• Energy Research

Abstract The seaborn trade is considered as the engine for an inclusive and sustainable growth and development of the worldwide economy.For the last decades, the seaborn trade has maintained its growth trend, resulting in the increase of number of ships. This increase of shipping fleet has been accompanied with increase of fuels consumption and harmful to the environment. Various types of marine fuels have been used for the propulsion of ships depending on ship size, type of engine and trading area. However, the use of marine fuels and in particular heavy fuel oil for the propulsion of ship, generate greenhouse gases emission, waste, and in case of oil spill causes harmful to the marine ecosystem. In order to limit and reduce this environmental impact, the international maritime organization has established several stricter regulations. The implementation of these regulations has put pressure on shipowners, to operate their vessels efficiently in compliance with compulsory regulations. In this paper, we evaluate the different types of nowadays marine fuels, in term of their compliance with the compulsory environmental regulations, cost-effectiveness and their suitability to be used for the future propulsion of the autonomous ship.

Les actes de la 1re Rencontre scientifique internationale sur les oasis, ISMO 2023, se sont concentrés sur le thème ''Quel apport de la recherche scientifique pour la sauvegarde et le développement des oasis?'' et ont eu lieu à Errachidia du 20 au 22 novembre 2023. The proceedings of the 1st International Scientific Meeting on Oases, ISMO 2023, focused on the theme ''What contribution does scientific research make to the preservation and development of oases?'' and took place in Errachidia from November 20 to 22, 2023.

This paper presents a new model for learning and predicting energy consumption based on recurrent neural networks. Specifically, the Long Short Time Memory (LSTM) networks. In this model, we first calculate the moving average of the energy consumption according to a window, well-chosen in accordance with the nature of the data, in order to build an approximate output of the model. Then we use a deep neural network model that combines a multitude of different types of layers to learn how to predict energy consumption in any context. To implement this model, we used the TensorFlowJS Framework in web, mobile or embedded application context. By comparing the prediction results with those obtained by the moving average, we conclude that our model has learned perfectly well how to make good predictions and we can trust it in a different context.

The emergence of renewable energy sources with controllable loads gave the opportunity to the consumers to build their own Microgrids. However, the intermittence of renewable energy sources such as wind and photovoltaic leads to some challenges in terms of balancing generation and consumption. This paper aims to present a novel multi-agent model based intelligent control scheme to balance the home/building alternative current (AC)-direct current (DC) load demands and renewable energy sources. The new proposed scheme consists of a three-level hierarchical multi agent system based on cooperation, communication and interaction between intelligent agents to fulfill the load's requirements. Then, the proposed multi agent framework is simulated using four different nanogrids to prove its effectiveness using different temporal profiles for loads and generators. The proposed model is designed to be modular, so that it can be considered as a sample from a set of similar modules, assigned to different buildings to allow efficient energy sharing and balancing. The used approach in this concept is inspired from auto-similar systems, which is well suited and easy to implement on multi agent systems. A co-simulation in MATLAB and JAVA/JADE platforms has been performed regarding the production-consumption of the 24 hours baseline period.