Optimized deep learning-based prediction model for chiller performance prediction

Abstract

With the development of primary energy consumption and global climate change are encouraged air conditioning chillers to conventional air-conditioning methods. In the last previous couple of decades, several absorption technologies are introduced for absorption cooling systems and solar cooling to achieve higher chiller performance. In this paper, an efficient and optimal chiller performance prediction mechanism, named Spider monkey Bat Algorithm (SMBat)-based Generative adversarial network (SMBat-based GAN), is proposed. It is exploited to predict the chiller performance from time-series data. The proposed SMBat algorithm integrates the Spider Monkey Optimization (SMO) and the Bat algorithm. The GAN classifier is used to predict the chiller performance using the time series data based on the fitness function. In addition, extraction of the feature is achieved using the chilled water supply temperature, chilled water return temperature, condenser water return temperature, chiller water flow, cooling capacity, and power utilization. The features are selected by the wrapper method for selecting the appropriate features for better chiller performance prediction. Then these features are forwarded to the prediction module, where the prediction is done based on the proposed SMBat-based GAN. Finally, the experimental analysis exhibits that the proposed model offered better performance based on the metrics, such as Mean Square Error (MSE), R2-score, and Mean Absolute Error (MAE) when considering four datasets. The developed SMBat-based GAN attained better results for dataset-3 with a minimal MAE of 0.008, a minimal MSE of 0.0001, and a maximal R2-score of 0.999. The R2-score of the proposed method is 5.905%, 0.4%, 0.2%, and 1% higher when compared to the existing approaches, namely, regression model, ANN+ Bat, ANN, and Multi obj GA+NN.