Predicting Turbine Energy Yield (TEY) using ambient variables as features.

TEIES project on VM/CMS developed by CCCC @ NJIT

Prediction of turbine energy yield (TEY) using Neural Networks

share your book with friends and tey can answer some fun question for you

online financial calculators (mortgage calc, TEY calc, etc)

predicting turbine energy yield (TEY) using ambient variables as features Gas Turbine

Work for everyone and everywhere

fisrt tey

Tey's portfolio

Predicting Turbine Energy Yield (TEY) using ambient variables as features.

Predicting Turbine Energy Yield (TEY) using ambient variables as features.

predicting turbine energy yield (TEY) using ambient variables as features.

predicting turbine energy yield (TEY) using ambient variables as features.

Neural-Network-Gas-Turbine-data->predicting turbine energy yield (TEY) using neural network concept.

Predicting turbine energy yield (TEY) using ambient variables as features with the help of Neural Networks

This project uses machine learning to predict Turbine Energy Yield (TEY) from gas turbine data, optimizing settings to improve energy output, reduce fuel consumption, and cut costs. TEY predictions help detect deviations from normal operations, signaling potential turbine issues like degradation.

1. Predict the Burned Area of Forest Fires with Neural Networks 2. predicting turbine energy yield (TEY) using ambient variables as features.

Predict the Burned Area of Forest Fire with Neural Networks and Predicting Turbine Energy Yield (TEY) using Ambient Variables as Features.

The dataset contains 36733 instances of 11 sensor measures aggregated over one hour (by means of average or sum) from a gas turbine. The Dataset includes gas turbine parameters (such as Turbine Inlet Temperature and Compressor Discharge pressure) in addition to the ambient variables. Problem statement: predicting turbine energy yield (TEY) using ambient variables as features. Attribute Information: The explanations of sensor measurements and their brief statistics are given below. Variable (Abbr.) Unit Min Max Mean Ambient temperature (AT) C –6.23 37.10 17.71 Ambient pressure (AP) mbar 985.85 1036.56 1013.07 Ambient humidity (AH) (%) 24.08 100.20 77.87 Air filter difference pressure (AFDP) mbar 2.09 7.61 3.93 Gas turbine exhaust pressure (GTEP) mbar 17.70 40.72 25.56 Turbine inlet temperature (TIT) C 1000.85 1100.89 1081.43 Turbine after temperature (TAT) C 511.04 550.61 546.16 Compressor discharge pressure (CDP) mbar 9.85 15.16 12.06 Turbine energy yield (TEY) MWH 100.02 179.50 133.51 Carbon monoxide (CO) mg/m3 0.00 44.10 2.37 Nitrogen oxides (NOx) mg/m3 25.90 119.91 65.29

Artificial-Neural-Network Assignment Predicted Burned area of forest fires and Turbine yield energy using ANN Probem 1 PREDICT THE BURNED AREA OF FOREST FIRES WITH NEURAL NETWORKS Problem 2 The dataset contains 36733 instances of 11 sensor measures aggregated over one hour (by means of average or sum) from a gas turbine. The Dataset includes gas turbine parameters (such as Turbine Inlet Temperature and Compressor Discharge pressure) in addition to the ambient variables. Problem statement: predicting turbine energy yield (TEY) using ambient variables as features. Attribute Information: The explanations of sensor measurements and their brief statistics are given below. Variable (Abbr.) Unit Min Max Mean Ambient temperature (AT) C –6.23 37.10 17.71 Ambient pressure (AP) mbar 985.85 1036.56 1013.07 Ambient humidity (AH) (%) 24.08 100.20 77.87 Air filter difference pressure (AFDP) mbar 2.09 7.61 3.93 Gas turbine exhaust pressure (GTEP) mbar 17.70 40.72 25.56 Turbine inlet temperature (TIT) C 1000.85 1100.89 1081.43 Turbine after temperature (TAT) C 511.04 550.61 546.16 Compressor discharge pressure (CDP) mbar 9.85 15.16 12.06 Turbine energy yield (TEY) MWH 100.02 179.50 133.51 Carbon monoxide (CO) mg/m3 0.00 44.10 2.37 Nitrogen oxides (NOx) mg/m3 25.90 119.91 65.29

Predicted Burned area of forest fires and Turbine yield energy using ANN Probem 1 PREDICT THE BURNED AREA OF FOREST FIRES WITH NEURAL NETWORKS Problem 2 The dataset contains 36733 instances of 11 sensor measures aggregated over one hour (by means of average or sum) from a gas turbine. The Dataset includes gas turbine parameters (such as Turbine Inlet Temperature and Compressor Discharge pressure) in addition to the ambient variables. Problem statement: predicting turbine energy yield (TEY) using ambient variables as features. Attribute Information: The explanations of sensor measurements and their brief statistics are given below. Variable (Abbr.) Unit Min Max Mean Ambient temperature (AT) C –6.23 37.10 17.71 Ambient pressure (AP) mbar 985.85 1036.56 1013.07 Ambient humidity (AH) (%) 24.08 100.20 77.87 Air filter difference pressure (AFDP) mbar 2.09 7.61 3.93 Gas turbine exhaust pressure (GTEP) mbar 17.70 40.72 25.56 Turbine inlet temperature (TIT) C 1000.85 1100.89 1081.43 Turbine after temperature (TAT) C 511.04 550.61 546.16 Compressor discharge pressure (CDP) mbar 9.85 15.16 12.06 Turbine energy yield (TEY) MWH 100.02 179.50 133.51 Carbon monoxide (CO) mg/m3 0.00 44.10 2.37 Nitrogen oxides (NOx) mg/m3 25.90 119.91 65.29

TEY

tey

teys

TEY

tey

tey

tey

tey

tey_bot