SMART-GTPP Dataset

This dataset contains operational and environmental measurements collected from a utility-scale gas turbine operating in a combined-cycle gas power plant in the Kurdistan Region of Iraq. The data cover the period from 2019 to 2023 and were prepared for gas turbine power-output prediction, performance analysis, machine-learning research, and data-driven digital-twin development. The dataset contains 7,893 observations, eight input features, and one target variable. All variables are numerical, and the file contains no missing values. The prediction task is a supervised regression problem. The input features are: AT: Ambient temperature, representing the temperature of the air entering the gas turbine compressor. Higher ambient temperature usually reduces air density and gas turbine output. AP: Atmospheric pressure, which affects inlet-air density, compressor mass flow, and turbine performance. DF: Electrical or grid frequency, reflecting generator and power-system operating conditions. CH: Humidity-related parameter describing the moisture content of the ambient air. Humidity can influence air density, combustion, and turbine efficiency. GP: Fuel-gas pressure supplied to the gas turbine combustion system. Stable gas pressure is important for combustion stability and power generation. CPR: Compressor pressure ratio, defined as the ratio between compressor discharge and inlet pressure. It is an important indicator of compressor and thermodynamic performance. CPD: Compressor discharge pressure, measured at the compressor outlet before the compressed air enters the combustion chambers. TTXM: Average turbine exhaust temperature, which is related to fuel input, combustion conditions, turbine loading, and overall efficiency. The target variable is EP, representing the active electrical power generated by the gas turbine in megawatts. The dataset can be used for electrical power-output forecasting, gas turbine performance modelling, feature-importance analysis, anomaly detection, digital-twin development, and comparison of regression algorithms such as Support Vector Regression, Random Forest, XGBoost, K-Nearest Neighbours, and Artificial Neural Networks. The dataset can be used for: Gas turbine electrical power-output prediction Energy forecasting in combined-cycle gas power plants Development of data-driven digital twins Gas turbine performance assessment Operational deviation and anomaly detection Feature-importance and sensitivity analysis Comparison of machine-learning regression algorithms Investigation of environmental effects on gas turbine output Predictive monitoring and decision-support system development Academic teaching and research in energy systems and artificial intelligence本数据集采集自伊拉克库尔德地区某联合循环燃气发电厂的公用事业级燃气轮机运行过程中的运行与环境监测数据。数据覆盖2019年至2023年时段，旨在用于燃气轮机出力预测、性能分析、机器学习研究以及数据驱动型数字孪生（digital-twin）开发。 本数据集共包含7893条观测样本、8项输入特征与1项目标变量。所有变量均为数值型，数据集文件中无缺失值，其对应的预测任务属于监督回归问题。 输入特征如下： AT：环境温度（Ambient temperature），指进入燃气轮机压气机的空气温度。环境温度升高通常会降低空气密度与燃气轮机出力。 AP：大气压力（Atmospheric pressure），会影响进气密度、压气机质量流量以及涡轮机性能。 DF：电网频率（Electrical or grid frequency），反映发电机与电力系统的运行工况。 CH：湿度相关参数（Humidity-related parameter），表征环境空气的含水率。湿度会影响空气密度、燃烧过程与涡轮机效率。 GP：供给燃气轮机燃烧系统的燃料气压力（Fuel-gas pressure）。稳定的燃气压力对燃烧稳定性与发电效率至关重要。 CPR：压气机压比（Compressor pressure ratio），定义为压气机出口与进口压力的比值，是衡量压气机与热力学性能的重要指标。 CPD：压气机排气压力（Compressor discharge pressure），指压缩空气进入燃烧室前，在压气机出口处测得的压力。 TTXM：涡轮机平均排气温度（Average turbine exhaust temperature），与燃料投入量、燃烧工况、涡轮机负荷以及整体运行效率密切相关。 目标变量为EP，代表燃气轮机产生的有功电功率，单位为兆瓦（megawatts）。 本数据集可用于电力出力预测、燃气轮机性能建模、特征重要性分析、异常检测、数字孪生开发，以及支持向量回归（Support Vector Regression）、随机森林（Random Forest）、XGBoost、K近邻（K-Nearest Neighbours）与人工神经网络（Artificial Neural Networks）等回归算法的对比研究。 本数据集可应用于以下场景： 1. 燃气轮机有功电力出力预测 2. 联合循环燃气发电厂的能源预测 3. 数据驱动型数字孪生开发 4. 燃气轮机性能评估 5. 运行偏差与异常检测 6. 特征重要性与敏感性分析 7. 机器学习回归算法对比研究 8. 环境因素对燃气轮机出力影响的探究 9. 预测性监测与决策支持系统开发 10. 能源系统与人工智能领域的学术教学与研究