scholarly journals Estimation of daily dew point temperature using genetic programming and neural networks approaches

2013 ◽  
Vol 45 (2) ◽  
pp. 165-181 ◽  
Author(s):  
Jalal Shiri ◽  
Sungwon Kim ◽  
Ozgur Kisi
Keyword(s):  
Gene Expression Programming ◽  
Dew Point ◽  
Weather Data ◽  
Management Scenarios ◽  
Point Temperature ◽  
Dew Point Temperature ◽  
Ann Models ◽  
The Republic ◽  
Artificial Neural Network Ann ◽  
Comparison Of The Results

The present study investigates the ability of two different artificial neural network (ANN) models and gene expression programming (GEP) technique for estimating daily dew point temperature by using recorded weather data. The weather data used consist of 8 years of daily records of air temperature, wind speed, relative humidity, atmospheric pressure, incoming solar radiation and dew point temperature from two weather stations (Seoul and Incheon, in the Republic of Korea). Two different data management scenarios are applied in this paper. In the first scenario, weather data obtained from each station are used to estimate Tdew at the same station (at-station approach). In the second scenario, the ANN and GEP models are used for estimating dew point temperature of each station by using the data of the other station (cross-station application), through the optimal input combinations of the first scenario. Comparison of the results reveals that the GEP model surpasses ANN in estimating daily dew point temperature values.

scholarly journals Estimating Daily Dew Point Temperature Using Machine Learning Algorithms

Water ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 582 ◽  
Author(s):  
Sultan Noman Qasem ◽  
Saeed Samadianfard ◽  
Hamed Sadri Nahand ◽  
Amir Mosavi ◽  
Shahaboddin Shamshirband ◽  
...  
Keyword(s):  
Meteorological Parameters ◽  
Gene Expression Programming ◽  
Machine Learning Algorithms ◽  
Dew Point ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
M5 Model Tree ◽  
Point Temperature ◽  
Dew Point Temperature ◽  
Input Parameters

In the current study, the ability of three data-driven methods of Gene Expression Programming (GEP), M5 model tree (M5), and Support Vector Regression (SVR) were investigated in order to model and estimate the dew point temperature (DPT) at Tabriz station, Iran. For this purpose, meteorological parameters of daily average temperature (T), relative humidity (RH), actual vapor pressure (Vp), wind speed (W), and sunshine hours (S) were obtained from the meteorological organization of East Azerbaijan province, Iran for the period 1998 to 2016. Following this, the methods mentioned above were examined by defining 15 different input combinations of meteorological parameters. Additionally, root mean square error (RMSE) and the coefficient of determination (R2) were implemented to analyze the accuracy of the proposed methods. The results showed that the GEP-10 method, using three input parameters of T, RH, and S, with RMSE of 0.96°, the SVR-5, using two input parameters of T and RH, with RMSE of 0.44, and M5-15, using five input parameters of T, RH, Vp, W, and S with RMSE of 0.37 present better performance in the estimation of the DPT. As a conclusion, the M5-15 is recommended as the most precise model in the estimation of DPT in comparison with other considered models. As a conclusion, the obtained results proved the high capability of proposed M5 models in DPT estimation.

Comparison of individual and combined ANN models for prediction of air and dew point temperature

2013 ◽  
Vol 39 (2) ◽  
pp. 354-366 ◽  
Author(s):  
Karthik Nadig ◽  
Walter Potter ◽  
Gerrit Hoogenboom ◽  
Ronald McClendon
Keyword(s):  
Dew Point ◽  
Point Temperature ◽  
Dew Point Temperature ◽  
Ann Models

scholarly journals Spatial interpolation of daily humidity using natural neighbours over mountain areas in south eastern Australia

2008 ◽  
Vol 61 ◽  
pp. 292-295 ◽  
Author(s):  
K.S. Kim ◽  
R.M. Beresford ◽  
W.R. Henshall
Keyword(s):  
Dew Point ◽  
Lapse Rate ◽  
Weather Data ◽  
Mountain Areas ◽  
Mountain Area ◽  
Point Temperature ◽  
Dew Point Temperature ◽  
South Eastern ◽  
Eastern Australia ◽  
South Eastern Australia

Natural neighbour interpolation was investigated to estimate daily humidity at specific sites in a mountain area The Global Summary of Day (GSOD) dataset was used to obtain weather data in mountain areas in south eastern Australia Eighteen weather stations were selected as validation sites Dew point temperature was estimated from January to December 2007 When the inverse distance weight method was used without adjusting the elevation difference between stations accuracy of virtual dew point temperature was poor with a mean absolute error (MAE) of 36C When natural neighbour interpolation was used the MAE for dew point temperature was 21C with altitude adjustment Furthermore application of wet adiabatic lapse rate (0004C/m) for altitude adjustment reduced the MAE to 13C These results will be used to improve the accuracy of weather estimates in areas with complex terrain in order to implement crop disease predictions using risk models

scholarly journals A Typical Meteorological Year Generation Based on NASA Satellite Imagery (GEOS-I) for Sokoto, Nigeria

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Olayinka S. Ohunakin ◽  
Muyiwa S. Adaramola ◽  
Olanrewaju M. Oyewola ◽  
Richard L. Fagbenle ◽  
Fidelis I. Abam
Keyword(s):  
Energy Systems ◽  
Building Energy ◽  
Global Solar Radiation ◽  
Dew Point ◽  
Weather Data ◽  
Maximum Temperature ◽  
Building Energy Simulation ◽  
Dew Point Temperature ◽  
Northwest Region

Computer simulation of buildings and solar energy systems are being used increasingly in energy assessments and design. This paper evaluates the typical meteorological year (TMY) for Sokoto, northwest region, Nigeria, using 23-year hourly weather data including global solar radiation, dew point temperature, mean temperature, maximum temperature, minimum temperature, relative humidity, and wind speed. Filkenstein-Schafer statistical method was utilized for the creation of a TMY for the site. The persistence of mean dry bulb temperature and daily global horizontal radiation on the five candidate months were evaluated. TMY predictions were compared with the 23-year long-term average values and are found to have close agreement and can be used in building energy simulation for comparative energy efficiency study.

scholarly journals Improving efficiency and lowering operating costs of evaporative cooling

2021 ◽  
Vol 338 ◽  
pp. 01027
Author(s):  
Jan Taler ◽  
Bartosz Jagieła ◽  
Magdalena Jaremkiewicz
Keyword(s):  
Electricity Consumption ◽  
Open Circuit ◽  
Dew Point ◽  
Operating Costs ◽  
Water Evaporation ◽  
Total Water ◽  
Point Temperature ◽  
Dew Point Temperature ◽  
Adiabatic Cooling ◽  
Positive Effect

Cooling towers, or so-called evaporation towers, use the natural effect of water evaporation to dissipate heat in industrial and comfort installations. Water, until it changes its state of aggregation, from liquid to gas, consumes energy (2.257 kJ/kg). By consuming this energy, it lowers the air temperature to the wet-bulb temperature, thanks to which the medium can be cooled below the ambient temperature. Evaporative solutions are characterized by continuous water evaporation (approx. 1.5% of the total water flow) and low electricity consumption (high EER). Evaporative (adiabatic) cooling also has a positive effect on the reduction of electricity consumption of cooled machines. Lowering the relative humidity (RH) by approx. 2% lowers the wet-bulb temperature by approx. 0.5°C, which increases the efficiency of the tower, operating in an open circuit, expressed in kW, by approx. 5%, while reducing water consumption and treatment costs. The use of the M-Cycle (Maisotsenko cycle) to lower the temperature of the wet thermometer to the dew point temperature will reduce operating costs and increase the efficiency of cooled machines.

Sign in / Sign up

Export Citation Format

Share Document