Institute for International Energy Studies IIES

Tehrān, Iran

Institute for International Energy Studies IIES

Tehrān, Iran
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Huang X.,Bournemouth University | Hassani H.,Institute for International Energy Studies IIES | Ghodsi M.,Institute for International Energy Studies IIES | Mukherjee Z.,Simmons College | Gupta R.,University of Pretoria
Physica A: Statistical Mechanics and its Applications | Year: 2017

Various scientific studies have investigated the causal link between solar activity (SS) and the earth's temperature (GT). Results from literature indicate that both the detected structural breaks and existing trend have significant effects on the causality detection outcomes. In this paper, we make a contribution to this literature by evaluating and comparing seven trend extraction methods covering various aspects of trend extraction studies to date. In addition, we extend previous work by using Convergent Cross Mapping (CCM) - an advanced non-parametric causality detection technique to provide evidence on the effect of existing trend in global temperature on the causality detection outcome. This paper illustrates the use of a method to find the most reliable trend extraction approach for data preprocessing, as well as provides detailed analyses of the causality detection of each component by this approach to achieve a better understanding of the causal link between SS and GT. Furthermore, the corresponding CCM results indicate increasing significance of causal effect from SS to GT since 1880 to recent years, which provide solid evidences that may contribute on explaining the escalating global tendency of warming up recent decades. © 2016 Elsevier B.V.

Ghodsi M.,Bournemouth University | Alharbi N.,Bournemouth University | Alharbi N.,King Saud bin Abdulaziz University for Health Sciences | Hassani H.,Institute for International Energy Studies IIES
Fluctuation and Noise Letters | Year: 2015

The empirical distribution of the eigenvalues of the matrix HHT divided by its trace is considered, where H is a Hankel random matrix. The normal distribution with different parameters are considered and the effect of scale and shape parameters are evaluated. The correlation among eigenvalues are assessed using parametric and non-parametric association criteria. © 2015 World Scientific Publishing Company.

Jafari H.H.,Institute for International Energy Studies IIES | Dehkordi B.G.,Tarbiat Modares University
Journal of Fluids Engineering, Transactions of the ASME | Year: 2013

Prediction of fluid-elastic instability onset is a great matter of importance in designing cross-flow heat exchangers from the perspective of vibration. In the present paper, the threshold of fluid-elastic instability has been numerically predicted by the simulation of incompressible, unsteady, and turbulent cross flow through a tube bundle in a normal triangular arrangement. In the tube bundle under study, there were single or multiple flexible cylinders surrounded by rigid tubes. A finite volume solver based on a Cartesian-staggered grid was implemented. In addition, the ghost-cell method in conjunction with the great-source-term technique was employed in order to directly enforce the no-slip condition on the cylinders' boundaries. Interactions between the fluid and the structures were considered in a fully coupled manner by means of intermittence solution of the flow field and structural equations of motion in each time step of the numerical modeling algorithm. The accuracy of the solver was validated by simulation of the flow over both a rigid and a flexible circular cylinder. The results were in good agreement with the experiments reported in the literatures. Eventually, the flow through seven different flexible tube bundles was simulated. The fluid-elastic instability was predicted and analyzed by presenting the structural responses, trajectory of flexible cylinders, and critical reduced velocities. © 2013 by ASME.

Hassani H.,Bournemouth University | Hassani H.,Institute for International Energy Studies IIES | Leonenko N.,University of Cardiff | Patterson K.,University of Reading
Physica A: Statistical Mechanics and its Applications | Year: 2012

The detection of long-range dependence in time series analysis is an important task to which this paper contributes by showing that whilst the theoretical definition of a long-memory (or long-range dependent) process is based on the autocorrelation function, it is not possible for long memory to be identified using the sum of the sample autocorrelations, as usually defined. The reason for this is that the sample sum is a predetermined constant for any stationary time series; a result that is independent of the sample size. Diagnostic or estimation procedures, such as those in the frequency domain, that embed this sum are equally open to this criticism. We develop this result in the context of long memory, extending it to the implications for the spectral density function and the variance of partial sums of a stationary stochastic process. The results are further extended to higher order sample autocorrelations and the bispectral density. The corresponding result is that the sum of the third order sample (auto) bicorrelations at lags h,k<1, is also a predetermined constant, different from that in the second order case, for any stationary time series of arbitrary length. © 2012 Elsevier B.V. All rights reserved.

Shafiei E.,Institute for International Energy Studies IIES
Energy Systems | Year: 2011

In this study, a comprehensive analytical tool for assessment of energy technologies and R&D resource allocation is developed taking into account the specific conditions of technology follower countries. The analytical instrument includes two interlinked models: technology assessment and optimal R&D resource allocation. Energy technology assessment and prioritization of new energy technologies are provided by a dynamic systems engineering optimization model of energy supply system. Then based on the economic and environmental impacts of technologies, optimal allocation of R&D resources for new technologies is estimated with the help of the R&D resource allocation model. This model is formulated as an optimal control problem and it considers the R&D activities and knowledge stock as the main control and state variables. This model maximizes the total net present value of resulting R&D benefits taking into account the dynamics of technological progress, knowledge and experience spillovers from advanced regions, technology adoption and R&D constraints. In this model, the role of degree of spillover, follower country's innovation capacity, knowledge complexity and absorption capacity is highlighted in the modeling of knowledge accumulation in follower countries. In this paper the mathematical formulation of the R&D resource allocation model and its linkage with the energy supply model will be described. Thereafter, the application of the interlinked models will be explained through a test case and the applicability of the energy R&D resource allocation model and its contribution to the profession of energy modeling will be concluded. © The Author(s) 2011.

Hassani H.,Bournemouth University | Hassani H.,Institute for International Energy Studies IIES | Mahmoudvand R.,Bu - Ali Sina University | Omer H.N.,Salahaddin University Erbil | Silva E.S.,Bournemouth University
Fluctuation and Noise Letters | Year: 2014

The aim of this paper is to study the effect of outliers on different parts of singular spectrum analysis (SSA) from both theoretical and practical points of view. The rank of the trajectory matrix, the magnitude of eigenvalues, reconstruction, and forecasting results are evaluated using simulated and real data sets. The performance of both recurrent and vector forecasting procedures are assessed in the presence of outliers. We find that the existence of outliers affect the rank of the matrix and increases the linear recurrent dimensions whilst also having a significant impact on SSA reconstruction and forecasting processes. There is also evidence to suggest that in the presence of outliers, the vector SSA forecasts are more robust in comparison to the recurrent SSA forecasts. These results indicate that the identification and removal of the outliers are mandatory to achieve optimal SSA decomposition and forecasting results. © World Scientific Publishing Company.

Hassani H.,Institute for International Energy Studies IIES | Huang X.,Bournemouth University | Gupta R.,University of Pretoria | Ghodsi M.,Institute for International Energy Studies IIES
Physica A: Statistical Mechanics and its Applications | Year: 2016

In a recent paper, Gupta et al., (2015), analyzed whether sunspot numbers cause global temperatures based on monthly data covering the period 1880:1-2013:9. The authors find that standard time domain Granger causality test fails to reject the null hypothesis that sunspot numbers do not cause global temperatures for both full and sub-samples, namely 1880:1-1936:2, 1936:3-1986:11 and 1986:12-2013:9 (identified based on tests of structural breaks). However, frequency domain causality test detects predictability for the full-sample at short (2-2.6 months) cycle lengths, but not the sub-samples. But since, full-sample causality cannot be relied upon due to structural breaks, Gupta et al., (2015) conclude that the evidence of causality running from sunspot numbers to global temperatures is weak and inconclusive. Given the importance of the issue of global warming, our current paper aims to revisit this issue of whether sunspot numbers cause global temperatures, using the same data set and sub-samples used by Gupta et al., (2015), based on an nonparametric Singular Spectrum Analysis (SSA)-based causality test. Based on this test, we however, show that sunspot numbers have predictive ability for global temperatures for the three sub-samples, over and above the full-sample. Thus, generally speaking, our non-parametric SSA-based causality test outperformed both time domain and frequency domain causality tests and highlighted that sunspot numbers have always been important in predicting global temperatures. © 2016 Elsevier B.V.

Hassani H.,Bournemouth University | Hassani H.,Institute For International Energy Studies Iies | Mahmoudvand R.,Shahid Beheshti University | Zokaei M.,Shahid Beheshti University | Ghodsi M.,University of Cardiff
Fluctuation and Noise Letters | Year: 2012

The optimal value of the window length in singular spectrum analysis (SSA) is considered with respect to the concept of separability between signal and noise component, from the theoretical and practical perspective. The theoretical results confirm that for a wide class of time series of length N, the suitable value of this parameter is median {1, ..., N}. The results of both simulated and real data verify the effectiveness of the theoretical results. The theoretical results obtained here coincide with those obtained previously from the empirical point of view. © 2012 World Scientific Publishing Company.

Majidpour M.,Institute For International Energy Studies Iies
Energy Policy | Year: 2012

This paper for the first time systematically examines the heavy duty gas turbine (HDGT) industry in the context of developing countries. It provides a comparative analysis of the HDGT industries in Iran, India and China. It contrasts their national strategies, the historical development of their technological capabilities, the similarities and differences in approach, the varying evolutionary paths and policy drivers and the reasons for their differing outcomes. This paper argues that a high level of state involvement is a prominent feature of HDGT industries in developing countries. It also argues that the development and evolution of the HDGT industries in these countries is closely interrelated with the countries' national energy policies. It clarifies why such an advanced and sophisticated industry is a strategic choice in one country, while it is seen as an inferior choice in another. © 2011 Elsevier Ltd.

Alharbi N.,Bournemouth University | Alharbi N.,King Saud University | Hassani H.,Institute for International Energy Studies IIES
Journal of the Franklin Institute | Year: 2016

Singular spectrum analysis (SSA) is a reliable technique for separating an arbitrary signal from a noisy time series (signal+noise). The SSA technique is based upon two main selections: window length, L, and the number of the eigenvalues, r. These values play an important role for the reconstruction stage. In this paper, we introduce a new approach for selecting the optimal value of r, which is based on the distribution of the eigenvalues of a scaled Hankel matrix. The proposed approach is applied to a number of simulated and real data with different structures. The results indicate that the proposed approach has potential in selecting the value of r for signal extraction. © 2015 The Franklin Institute.

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