The demand for electricity is increased due to the development of the industry, the electrification of transport, the rise of household demand, and the increase in demand for digitally connected devices and air conditioning systems. For that, solutions and actions should be developed for greater consumers of electricity. For instance, MG (Micro-grid) buildings are one of the main consumers of electricity, and if they are correctly constructed, controlled, and operated, a significant energy saving can be attained. As a solution, hybrid RES (renewable energy source) systems are proposed, offering the possibility for simple consumers to be producers of electricity. This hybrid system contains different renewable generators connected to energy storage systems, making it possible to locally produce a part of energy in order to minimize the consumption from the utility grid. This work gives a concise state-of-the-art overview of the main control approaches for energy management in MG systems. Principally, this study is carried out in order to define the suitable control approach for MGs for energy management in buildings. A classification of approaches is also given in order to shed more light on the need for predictive control for energy management in MGs.

Predicting energy consumption in Smart Buildings (SB), and scheduling it, is crucial for deploying Energy-efficient Management Systems. Most important, this constitutes a key aspect in the promising Smart Grids technology, whereby loads need to be predicted and scheduled in real-time to cope for the strongly coupled variance between energy demand and cost. Several approaches and models have been adopted for energy consumption prediction and scheduling. In this paper, we investigated available models and opted for machine learning. Namely, we use Artificial Neural Networks (ANN) along with Genetic Algorithms. We deployed our models in a real-world SB testbed. We used CompactRIO for ANN implementation. The proposed models are trained and validated using real-world data collected from a PV installation along with SB electrical appliances. Though our model exhibited a modest prediction accuracy, which is due to the small size of the data set, we strongly recommend our model as a blue-print for researchers willing to deploy real-world SB testbeds and investigate machine learning as a promising venue for energy consumption prediction and scheduling.

The integration of renewable energy sources (RES) was amplified, during the past decades, in order to tackle the challenges related to energy demands and CO₂ increases. Recently, many initiatives have been taken by promoting the deployment and the usage of micro-grids (MG) in buildings, as decentralized systems, for energy production. However, the variable nature of RESs and the limited size of energy storage systems require the deployment of adaptive control strategies for efficient energy balance. In this paper, a generalized predictive control (GPC) strategy is introduced for energy management (EM) in MG systems. Its main objective is to efficiently connect the electricity generators and consumers in order to predict the most suitable actions for energy flow management. In fact, based on energy production and consumption profiles as well as the availability of energy storage systems, the proposed EM will be able to select the best suitable energy source for supplying the building’s loads. It will efficiently manage the usage of energy storage and the utility grid while maximizing RESs power generation. Simulations have been conducted, using real-sitting scenarios, and results are presented to validate the proposed predictive control approach by showing its effectiveness for MG systems control.

The COVID-19 flare-up in late 2019 caused a worldwide medical crisis. In a littlemore than a quarter of a year, the quantity of COVID new cases has risen to in excessof 1,000,000 around the world.The fast transmission of the infection prompts newcases to be accounted for all around the world constantly. At the same time, mortalityrate and contaminations keep on rising rapidly. Therefore, the COVID-19 pandemichas led to the implementation of lockdowns and social distancing rules that negativelyimpact global economies. It has upturned many challenges which are mentioned inthis paper,we proposed a solution to these major problems, blockchain technology canplay a vital role in closing maximum escape clauses in the present system to improvevisibility and traceability and to provide a clear far vision for the stakeholders presentin pharmaceutical industry.

This paper studies and evaluate Scalability of Voice over IP (VOIP) on various MPLS Tunneling technologies. Several works have been carried out to evaluate the performances of VOIP in different IP and MPLS architectures. According to our research, no scientific work has been done analyzing scalability by increasing the load of VOIP packets in different MPLS tunneling technologies. This was a motivation for us to propose a scalability study of the VOIP technology in the various scenarios (IP, MPLS, MPLS VPN and MPLS VPN protected by IPsec). The study was conducted on 64 different scenarios by changing MPLS tunneling technology and rising the packet load to the order of 2 ^ n ranging from 64 to 475000. The obtained results showed that the IP network is affected by a high latency and a bad MOS score. While MPLS remains scalable even in high load scenario (475000 bytes). IPsec on MPLS VPN isn't a good choice to add additional security level, from results it's shown clearly that from scenario of 46875 bytes on load, VOIP become unusable. This study carried out under OPNET Modeler 14.5, the evaluation criteria are jitter, latency, loss rate and MOS score.