GREEN 2017 - The Second International Conference on Green Communications, Computing and Technologies

—This paper reports the results of a study on thermophysical properties of two varieties of local date palm wood, called petiole, namely Boufeggous and Hafsa from Tinghir oasis, southern Morocco. The goal is to use this natural material as insulation for a solar furnace to reduce heat loss. Experimental measurements of thermo-physical properties, according to the orientation of the fibers at ambient temperature and atmospheric pressure, have been conducted and analyzed. Furthermore, a scanning microscopy (SEM) analysis of the samples was investigated to characterize their microstructure. Preliminary results deducted from this study were compared with other insulation materials in literature in order to evaluate the interest of these kind of materials for solar cooking application. Abstract —This paper explores the extent to which senior man- agers using standard management models as tools for developing corporate strategy, structures and culture are likely to be encouraged to adopt green IT. A range of standard management models are considered: strategic, tactical and operational. Analysis reveals that many standard models, in particular older ones that rely heavily on numbers and take a narrow view of corporate responsibility, are not favourable to the adoption of green IT. Accordingly, managers need to avoid excessive reliance on such models and should consider using models which take account of softer issues, in particular those models which address sustainability directly. There is a need for the development of new management models, which more explicitly integrate traditional bottom line considerations with the wider ethical responsibilities of companies, including sustainability. Abstract — This paper presents an optimal energy conservation measure (ECM) decision method, which is to find optimal ECMs to minimize the initial implementation cost of ECMs for satisfying the target of greenhouse gas (GHG) emission reduction. The method estimates energy savings by implementing ECMs and calculates GHG emission reduction considering both energy savings and carbon dioxide emission factors of each energy source. Then, it decides on the optimal ECMs whose initial implementation cost is minimal while meeting the target of GHG emission reduction. This paper modifies the knapsack algorithm to decide an optimal ECM combination to satisfy the target of GHG emission reduction and presents the simulation results including the optimal ECM list, the amount of GHG emission reduction and implementation cost to verify the optimal ECMs. Abstract —Many important distribution network applications, such as load balancing, state-estimation, and network recon-ﬁguration, depend on accurate phase connectivity information. The existing data-driven phase identiﬁcation algorithms have a few drawbacks. First, the existing algorithms require the number of phase connections as an input. Second, they can not provide accurate results when there is a mix of phase-to- neutral and phase-to-phase connected smart meters, or when the distribution circuit is less unbalanced. This paper develops an advanced metering infrastructure (AMI) data driven phase identiﬁcation algorithm that addresses the drawbacks of the existing solutions in two ways. First, it leverages a nonlinear dimensionality reduction technique to extract key features from the voltage time series. Second, a constraint-driven hybrid clustering (CHC) algorithm is developed to dynamically create smart meter clusters with arbitrary shapes. The ﬁeld validation results show that the proposed algorithm outperforms the existing ones. The improvement in the phase identiﬁcation accuracy is more pronounced for distribution feeders that are less unbalanced. In addition, this paper discovers that more granular voltage time series leads to higher phase identiﬁcation accuracy.