scholarly journals Neural Methods Comparison for Prediction of Heating Energy Based on Few Hundreds Enhanced Buildings in Four Season’s Climate

Energies ◽  
2020 ◽  
Vol 13 (20) ◽  
pp. 5453 ◽  
Author(s):  
Tomasz Szul ◽  
Krzysztof Nęcka ◽  
Thomas G. Mathia
Keyword(s):  
Energy Consumption ◽  
Energy Use ◽  
Residential Building ◽  
Regression Trees ◽  
Predictive Modelling ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
New Paradigm

Sustainable development and the increasing demand for equitable energy use as well as the reduction of waste of energy are the author’s social and scientific motivations. This new paradigm is the selection of a pertinent methodology to evaluate the efficiency of habitat thermomodernization, which is one of the scientific tasks of the presented study. In order to meet the social and scientific requirements, 380 buildings from the end of the last century (made of large plate technology), which were thermally improved at the beginning of the XXI century, were designed for a comparative analysis of the predictive modelling of heating energy consumption. A specific set of important variables characterizing the examined buildings has been identified. Groups of variables were used to estimate the energy consumption in such a way as to achieve a compromise between the difficulty of obtaining them and the quality of forecast. To predict energy consumption, the six most appropriate neural methods were used: artificial neural networks (ANN), general regression trees (CART), exhaustive regression trees (CHAID), support regression trees (SRT), support vectors (SV), and method multivariant adaptive regression splines (MARS). The quality assessment of the developed models used the mean absolute percentage error (MAPE) also known as mean absolute percentage deviation (MAPD), as well as mean bias error (MBE), coefficient of variance of the root mean square error (CV RMSE) and coefficient of determination (R2), which are accepted as statistical calibration standards by (American Society of Heating, Refrigerating and Air-Conditioning Engineers) ASHRAE. On this basis, the most effective method has been chosen, which gives the best results and therefore allows to forecast with great precision the energy consumption (after thermal improvement) for this type of residential building.

scholarly journals An artificial neural network-based prediction model for utilization of coal ash in production of fired clay bricks: A review

2021 ◽  
Vol 53 (1) ◽  
pp. 37-53
Author(s):  
Milica Vidak-Vasic ◽  
Lato Pezo ◽  
Vivek Gupta ◽  
Sandeep Chaudhary ◽  
Zagorka Radojevic
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Coal Ash ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Clay Bricks ◽  
Coal Ashes ◽  
Artificial Neural

This study analyzed the last 20 years` data available on power plant coal ashes used in clay brick production. The statistical analysis has been carried out for a total of 302 cases based on the relevant parameters reported in the literature. The chemical composition of the clays and coal ashes, percentage incorporation and maximum particle size of ash, size of fired samples, peak firing temperature, and the corresponding soaking time were selected as inputs for modeling. The product characteristics i.e. open porosity, water absorption, and compressive strength was taken as output parameters. An artificial neural network model has been developed and showed a satisfactory fit to experimental data and predicted the observed output variables with the overall coefficient of determination (r2) of 0.972 during the training period. Besides, the reduced chi-square, mean bias error, root mean square error, and mean percentage error were utilized to check the correctness of the obtained model, which proved the network generalization capability. The sensitivity analysis of the model suggested that the quantity of Na2O coming from brick clays, the percentages of SiO2 and K2O coming from ashes, and MgO coming from clays were the most influential parameters in descending order for the ash-clay composite bricks` quality, mostly owing to the influence of fluxes during firing.

Evaluation of Global Solar Radiation using Sunshine Hour, Temperature and Relative Humidity at Low Land Region of Nepal

2020 ◽  
Vol 6 (1) ◽  
pp. 16-24
Author(s):  
U. Joshi ◽  
K.N. Poudyal ◽  
I.B. Karki ◽  
N.P. Chapagain
Keyword(s):  
Solar Radiation ◽  
Meteorological Data ◽  
Global Solar Radiation ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Energy Potential ◽  
To Come ◽  
Empirical Constants

The accurate knowledge of solar energy potential is essential for agricultural scientists, energy engineers, architects and hydrologists for relevant applications in concerned fields. It is cleanest and freely available renewable energy measured using CMP6 Pyranometer. However, it is quite challenging to acquire accurate solar radiation data in different locations of Nepal because of the high cost of instruments and maintenances. In these circumstances, it is essential to select an appropriate empirical model to predict global solar radiation for the use of future at low land, Nepalgunj (28.102°N, 81.668°E and alt. 165 masl) for the year 2011-2012. In this paper, six different empirical models have been used based on regression technique, provided the meteorological data. The empirical constants (a = 0.61, b = 0.05, c = -0.0012 and d = -0.017) are obtained to predict Global solar radiation. The values of statistical tools such as mean percentage error, mean bias error, root mean square error, and coefficient of determination obtained for Abdalla model are 1.99%, 0.003 MJ/m2/day, 2.04 MJ/m2/day and 0.74 respectively. Using the error analysis, it is concluded that the Abdalla model is better than others. So the empirical constants of this model are utilized to predict the global solar radiation to the similar geographical sites of Nepal for the years to come and it can be used to estimate the missing data of solar radiation for the respective sites.

scholarly journals INVESTIGATION ON THE VALIANTZAS’ EVAPOTRANSPIRATION MODELS FOR PENINSULAR MALAYSIA

2019 ◽  
pp. 174-182
Author(s):  
Nor Farah Atiqah Binti Ahmad ◽  
Sobri Harun ◽  
Haza Nuzly Abdull Hamed ◽  
Muhamad Askari ◽  
Zulkiflee Ibrahim ◽  
...  
Keyword(s):  
Accurate Method ◽  
Peninsular Malaysia ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Mean Square ◽  
The World ◽  
Good Agreement ◽  
Agricultural Organization

The search for an accurate evapotranspiration (ET) continues when the world has responsibility to cope with the water scarcity issue, population outgrown and uncertain change of weather. Measuring actual evapotranspiration (ETa) can be tedious and requires a lot of time and cost. Therefore, numbers of empirical ET models have been developed to overcome this problem. The Valiantzas’ models are quite familiar to the hydrologist community as it has been developed based on Penman evaporation equation. This paper presents the evaluation on the selected six Valiantzas’ models by comparing to Food and Agricultural Organization Penman-Montieth (FAO-PM) empirical model in estimating ET in the Peninsular Malaysia. Seventeen meteorological stations around Peninsular Malaysia with data gathered from 1987 till 2003 were tested. The performance for each model was evaluated by root mean square error (RMSE), coefficient of determination (R2), percentage error (PE) and mean bias error (MBE). All the six models showed good agreement to FAO-PM with R2> 0.90. The PETval2 model which gave R2 of 0.97 was the best performer with the lowest RMSE, PE and MBE of 0.26, 5.5% and 0.14, respectively. The good and sensible performance on the ET estimation displayed by Valiantzas’ model may promise an accurate method for calculation on the water management for irrigation and catchment studies.

scholarly journals Empirical models for predicting global solar radiation using meteorological parameters for Sokoto, Nigeria

2019 ◽  
Vol 7 (2) ◽  
pp. 48
Author(s):  
Davidson O. Akpootu ◽  
Bello I. Tijjani ◽  
Usman M. Gana
Keyword(s):  
Solar Radiation ◽  
Meteorological Parameters ◽  
Meteorological Data ◽  
Climatic Variability ◽  
Global Solar Radiation ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Radiation Maximum

The performances of sunshine, temperature and multivariate models for the estimation of global solar radiation for Sokoto (Latitude 13.020N, Longitude 05.250E and 350.8 m asl) located in the Sahelian region in Nigeria were evaluated using measured monthly average daily global solar radiation, maximum and minimum temperatures, sunshine hours, rainfall, wind speed, cloud cover and relative humidity meteorological data during the period of thirty one years (1980-2010). The comparison assessment of the models was carried out using statistical indices of coefficient of determination (R2), Mean Bias Error (MBE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), t – test, Nash – Sutcliffe Equation (NSE) and Index of Agreement (IA). For the sunshine based models, a total of ten (10) models were developed, nine (9) existing and one author’s sunshine based model. For the temperature based models, a total of four (4) models were developed, three (3) existing and one author’s temperature based model. The results of the existing and newly developed author’s sunshine and temperature based models were compared and the best empirical model was identified and recommended. The results indicated that the author’s quadratic sunshine based model involving the latitude and the exponent temperature based models are found more suitable for global solar radiation estimation in Sokoto. The evaluated existing Ångström type sunshine based model for the location was compared with those available in literature from other studies and was found more suitable for estimating global solar radiation. Comparing the most suitable sunshine and temperature based models revealed that the temperature based models is more appropriate in the location. The developed multivariate regression models are found suitable as evaluation depends on the available combination of the meteorological parameters based on two to six variable correlations. The recommended models are found suitable for estimating global solar radiation in Sokoto and regions with similar climatic information with higher accuracy and climatic variability.   

scholarly journals Evaluation of the Operational Simplified Surface Energy Balance Model for Pastureland Evapotranspiration Mapping and Drought Monitoring in North Central Kentucky

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Maheteme Gebremedhin ◽  
Ian Ries ◽  
Gabriel B. Senay ◽  
Martin Matisoff ◽  
Ibukun Amusan ◽  
...  
Keyword(s):  
Energy Balance ◽  
Surface Energy ◽  
Surface Energy Balance ◽  
Drought Monitoring ◽  
Balance Model ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
North Central

The use of remotely sensed evapotranspiration (ET) for field applications in drought monitoring and assessment is gaining momentum, but meeting this need has been hampered by the absence of extensive ground-based measurement stations for ground validation across agricultural zones and natural landscapes. This is particularly crucial for regions more prone to recurring droughts with limited ground monitoring stations. A three-year (2016–2018) flux ET dataset from a pastureland in north central Kentucky was used to validate the Operational Simplified Surface Energy Balance (SSEBop) model at monthly and annual scales. Flux and SSEBop ET track each other in a consistent manner in response to seasonal changes. The mean bias error (MBE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R2) were 5.47, 21.49 mm mon−1, 30.94%, and 0.87, respectively. The model consistently underestimated ET values during winter months and overestimated them during summer months. SSEBop’s monthly ET anomaly maps show spatial ET distribution and its accurate representation. This is particularly important in areas where detailed surface meteorological and hydrological data are limited. Overall, the model estimated monthly ET magnitude satisfactorily and captured it seasonally. The SSEBop’s functionality for remote ET estimation and anomaly detection, if properly coupled with ground measurements, can significantly enhance SSEBop’s ability to monitor drought occurrence and prevalence quickly and accurately.

scholarly journals New temperature dependent models for estimating global solar radiation across the midland climatic zone of Nigeria

2019 ◽  
Vol 7 (2) ◽  
pp. 70
Author(s):  
Davidson O. Akpootu ◽  
Bello I. Tijjani ◽  
Usman M. Gana
Keyword(s):  
Solar Radiation ◽  
Meteorological Data ◽  
Global Solar Radiation ◽  
Climatic Zone ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Temperature Dependent ◽  
Statistical Validation

Authentic information of the availability of global solar radiation is significant to agro/hydro meteorologists, atmospheric Physicists and solar energy engineers for the purpose of local and international marketing, designs and manufacturing of solar equipment. In this study, five new proposed temperature dependent models were evaluated using measured monthly average daily global solar radiation, maximum and minimum temperature meteorological data during the period of thirty one years (1980-2010). The new models were compared with three existing temperature dependent models (Chen et al., Hargreaves and Samani and Garcia) using seven different statistical validation indicators of coefficient of determination (R2), Mean Bias Error (MBE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), t – test, Nash – Sutcliffe Equation (NSE) and Index of Agreement (IA) to ascertain the suitability of global solar radiation estimation in five different locations (Zaria, Bauchi, Jos, Minna and Yola) situated in the Midland climatic zone of Nigeria. In each location, the result shows that a new empirical regression model was found more accurate when compared to the existing models and are therefore recommended for estimating global solar radiation in the location and regions with similar climatic information where only temperature data are available. The evaluated existing Hargreaves and Samani and Garcia temperature based models for Jos were compared to those available in literature and was found more suitable for estimating global solar radiation for the location. The comparison between the measured and estimated temperature dependent models depicts slight overestimation and underestimation in some months with good fitting in the studied locations. However, the recommended models give the best fitting.   

scholarly journals Calibration of Empirical Equations for Estimating Reference Evapotranspiration in Different Climates of Iran

2021 ◽  
Author(s):  
Saeed Sharafi ◽  
Mehdi Mohammadi Ghaleni
Keyword(s):  
Mass Transfer ◽  
Solar Radiation ◽  
Reference Evapotranspiration ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Accurate Estimation ◽  
Bias Error ◽  
Empirical Equations ◽  
Different Climates

Abstract The accurate estimation of reference evapotranspiration (ETref) is a crucial component for modeling hydrological and ecological cycles. The goal of this study was the calibration of 32 empirical equations used to determine ETref in the three classes of temperature-based, solar radiation-based and mass transfer–based evapotranspiration. The calibration was based on measurements taken between the years 1990 and 2019 at 41 synoptic stations located in very dry, dry, semidry and humid climates of Iran. The performance of the original and calibrated empirical equations compared to the PM-FAO56 equation was evaluated based on model evaluation techniques including: the coefficient of determination (R2), the root mean square error (RMSE), the average percentage error (APE), the mean bias error (MBE), the index of agreement (D) and the scatter index (SI). The results show that the calibrated Baier and Robertson equation for temperature-based models, the Makkink equation for solar radiation–based models and the Penman equation for mass transfer–based models performed better than the original empirical equations. The calibrated equations had, respectively, an average R2 = 0.73, 0.67 and 0.78; RMSE = 35.14, 35.02 and 30.20 mm year− 1; and MBE=-5.6, -3.89 and 2.57 mm year− 1. The original empirical equations had values of average R2 = 0.60, 0.37 and 0.65; RMSE = 68.34, 66.98 and 52.62 mm year− 1; and MBE=-5.75, 4.26 and 8.99 mm year− 1, respectively. The calibrated empirical equations for very dry climate (e.g. Zabol, Zahedan, Bam, Iranshahr and Chabahar stations) also significantly reduced the SI value from SI > 0.3 (poor class) to SI < 0.1 (excellent class). Therefore, the calibrated empirical equations are highly recommended for estimating ETref in different climates.

scholarly journals Estimation of Solar Energy Using Different Empirical Models at Mid Hill, Nepal

2021 ◽  
Vol 7 (2) ◽  
pp. 42-48
Author(s):  
U. Joshi ◽  
P. M. Shrestha ◽  
S. Maharjan ◽  
B. Maharjan ◽  
N. P. Chapagain ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Solar Radiation ◽  
Solar Energy ◽  
Global Solar Radiation ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Sunshine Hour ◽  
Empirical Constants

Accurate knowledge of global solar radiation distribution is essential for designing, sizing, and performing an evaluation of solar energy system in any part of the world. However, it is not available in many sites of Nepal due to the high expense of the technical process. This study is focused on the performance of different models based on daily global solar radiation, sunshine hour, temperature, and relative humidity at mid-hill region Lumle, (lat. 28.29650N, long. 83.8179oE, and Alt. 1740.0 m.a.s.l.). This study is carried for the year 2018 to 2020. The performance of different models based on sunshine hour, temperature, and relative humidity were analyzed using the regression technique and statistical tools such as Root Mean Square Error (RMSE), Mean Bias Error (MBE), Mean Percentage Error (MPE), and Coefficient of determination (R2). After the analysis, the modified Angstrom model (M-9) based on temperature difference and relative humidity was found to be the best in terms of accuracy of least RMSE value and highest coefficient of determination. Finally, the empirical constants for model m-9 are a = 0.003, b = 0.523, c = 0.118 and, d = 0.002 obtained. The calculated empirical constants can be utilized for the prediction of GSR at similar geographical locations of Nepal.

A Robust Methodology for Assessing the Effectiveness of Site Adaptation Techniques for Calibration of Solar Radiation Data

2020 ◽  
Vol 143 (3) ◽  
Author(s):  
Yatindra Kumar Ramgolam ◽  
Kaviraj Bangarigadu ◽  
Tavish Hookoom
Keyword(s):  
Solar Energy ◽  
Linear Method ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Data Sets ◽  
Average Data ◽  
Yearly Average

Abstract The abundant spatial and temporal availability of solar energy has been fueling many researches and have been the reason for the proliferation of solar energy applications in the past decades. Many of these applications involve heavy investments and thus require highly accurate and reliable long-term average solar data for efficient deployment of solar energy technologies. Since ground stations are costly, site-specific, scarce and cannot provide long-term solar data, satellite-derived data is the next best alternative. However, satellite models are often unable to capture the complex local climatological variations of a given site. As such, short-term high precision solar ground measurements are used to train the satellite model so as to improve the accuracy of long-term solar estimates. There exist several site adaptation techniques to perform this task. However, to the knowledge of the researchers, no comparative study has been conducted to establish which site adaptation technique is the most effective. In this study, a robust methodology has been proposed to compare the effectiveness of four site adaptation techniques for monthly and yearly data sets using novel key performance indicators. Ground measurements from 12 stations in the tropical islands of Mauritius, Rodrigues, and Agalega were used to adapt satellite data obtained from HelioClim-3 database using different techniques. Three new nonlinear site adaptation techniques have been proposed: adjustment technique (Technique 2), compensation technique (Technique 3), and relationship technique (Technique 4). The first part of the study showed that 67–100% of the data sets were best approximated with sixth-order polynomials for the three nonlinear techniques. The second part revealed that Technique 1 (linear method) and Technique 2 were most appropriate for maximum and average data sets, respectively. The results were such that Technique 2 and Technique 1 provided best approximations for77.9–83.3% and 40.7–58.3% of average and maximum data sets, respectively. In the third part of the study, only Technique 2 provided remarkable improvements for all statistical metrics with respect to the original monthly data sets (113–118 data sets). The analysis reported 57.6–89.9%, 49.8–68.0%, 67.4–87.3%, 53.8–63.1%, 45.0–64.0%, 7.7–9.6% and 2.7–4.7% mean improvements for mean bias error (MBE), mean absolute bias error (MABE), mean percentage error (MPE), mean absolute percentage error (MAPE), root-mean-square error (RMSE), Nash–Sutcliffe (NSE), and coefficient of determination (COD), respectively, for Technique 2. Similar results were observed for yearly average data sets while the appreciation was shared among all four techniques for yearly maximum data sets, with Technique 1 having a slight advantage.

scholarly journals Performance Evaluation of Models Established for the Estimation of Diffused Solar Radiation: Case Study Lahore, Pakistan

2019 ◽  
Vol 10 (1) ◽  
pp. 113-119
Author(s):  
Saif Ur Rehman ◽  
Muhammad Shoaib ◽  
Imran Siddiqui ◽  
S. Zeeshan Abbas
Keyword(s):  
Solar Radiation ◽  
Goodness Of Fit ◽  
Statistical Error ◽  
Global Solar Radiation ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Clearness Index ◽  
Diffuse Solar Radiation

A suitable design of solar power project requires accurate measurements of solar radiation for the site ofinvestigation. Such measurements play a pivotal role in the installation of PV systems. While conducting such studies,in general, global solar radiation (GSR) is recorded, whereas diffuse component of solar radiation on a horizontalsurface is seldom recorded. The objective of the present study is to assess diffuse solar radiation (DSR) on horizontalsurfaces by using polynomial models for Lahore, Pakistan (27.89 N, 78.08 E) and by correlating clearness index withdiffuse fraction. The established models are compared with some of the existing models from the literature.Performance of models is evaluated by employing five goodness-of-fit (GoF) tests that are, mean bias error (MBE),root mean square (RMSE), Coefficient of Determination (R2), Mean Absolute Percentage Error (MAPE) and Akaike’sInformation Criterion (AIC). The comparison of the results of goodness-of-fit tests with those of existing modelsindicate that the models established in the present study are performed better as compared to the existing models. Thevalues of statistical error analysis further suggested that a cubic model with a good accuracy of 97.5% and AIC of -22.8is relatively more suitable for this climatic region for estimating diffuse solar radiation. The study shows that the modeldeveloped is in good agreement with Elhadidy and Nabi model with an accuracy of 96.1% and AIC of 4.4 andsatisfactory results are obtained for Lahore. The findings can help to give a generous understanding of solar radiation inorder to optimize the solar energy conversion systems. The results of this study provide a better understanding of theassociations between global solar radiation, clearness index and diffused fraction for the region under study.

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