• Advances in Energy Research
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• 제6권2호
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• pp.131-143
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• 2019

Global Solar Radiation (GSR) is the key element for performance estimation of any Solar Power Plant (SPP). Its forecasting may help in estimation of power production from a SPP well in advance, and may also render help in optimal use of this power. Seasonal Auto-Regressive Moving Average (SARMA) and Artificial Neural Network (ANN) models are combined in order to develop a hybrid model (SARMA-ANN) conceiving the characteristics of both linear and non-linear prediction models. This developed model has been used for prediction of GSR at Gorakhpur, situated in the northern region of India. The proposed model is beneficial for the univariate forecasting. Along with this model, we have also used Auto-Regressive Moving Average (ARMA), SARMA, ANN based models for 1 - 6 day-ahead forecasting of GSR on hourly basis. It has been found that the proposed model presents least RMSE (Root Mean Square Error) and produces best forecasting results among all the models considered in the present study. As an application, the comparison between the forecasted one and the energy produced by the grid connected PV plant installed on the parking stands of the University shows the superiority of the proposed model.

• 산업경영시스템학회지
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• 제39권1호
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• pp.25-30
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• 2016

It is critical to forecast the maximum daily and monthly demand for power with as little error as possible for our industry and national economy. In general, long-term forecasting of power demand has been studied from both the consumer's perspective and an econometrics model in the form of a generalized linear model with predictors. Time series techniques are used for short-term forecasting with no predictors as predictors must be predicted prior to forecasting response variables and containing estimation errors during this process is inevitable. In previous researches, seasonal exponential smoothing method, SARMA (Seasonal Auto Regressive Moving Average) with consideration to weekly pattern Neuron-Fuzzy model, SVR (Support Vector Regression) model with predictors explored through machine learning, and K-means clustering technique in the various approaches have been applied to short-term power supply forecasting. In this paper, SARMA and intervention model are fitted to forecast the maximum power load daily, weekly, and monthly by using the empirical data from 2011 through 2013. $ARMA(2,\;1,\;2)(1,\;1,\;1)_7$ and $ARMA(0,\;1,\;1)(1,\;1,\;0)_{12}$ are fitted respectively to the daily and monthly power demand, but the weekly power demand is not fitted by AREA because of unit root series. In our fitted intervention model, the factors of long holidays, summer and winter are significant in the form of indicator function. The SARMA with MAPE (Mean Absolute Percentage Error) of 2.45% and intervention model with MAPE of 2.44% are more efficient than the present seasonal exponential smoothing with MAPE of about 4%. Although the dynamic repression model with the predictors of humidity, temperature, and seasonal dummies was applied to foretaste the daily power demand, it lead to a high MAPE of 3.5% even though it has estimation error of predictors.

• The Journal of Asian Finance, Economics and Business
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• 제8권12호
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• pp.363-370
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• 2021

This study aims to revisit the issues and misconceptions about financial inclusion (FI) indices. For indices construction, this study uses two approaches: one approach following the methodology of Sarma (2008) which is based on UNDP methodology, while the other is the Dynamic Factor Model (DFM)-based index of Stock and Watson (2002) and Rehman et al. (2021). The data of 18 economies of Asia from 1997 till 2017 is used for indices construction and analysis. The authors constructed macro and micro-level financial inclusion indices based on the different types of financial inclusion indicators. Second, the authors have critically evaluated two different approaches, and the results show that Sarma (2008)-based index show financial inclusion's level, while DFM-based index reveal fluctuation in the current year's financial inclusion level due to the prior variations. For measuring the level of financial inclusion, the Sarma (2008) index is effective, while for forecasting the level of financial inclusion, the DFM approach is more appropriate. Furthermore, the micro and macro aspects of financial inclusion should be reflected in separate indices for better understanding and in-depth insights.

• East Asian mathematical journal
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• 제16권1호
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• pp.1-7
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• 2000

• Journal of applied mathematics & informatics
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• 제32권5_6호
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• pp.807-816
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• 2014

The asymptotic behavior of solutions of Lyapunov type matrix Volterra integro differential equation, in which the coefficient matrices are not stable, is studied by the method of reduction.

• 대한수학회보
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• 제50권2호
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• pp.697-704
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• 2013

The function $g{\mapsto}{\zeta}^k_G(g)$ which counts the number of solutions of $x^k=g$ in a finite group G, is not necessarily a character of G. We study this function for the case of dihedral groups and generalized quaternion groups.

• 한국지능시스템학회논문지
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• 제17권1호
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• pp.143-147
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• 2007

With a new ordinary norm as an analogy of Krishna and Sarma[5] and Bag and Samanta[1], we will characterize the notions of the convergence of the sequences of fuzzy points, the fuzzy, ${\alpha}$-Cauchy sequence and fuzzy completeness.

• Kyungpook Mathematical Journal
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• 제52권2호
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• pp.189-200
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• 2012

In this article we introduce the class of I-convergent double sequences of fuzzy real numbers. We have studied different properties like solidness, symmetricity, monotone, sequence algebra etc. We prove that the class of I-convergent double sequences of fuzzy real numbers is a complete metric spaces.

• Kyungpook Mathematical Journal
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• 제48권4호
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• pp.613-622
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• 2008

In this article we introduce some difference sequence spaces defined by Orlicz function and study different properties of these spaces like completeness, solidity, symmetricity etc. We establish some inclusion results among them.

• Kyungpook Mathematical Journal
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• 제51권4호
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• pp.429-433
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• 2011

In this article we introduce the notion of b-locally open sets, $bLO^*$ sets, $bLO^{**}$ sets in bitopological spaces and obtain several characterizations and some properties of these sets.