A Novel Machine Learning Technique by Several Meta and Naïve Bayes Method to Predict Full Load Electrical Power Output of Base Load Operated Combined Cycle Power Plant
Keywords:
Bagging, Logit Boost, F-Measure, Random Committee, Combined cycle power plant.Abstract
Avoiding technical concerns, such blackouts, is crucial to the efficient and cost-effective operation of a combined cycle power plant (CCPP). In this paper, we suggest making use of machine learning methods to estimate the hourly electricity output of a CCPP. For this purpose, we take into account the exhaust vacuum, ambient temperature, atmospheric pressure, and relative humidity as basic parameters that affect the generated power. The output power and other parameters are measured and utilized to train and test machine learning models. This paper explores the Logit Boost with Bagging perform well as well it showing an efficient outcome. It has the greatest accuracy result of 85.80%. The Logit Boost with Bagging produces the greatest precision result of 0.87. The Logit Boost with Bagging and Random Committee Bagging produce the maximum recall of 0.87. The Logit Boost with Bagging has the greatest F-Measure result of 0.87. The Logit Boost with Bagging model has the highest MCC value of 0.66. The Logit Boost with Bagging model has the greatest kappa value of 0.67. The Logit Boost with Bagging model has an optimal results compare with other models.
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