Mixed models for short-run forecasting of electricity prices: Application for the Spanish market

Short-run forecasting of electricity prices has become necessary for power generation unit schedule, since it is the basis of every profit maximization strategy. In this article a new and very easy method to compute accurate forecasts for electricity prices using mixed models is proposed. The main idea is to develop an efficient tool for one-step-ahead forecasting in the future, combining several prediction methods for which forecasting performance has been checked and compared for a span of several years. Also as a novelty, the 24 hourly time series has been modelled separately, instead of the complete time series of the prices. This allows one to take advantage of the homogeneity of these 24 time series. The purpose of this paper is to select the model that leads to smaller prediction errors and to obtain the appropriate length of time to use for forecasting. These results have been obtained by means of a computational experiment. A mixed model which combines the advantages of the two new models discussed is proposed. Some numerical results for the Spanish market are shown, but this new methodology can be applied to other electricity markets as well

An approach to operational modal analysis using the expectation maximization algorithm

This paper presents the Expectation Maximization algorithm (EM) applied to operational modal analysis of structures. The EM algorithm is a general-purpose method for maximum likelihood estimation (MLE) that in this work is used to estimate state space models. As it is well known, the MLE enjoys some optimal properties from a statistical point of view, which make it very attractive in practice. However, the EM algorithm has two main drawbacks: its slow convergence and the dependence of the solution on the initial values used. This paper proposes two different strategies to choose initial values for the EM algorithm when used for operational modal analysis: to begin with the parameters estimated by Stochastic Subspace Identification method (SSI) and to start using random points. The effectiveness of the proposed identification method has been evaluated through numerical simulation and measured vibration data in the context of a benchmark problem. Modal parameters (natural frequencies, damping ratios and mode shapes) of the benchmark structure have been estimated using SSI and the EM algorithm. On the whole, the results show that the application of the EM algorithm starting from the solution given by SSI is very useful to identify the vibration modes of a structure, discarding the spurious modes that appear in high order models and discovering other hidden modes. Similar results are obtained using random starting values, although this strategy allows us to analyze the solution of several starting points what overcome the dependence on the initial values used.

Signal to noise ratio maximization in quiet zone acquisitions for range assessment at sub-millimeter wavelengths

This paper proposes a quiet zone probing approach which deals with low dynamic range quiet zone acquisitions. Lack of dynamic range is a feature of millimeter and sub-millimeter wavelength technologies. It is consequence of the gradually smaller power generated by the instrumentation, that follows a f^α law with frequency, being α≥1 variable depending on the signal source’s technology. The proposed approach is based on an optimal data reduction scenario which redounds in a maximum signal to noise ratio increase for the signal pattern, with minimum information losses. After theoretical formulation, practical applications of the technique are proposed.

Maximum Likelihood Estimation of Modal Parameters in Structures Using the Expectation Maximization Algorithm

This paper presents a time-domain stochastic system identification method based on maximum likelihood estimation (MLE) with the expectation maximization (EM) algorithm. The effectiveness of this structural identification method is evaluated through numerical simulation in the context of the ASCE benchmark problem on structural health monitoring. The benchmark structure is a four-story, two-bay by two-bay steel-frame scale model structure built in the Earthquake Engineering Research Laboratory at the University of British Columbia, Canada. This paper focuses on Phase I of the analytical benchmark studies. A MATLAB-based finite element analysis code obtained from the IASC-ASCE SHM Task Group web site is used to calculate the dynamic response of the prototype structure. A number of 100 simulations have been made using this MATLAB-based finite element analysis code in order to evaluate the proposed identification method. There are several techniques to realize system identification. In this work, stochastic subspace identification (SSI)method has been used for comparison. SSI identification method is a well known method and computes accurate estimates of the modal parameters. The principles of the SSI identification method has been introduced in the paper and next the proposed MLE with EM algorithm has been explained in detail. The advantages of the proposed structural identification method can be summarized as follows: (i) the method is based on maximum likelihood, that implies minimum variance estimates; (ii) EM is a computational simpler estimation procedure than other optimization algorithms; (iii) estimate more parameters than SSI, and these estimates are accurate. On the contrary, the main disadvantages of the method are: (i) EM algorithm is an iterative procedure and it consumes time until convergence is reached; and (ii) this method needs starting values for the parameters. Modal parameters (eigenfrequencies, damping ratios and mode shapes) of the benchmark structure have been estimated using both the SSI method and the proposed MLE + EM method. The numerical results show that the proposed method identifies eigenfrequencies, damping ratios and mode shapes reasonably well even in the presence of 10% measurement noises. These modal parameters are more accurate than the SSI estimated modal parameters.

International for-profit investiments in microfinance institutions equity

In recent years international investors are increasing the focus on the social consequences of their investments along with its financial returns. The microfinance sector, considered as an asset class is a relatively young concept but the microfinance industry is experiencing a tremendous growth and has a high potential for the future. Today most social responsible investments in microfinance are performed through loans or fixed income structured finance vehicles. The possibilities to invest in the equity tranche of the industry are still scarce since the number of listed microfinance institutions is reduced and the private equity investments are limited and difficult to reach for the majority of investors. In this document we present a study on the characteristics of the MFIs and we try to shed some light on this subsector of the equity assets universe that may become important in the coming future. Keywords: Microfinance institutions, Micro-credits, Financial Institutions, Equity; Stock Exchange

Lifetime maximization for beamforming applications in wireless sensor networks

Energy efficiency is a major design issue in the context of Wireless Sensor Networks (WSN). If data is to be sent to a far-away base station, collaborative beamforming by the sensors may help to dis- tribute the load among the nodes and reduce fast battery depletion. However, collaborative beamforming techniques are far from opti- mality and in many cases may be wasting more power than required. In this contribution we consider the issue of energy efficiency in beamforming applications. Using a convex optimization framework, we propose the design of a virtual beamformer that maximizes the network's lifetime while satisfying a pre-specified Quality of Service (QoS) requirement. A distributed consensus-based algorithm for the computation of the optimal beamformer is also provided

Operational Modal Analysis using Expectation Maximization Algorithm

This paper presents a time-domain stochastic system identification method based on Maximum Likelihood Estimation and the Expectation Maximization algorithm. The effectiveness of this structural identification method is evaluated through numerical simulation in the context of the ASCE benchmark problem on structural health monitoring. Modal parameters (eigenfrequencies, damping ratios and mode shapes) of the benchmark structure have been estimated applying the proposed identification method to a set of 100 simulated cases. The numerical results show that the proposed method estimates all the modal parameters reasonably well in the presence of 30% measurement noise even. Finally, advantages and disadvantages of the method have been discussed.

International For-Profit Investments in Microfinance Institutions Equity

Purpose: The purpose of this document is to review the funding options for Microfinance Institutions (MFIs), define the size of the holdings of international investors in MFI equity and in particular the MFIs listed in stock exchanges, analyze the characteristics of these subset of the financial world and study the stock exchange evolution of some listed MFIs amid the financial crisis. Design/methodology/approach: Since academic literature on listed MFI equity is virtually inexistent, most of the information has been obtained from the World Bank, annual accounts of the listed MFIs, stock exchanges and from equity research documents. Findings and Originality/value: Microfinance Institutions share several common characteristics that make them a resilient business and the few MFIs that are listed in stock exchanges seem to have performed better in the financial crisis. Microfinance can be considered as one of the new frontiers of the expansion of the global banking industry. Practical implications: Presently, international for-profit investors have very few ways of investing in microfinance equity. Most of the equity of the MFI equity is funded locally or thanks to the local public sector. The stock exchange listing of the MFIs should drive MFIs towards a more professional management, more transparency and better governance. Social implications: Microfinance Institutions provide credit to microenterprises in poor countries that have no other alternative sources of external capital to expand its activity. If global investors could easily invest in the listed equity of the MFIs these institutions would expand its lending books and would improve its governance, part of the population living in poor areas or with lower income could ameliorate its standard of living. Originality/value: The number of Microfinance Institutions that are professionally run like commercial banks is still scarce and even more scarce are the MFI listed in public stock exchanges. Therefore the published literature on the characteristics and performance of the listed equity of the Microfinance Institutions is extremely reduced. But microfinance assets are rapidly growing and MFIs will need to list their equity in stock exchanges to sustain this expansion.