scholarly journals Home Thermal Modeling: Cooling Energy Consumption and Costs in Saudi Arabia

2016 ◽  
Vol 9 (4) ◽  
pp. 22
Author(s):  
Areej A. Malibari ◽  
Amjad H. Gamlo
Keyword(s):  
Saudi Arabia ◽  
Linear Regression ◽  
Thermal Model ◽  
Cooling System ◽  
Linear Regression Analysis ◽  
Grid Method ◽  
Data Fitting ◽  
Regression Method ◽  
Linear Regression Method ◽  
Non Linear

<p><strong>Objectives: </strong>The consumption of electricity and its costs are expected to be increased in Saudi Arabia due to its rapid growth in population. As the Kingdom is characterized by extreme hot climate, a massive amount of electricity consumed by the residential sector goes to power air conditioners. To control this huge amount of energyconsumedin homes, thermal models have been generated with two or more parameters. <strong>Methodology: </strong>The households’ surveys have been conducted in order to collect the data. The Non-linear regression analysis has been carried out to obtain the outcomes of study. Moreover, household surveys have been conducted for data collection. The grid algorithm and the non-linear regression have been used to learn the parameters in the model to simulate the weather in Saudi Arabia. The temperature loggers have been placed in the houses to observe the behavior of residents of using cooling system. The web forecast has been used to analyze the temperature of cities on hourly basis. <strong>Results: </strong>Simple thermal model has been built using two parameters by applying the grid and non-linear regression methods for data fitting. Then the thermal model with envelope has also been created using four parameters by applying non-linear regression method for data fitting. <strong>Conclusion: </strong>It has been evaluated through outcomes that thermal model with envelope is better as compared to simple thermal model. Moreover, the data fitting by non-linear regression method has also been observed to perform better than data fitting by grid method.</p>

A NEW PARAMETER ESTIMATION METHOD FOR ONLINE SOFT TISSUE CHARACTERIZATION

2016 ◽  
Vol 16 (08) ◽  
pp. 1640019 ◽  
Author(s):  
JAEHYUN SHIN ◽  
YONGMIN ZHONG ◽  
JULIAN SMITH ◽  
CHENGFAN GU
Keyword(s):  
Soft Tissue ◽  
Linear Regression ◽  
Tissue Characterization ◽  
Soft Tissues ◽  
Unscented Kalman Filter ◽  
Estimation Method ◽  
Regression Method ◽  
Linear Regression Method ◽  
Non Linear

Dynamic soft tissue characterization is of importance to robotic-assisted minimally invasive surgery. The traditional linear regression method is unsuited to handle the non-linear Hunt–Crossley (HC) model and its linearization process involves a linearization error. This paper presents a new non-linear estimation method for dynamic characterization of mechanical properties of soft tissues. In order to deal with non-linear and dynamic conditions involved in soft tissue characterization, this method improves the non-linearity and dynamics of the HC model by treating parameter [Formula: see text] as independent variable. Based on this, an unscented Kalman filter is developed for online estimation of soft tissue parameters. Simulations and comparison analysis demonstrate that the proposed method is able to estimate mechanical parameters for both homogeneous tissues and heterogeneous and multi-layer tissues, and the achieved performance is much better than that of the linear regression method.

scholarly journals Linear and Non-linear Regression Analysis for the Adsorption Kinetics of SO2 in a Fixed Carbon Bed Reactor – A Case Study

2021 ◽  
Author(s):  
Anna M. Kisiela-Czajka ◽  
Bartosz Dziejarski
Keyword(s):  
Linear Regression ◽  
Activated Carbons ◽  
Coal Dust ◽  
Linear Regression Analysis ◽  
Heterogeneous Surface ◽  
Hard Coal ◽  
Linear Regression Method ◽  
Order Kinetic ◽  
Lignite Fly Ash ◽  
Non Linear

Kinetic parameters of SO2 adsorption on unburned carbons from lignite fly ash and activated carbons based on hard coal dust were determined. The model studies were performed using the linear and non-linear regression method for the following models: pseudo first and second-order, intraparticle diffusion, and chemisorption on a heterogeneous surface. The quality of the fitting of a given model to empirical data was assessed based on: R2, R, &Delta;q, SSE, ARE, &chi;2, HYBRID, MPSD, EABS, and SNE. It was clearly shown that it is the linear regression that more accurately reflects the behaviour of the adsorption system, which is consistent with the first-order kinetic reaction &ndash; for activated carbons (SO2+Ar) or chemisorption on a heterogeneous surface &ndash; for unburned carbons (SO2+Ar and SO2+Ar+H2O(g)+O2) and activated carbons (SO2+Ar+H2O(g)+O2). Importantly, usually, each of the approaches (linear/non-linear) indicated a different mechanism of the studied phenomenon. A certain universality of the &chi;2 and HYBRID functions has been proved, the minimization of which repeatedly led to the lowest SNE values for the indicated models. Fitting data by any of the non-linear equations based on the R or R2 functions only, cannot be treated as evidence/prerequisite of the existence of a given adsorption mechanism.

scholarly journals Analisis Pengaruh Faktor Kebutuhan Energi Listrik Tahun 2015 Terhadap Daya Yang Tersambung Dan Energi Yang Terjual Menggunakan Regresi Linear Sederhana (Studi Kasus Pada PT. PLN (Persero) Unit Area Pelayanan Dan Jaringan (APJ) Palembang)

2018 ◽  
Vol 9 (1) ◽  
Author(s):  
Nazori Suhandi ◽  
Irma Yuliawati ◽  
Indah Charista
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Linear Regression ◽  
Linear Regression Analysis ◽  
Electric Energy ◽  
Electrical Energy ◽  
Regression Method ◽  
Linear Regression Method ◽  
Simple Linear Regression ◽  
Level Of Significance

<p class="SammaryHeader" align="center"><strong><em>Abstract</em></strong></p><p><em>The availability of electrical energy is a very important aspect and even become a parameter to support the successful development of a region. Proper management of electrical energy resources and directed clearly will make the potential possessed of an area developed and utilized optimally. Population growth and economic development of a region can be influenced by the use of electrical energy. The supply of electricity must be taken into account so that the electrical energy can be available in an amount that suits your needs. Demand for the use of electricity in Indonesia will always increase with economic growth in addition to the development of electrical energy is also influenced by the development of the population in terms of quantity of customers to be electricity. Predicting methods such as using time series method (Gustriansyah, 2017) or data mining methods. The purpose of this research is to know how to overcome the influence of electricity usage (VA) connected with electric energy sold (KWh). Research done by simple linear regression method to facilitate writer in processing data. Based on the calculation result using simple linear regression method can be concluded 99.2% of the variation of electric power connected can be explained by the variable amount of electrical energy sold. While the rest (100% - 99.2% = 0.8%) is explained by other causes. And the level of significance &lt;0.05 so that the regression model can be used to predict the electrical energy sold.</em></p><p><strong><em>Keywords : </em></strong><em>Linear regression, analysis, electrical energy</em></p><p class="SammaryHeader" align="center"> </p><p class="SammaryHeader" align="center"><strong><em>Abstrak</em></strong></p><p><em>Ketersediaan energi listrik merupakan aspek yang sangat penting dan bahkan menjadi suatu parameter untuk mendukung keberhasilan pembangunan suatu daerah. Pengelolaan sumber daya energi listrik yang tepat dan terarah dengan jelas akan menjadikan potensi yang dimiliki suatu wilayah berkembang dan termanfaatkan secara optimal. Pertumbuhan populasi dan perkembangan ekonomi suatu wilayah dapat dipengaruhi penggunaan energi listrik. Penyediaan listrik harus diperhitungkan sehingga energi listrik dapat tersedia dalam jumlah yang sesuai dengan kebutuhan Anda. Permintaan untuk penggunaan energi listrik di Indonesia akan selalu meningkat dengan pertumbuhan ekonomi disamping pengembangan energi listrik juga dipengaruhi oleh perkembangan populasi dalam hal kuantitas pelanggan yang akan dialiri listrik. </em><em>Metode untuk memprediksi seperti menggunakan metode time series (Gustriansyah, 2017) atau metode data mining.</em><em> Adapun tujuan dari penelitian ini adalah untuk mengetahui bagaimana cara mengatasi pengaruh penggunaan tenaga listrik (VA) yang terhubung dengan energi listrik yang terjual (KWh). Penelitian dilakukan dengan metode regresi linier sederhana agar memudahkan penulis dalam mengolah data. Berdasarkan hasil perhitungan menggunakan metode regresi linier sederhana dapat disimpulkan sebesar 99,2% dari variasi daya listrik yang terhubung dapat dijelaskan oleh variabel jumlah energi listrik yang terjual. Sedangkan sisanya (100% - 99,2% = 0,8%) dijelaskan oleh penyebab lain. Dan tingkat signifikansi &lt;0,05 sehingga model regresi dapat digunakan untuk memprediksi energi listrik yang terjual.</em></p><p align="left"><strong><em>Kata kunc</em></strong><em>i: Regresi linier, analisis, energi listrik</em></p>

Polynomial (Non Linear) Regression Method for Improved Estimation Based on Sampling

2008 ◽  
Vol 8 (8) ◽  
pp. 1597-1599 ◽  
Author(s):  
Emmanuel John Ekpen ◽  
Mfon Ime Okonna ◽  
Eno Donatus Jo
Keyword(s):  
Linear Regression ◽  
Regression Method ◽  
Linear Regression Method ◽  
Non Linear

scholarly journals Adsorption of anionic dyes onto natural, thermally and chemically modified smectite clays

2014 ◽  
Vol 16 (4) ◽  
pp. 33-40 ◽  
Author(s):  
Joanna Kyzioł-Komosińska ◽  
Czesława Rosik-Dulewska ◽  
Magdalena Pająk ◽  
Iwona Krzyżewska ◽  
Agnieszka Dzieniszewska
Keyword(s):  
Linear Regression ◽  
Adsorption Capacity ◽  
Regression Method ◽  
Linear Regression Method ◽  
Anionic Dyes ◽  
Charged Surfaces ◽  
Smectite Clays ◽  
Direct Blue ◽  
Non Linear ◽  
Better Than

Abstract The aim of this study was to determine the adsorption capacity of the smectite clays (from the overburden of the lignite deposit in Belchatow) for two anionic dyes, i.e. Reactive Blue 81 (RB-81) and Direct Blue 74 (DB-74). Additionally, the influence of the thermal and chemical (acid and alkali) clay modifications on the amount of bonded dyes was investigated. The adsorption capacity of the clay (natural and modified) was different for studied dyes and depended on the initial concentration and modification type. All the modified clays adsorbed the dyes at pH>pHPZC as the negatively charged surfaces of their particles (in accordance with the formula: AOH ↔ AO- + H+) prevented the formation of electrostatic bonds between the anionic dyes and the clay surface. The dyes were mainly bound with the hydrogen bonds forming between the donor groups in the dyes and the acceptor groups (-SiO and -Al2OH) in the clays. The coefficients in the adsorption isotherms were estimated with the linear and non-linear regression. The linear regression method was found that the Freundlich and Dubinin-Radushkevich isotherms described the dye sorption much better than the Langmuir model. On the other hand, all three models described well the experimental data in the non-linear regression method. Furthermore, the 1/n value (<1) obtained from the Freundlich equation for all the dye-sorbent systems indicated the favorable sorption.

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