A deep learning-based approach for predicting COVID-19 diagnosis.

This paper focuses on forecasting the total count of confirmed COVID-19 cases in Saudi Arabia through a range of methodologies, including ARIMA, mathematical modeling, and deep learning network (DQN) techniques. Its primary aim is to anticipate the verified COVID-19 cases in Saudi Arabia, aiding in decision-making for life-saving interventions by enhancing awareness of COVID-19 infection. Mathematical modeling and ARIMA are employed for their efficacy in forecasting, while DQN approaches, particularly through comparative analysis, are utilized for prediction. This comparative analysis evaluates the predictive capacities of ARIMA, mathematical modeling, and DQN techniques, aiming to pinpoint the most reliable method for forecasting positive COVID-19 cases. The modeling encompasses COVID-19 cases in Saudi Arabia, the United Kingdom (UK), and Tunisia (TU) spanning from 2020 to 2021. Predicting the number of individuals likely to test positive for COVID-19 poses a challenge, requiring adherence to fundamental assumptions in mathematical and ARIMA projections. The proposed methodology was implemented on a local server. The DQN algorithm formulates a reward function to uphold target functional performance while balancing training and testing periods. The findings indicate that DQN technology surpasses conventional approaches in efficiency and accuracy for predictions.

Multi-Agent Systems for Resource Allocation and Scheduling in a Smart Grid.

Multi-Agent Systems (MAS) have been seen as an attractive area of research for civil engineering professionals to subdivide complex issues. Based on the assignment's history, nearby agents, and objective, the agent intended to take the appropriate action to complete the task. MAS models complex systems, smart grids, and computer networks. MAS has problems with agent coordination, security, and work distribution despite its use. This paper reviews MAS definitions, attributes, applications, issues, and communications. For this reason, MASs have drawn interest from computer science and civil engineering experts to solve complex difficulties by subdividing them into smaller assignments. Agents have individual responsibilities. Each agent selects the best action based on its activity history, interactions with neighbors, and purpose. MAS uses the modeling of complex systems, smart grids, and computer networks. Despite their extensive use, MAS still confronts agent coordination, security, and work distribution challenges. This study examines MAS's definitions, characteristics, applications, issues, communications, and evaluation, as well as the classification of MAS applications and difficulties, plus research references. This paper should be a helpful resource for MAS researchers and practitioners. MAS in controlling smart grids, including energy management, energy marketing, pricing, energy scheduling, reliability, network security, fault handling capability, agent-to-agent communication, SG-electrical cars, SG-building energy systems, and soft grids, have been examined. More than 100 MAS-based smart grid control publications have been reviewed, categorized, and compiled.

Model for Managing the Integration of a Vehicle-to-Home Unit into an Intelligent Home Energy Management System.

Integration of vehicle-to-home (V2H) centralized photovoltaic (HCPV) systems is a requested and potentially fruitful research topic for both industry and academia. Renewable energy sources, such as wind turbines and solar photovoltaic panels, alleviate energy deficits. Furthermore, energy storage technologies, such as batteries, thermal, and electric vehicles, are indispensable. Consequently, in this article, we examine the impact of solar photovoltaic (SPV), microgrid (MG) storage, and an electric vehicle (EV) on maximum sun radiation hours. As a result, an HCPV scheduling algorithm is developed and applied to maximize energy sustainability in a smart home (SH). The suggested algorithm can manage energy demand between the MG and SPV systems, as well as the EV as a mobile storage system. The model is based on several limitations to meet households' electrical needs during sunny and cloudy weather. A multi-agent system (MAS) is undertaken to ensure proper system operation and meet the power requirements of various devices. An experimental database for weather and appliances is deployed to evaluate and control energy consumption and production cost parameters. The obtained results illustrate the benefits of V2H technology as a prospective unit storage solution.

Energy Internet Opportunities in Distributed Peer-to-Peer Energy Trading Reveal by Blockchain for Future Smart Grid 2.0.

The Energy Internet (EI) and Smart Grid 2.0 (SG 2.0) concepts are potential challenges in industry and research. The purpose of SG 2.0 and EI is to automate innovative power grid operations. To move from Distribution Network Operators (DSO) to consumer-centric distributed power grid management, the blockchain and smart contracts are applicable. Blockchain technology and integrated SGs will present challenges, limiting the deployment of Distributed Energy Resources (DERs). This review looks at the decentralization of the Smart Grid 2.0 using blockchain technology. Energy trading has increased due to access to distributed energy sources and electricity producers who can financially export surplus fuels. The energy trading system successfully combines energy from multiple sources to ensure consistent and optimal use of available resources and better facilities for energy users. Peer-to-peer (P2P) energy trading is a common field of study that presents some administrative and technical difficulties. This article provides a general overview of P2P energy exchange. It discusses how blockchain can improve transparency and overall performance, including the degree of decentralization, scalability, and device reliability. The research is extended to examine unresolved issues and potential directions for P2P blockchain-based energy sharing in the future. In fact, this paper also demonstrates the importance of blockchain in future smart grid activities and its blockchain-based applications. The study also briefly examines the issues associated with blockchain integration, ensuring the decentralized, secure and scalable operation of autonomous electric grids in the future.