Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Machine Learning Approaches for Accurate Prediction of Relative Humidity based on Temperature and Wet-Bulb Depression

Version 1 : Received: 5 February 2020 / Approved: 6 February 2020 / Online: 6 February 2020 (02:51:36 CET)
Version 2 : Received: 31 October 2020 / Approved: 2 November 2020 / Online: 2 November 2020 (09:44:25 CET)

How to cite: Luo, Q.; Shokri, M.; Dineva, A. Machine Learning Approaches for Accurate Prediction of Relative Humidity based on Temperature and Wet-Bulb Depression. Preprints 2020, 2020020075 (doi: 10.20944/preprints202002.0075.v1). Luo, Q.; Shokri, M.; Dineva, A. Machine Learning Approaches for Accurate Prediction of Relative Humidity based on Temperature and Wet-Bulb Depression. Preprints 2020, 2020020075 (doi: 10.20944/preprints202002.0075.v1).

Abstract

The main parameters for calculation of relative humidity are the wet-bulb depression and dry bulb temperature. In this work, easy-to-used predictive tools based on statistical learning concepts, i.e., the Adaptive Network-Based Fuzzy Inference System (ANFIS) and Least Square Support Vector Machine (LSSVM) are developed for calculating relative humidity in terms of wet bulb depression and dry bulb temperature. To evaluate the aforementioned models, some statistical analyses have been done between the actual and estimated data points. Results obtained from the present models showed their capabilities to calculate relative humidity for divers values of dry bulb temperatures and also wet-bulb depression. The obtained values of MSE and MRE were 0.132 and 0.931, 0.193 and 1.291 for the LSSVM and ANFIS approaches respectively. These developed tools are user-friend and can be of massive value for scientists especially, those dealing with air conditioning and wet cooling towers systems to have a noble check of the relative humidity in terms of wet bulb depression and dry bulb temperatures.

Subject Areas

wet-bulb depression; relative humidity; ANFIS; artificial neural network; LSSVM

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