Prediction of CCPP output based on improved fuzzy analytical hierarchy process

Chu, Fei; Wang, Jie; Lu, Nannan; Tan, Tian; Wang, Fuli

doi:10.1109/ccdc.2017.7979136

Cited by 1 publication

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nonetheless, various probabilistic approaches are previously used for CCPP power prediction including bagging and regression ANN [4,15]. However, their prediction error is slightly high [16], due to which, this paper proposes the machine learning algorithms for the prediction of electric power of CCPP operated on full load using the previously mentioned four parameters.…”

Section: Introductionmentioning

confidence: 99%

Power Prediction of Combined Cycle Power Plant (CCPP) Using Machine Learning Algorithm‐Based Paradigm

Siddiqui

Anwar

Ullah

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Power prediction is important not only for the smooth and economic operation of a combined cycle power plant (CCPP) but also to avoid technical issues such as power outages. In this work, we propose to utilize machine learning algorithms to predict the hourly-based electrical power generated by a CCPP. For this, the generated power is considered a function of four fundamental parameters which are relative humidity, atmospheric pressure, ambient temperature, and exhaust vacuum. The measurements of these parameters and their yielded output power are used to train and test the machine learning models. The dataset for the proposed research is gathered over a period of six years and taken from a standard and publicly available machine learning repository. The utilized machine algorithms are K -nearest neighbors (KNN), gradient-boosted regression tree (GBRT), linear regression (LR), artificial neural network (ANN), and deep neural network (DNN). We report state-of-the-art performance where GBRT outperforms not only the utilized algorithms but also all the previous methods on the given CCPP dataset. It achieves the minimum values of root mean square error (RMSE) of 2.58 and absolute error (AE) of 1.85.

show abstract

Section: Introductionmentioning

confidence: 99%