to uncover insights and improve decision making in power project development, design, procurement, construction and operations.