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- 저자 : Mankad, Ravin (검색결과 3 건)
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Comparing OLS based hedonic model and ANN in house price estimation using relative location
Mankad Mudit D. 대한공간정보학회 2022 Spatial Information Research Vol.30 No.2
Accurate house price estimation is of great importance not only to various real estates stakeholders such as house owners, buyers, investors, and agents but also to banking and insurance sectors among others. This estimation is a very challenging and complex task as it involves a variety of attributes. Among all attributes, location is one of the most influential attributes affecting house price. The present study has incorporated selected structural attributes and relative location through spatial attributes (geographical, infrastructural, and neighborhood) for modeling the influence of location on house prices. The hedonic model, a traditional method for estimating house price has been criticized due to nonlinearity, multicollinearity, and heteroskedasticity problems. Unlike the hedonic model, the Artificial Neural Network (ANN) permits nonlinear relationships and also tries to solve the problem of multicollinearity. The present study aims to examine the application of ANN in estimating accurate house prices and comparing the results with the Ordinary Least Squares (OLS) based hedonic model. The Gotri area located in the western part of Vadodara city, India is considered as a case study. TensorFlow (a python environment) of Google is used to implement the ANN model. In ANN model building, a three-layer network with a single hidden layer and RELU (REctified Linear Unit) activation function is adopted. The estimation performance has been evaluated by employing Root Mean Squared Error and Mean Absolute Percentage Error. According to results, ANN was found better when compared to OLS in terms of both the performance measures. This paper suggests that the ANN estimator could be a complement to the OLS based linear regression method.
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Comparing OLS based hedonic model and ANN in house price estimation using relative location
Mankad Mudit D. 대한공간정보학회 2022 Spatial Information Research Vol.30 No.1
Accurate house price estimation is of great importance not only to various real estates stakeholders such as house owners, buyers, investors, and agents but also to banking and insurance sectors among others. This estimation is a very challenging and complex task as it involves a variety of attributes. Among all attributes, location is one of the most influential attributes affecting house price. The present study has incorporated selected structural attributes and relative location through spatial attributes (geographical, infrastructural, and neighborhood) for modeling the influence of location on house prices. The hedonic model, a traditional method for estimating house price has been criticized due to nonlinearity, multicollinearity, and heteroskedasticity problems. Unlike the hedonic model, the Artificial Neural Network (ANN) permits nonlinear relationships and also tries to solve the problem of multicollinearity. The present study aims to examine the application of ANN in estimating accurate house prices and comparing the results with the Ordinary Least Squares (OLS) based hedonic model. The Gotri area located in the western part of Vadodara city, India is considered as a case study. TensorFlow (a python environment) of Google is used to implement the ANN model. In ANN model building, a three-layer network with a single hidden layer and RELU (REctified Linear Unit) activation function is adopted. The estimation performance has been evaluated by employing Root Mean Squared Error and Mean Absolute Percentage Error. According to results, ANN was found better when compared to OLS in terms of both the performance measures. This paper suggests that the ANN estimator could be a complement to the OLS based linear regression method.
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Effects of the incorporation of alkali elements on Cu(In,Ga)Se<sub>2</sub> thin film solar cells
Shin, Donghyeop,Kim, Jekyung,Gershon, Talia,Mankad, Ravin,Hopstaken, Marinus,Guha, Supratik,Ahn, Byung Tae,Shin, Byungha Elsevier 2016 Solar energy materials and solar cells Vol.157 No.-
<P><B>Abstract</B></P> <P>This study describes in detail the effects of sodium and potassium on Cu(In,Ga)Se<SUB>2</SUB> (CIGS) absorbers and solar cells. We report on the influence of these species on the surface and bulk composition as well as bulk defect structure of CIGS films as revealed by X-ray photoelectron spectroscopy (XPS), secondary ion mass spectroscopy (SIMS), and photoluminescence (PL). From the XPS studies it is found that Na and K promote oxygen absorption onto the CIGS films. Furthermore, potassium accelerates the formation of indium and gallium oxides on the film surface, making the surface Cu-deficient. Low temperature PL studies suggest that (i) Na and K help passivate non-radiative recombination centers, presumably at the grain boundaries, and (ii) Na further impacts the bulk defect structure inside of CIGS grains, which is not observed with K. This change in bulk defect structure is attributed to the greater diffusivity of Na in CIGS relative to K due to the smaller atomic size. This in-depth study (integration of XPS, SIMS, PL, and device characteristics) reveals that the surface chemistry and the grain boundary passivation have stronger influences on the device performance than the bulk defect structure.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Effects of alkali elements-Na and K-onCIGS are studied using SIMS, XPS, and PL. </LI> <LI> Alkali elements passivate non-radiative recombination centers at grain boundaries. </LI> <LI> The device with Na and K shows the highest efficiencies due to <I>surface</I> passivation. </LI> <LI> Passivation of both <I>external interfaces</I> and <I>internal grain boundaries</I> is crucial. </LI> </UL> </P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>
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