Empowering Distributed Solar PV Energy For Malaysian Rural Housing: Towards Energy Security And Equitability Of Rural Communities

This paper illustrates on how Malaysia’s development landscapes has been powered by cheap oil and gas making it dependent and addicted on using large amounts of fossil fuels. As a country that is primarily depended on fossil fuels for generating power supply, Malaysia needs to cogitate of long-term energy security due to fossil fuel depletion and peak oil issues. Loss of these resources could leadto thereduction of power generation capacitywhich will threaten the stability of the electricity supply in Malaysia. This could potentially influence in an increase in electricity costs which lead to a phase of power scarcity and load shedding for the country. With the risk of interrupted power supplies, rural households, especially those of low-income groups are particularly vulnerable to the post-effects of a power outage and an inequitable distribution to the people. Distributed generation of electricity by solar PVs diminishes the vulnerability of these households and can also offer an income to them by feeding the power supply to the national grid through Feed-in Tariff scheme. At the moment, the deployment of solar PV installations is still in the introductory stage in Malaysia, where roof-mounted PV panels are only available to commercial and urban residential buildings. This is due to the lack of a suitable renewable energy policy for rural householdsandthe high cost of the solar PV technology. This paper will put forward an analysis for incorporating solar photovoltaic on roofs of rural houses by identifying the energy consumption of these households and the extent to which PVs can alleviate electricity insecurity. The results present significant potential for distributed PV power generation in rural areas in Malaysia which shown a considerable amount of electricity needed to be harvested from roof-mounted solar PV for rural people in Malaysi

An Inexact Successive Quadratic Approximation Method for Convex L-1 Regularized Optimization

We study a Newton-like method for the minimization of an objective function that is the sum of a smooth convex function and an l-1 regularization term. This method, which is sometimes referred to in the literature as a proximal Newton method, computes a step by minimizing a piecewise quadratic model of the objective function. In order to make this approach efficient in practice, it is imperative to perform this inner minimization inexactly. In this paper, we give inexactness conditions that guarantee global convergence and that can be used to control the local rate of convergence of the iteration. Our inexactness conditions are based on a semi-smooth function that represents a (continuous) measure of the optimality conditions of the problem, and that embodies the soft-thresholding iteration. We give careful consideration to the algorithm employed for the inner minimization, and report numerical results on two test sets originating in machine learning

Computer magnetic tape rehabilitation study

Study determines the most efficient method for magnetic tapes rehabilitation and storage for reuse. Investigated were the physical changes taking place in the tape during the rehabilitation process, measure of quality of the processed tapes, and the level of quality required to achieve sufficient yield

Dynamic sampling schemes for optimal noise learning under multiple nonsmooth constraints

We consider the bilevel optimisation approach proposed by De Los Reyes, Sch\"onlieb (2013) for learning the optimal parameters in a Total Variation (TV) denoising model featuring for multiple noise distributions. In applications, the use of databases (dictionaries) allows an accurate estimation of the parameters, but reflects in high computational costs due to the size of the databases and to the nonsmooth nature of the PDE constraints. To overcome this computational barrier we propose an optimisation algorithm that by sampling dynamically from the set of constraints and using a quasi-Newton method, solves the problem accurately and in an efficient way