This paper presents a standardized, policy-driven framework for Demand-Side Management (DSM) integration to support Qatar’s smart grid transformation. The proposed framework is built upon a four-layer architecture—Policy, Control & Optimization, Communication & Interoperability, and Engagement & Measurement & Verification—designed to translate regulatory mandates into actionable and measurable DSM interventions. Utilizing over 2.4 million high-resolution smart meter readings collected between January 2024 and January 2025, we evaluate two DSM strategies: peak shaving and time-of-use (TOU) response. The peak shaving scenario achieved a 1.42% reduction in total energy consumption and a 9.12% reduction in peak demand, aligning with the objectives of Qatar’s National Renewable Energy Strategy. Conversely, the TOU scenario demonstrated limited effectiveness, underscoring the need for enhanced behavioral targeting and automation. The study concludes by outlining future directions, including the development of real-time adaptive controls, blockchain-enabled audit mechanisms, and potential scalability of the framework across the Gulf Cooperation Council region.

Leaf disease detection (LDD) is a main component of crop management systems. An operational LDD system requires a method that enables leaf detection in plant images and an LDD model that identifies whether the detected leaves present signs of disease and if so which type. The use of Convolutional Neural Networks (CNN) has enabled the development of effective LDD models using available rich leaf disease datasets. More recently, Vision Transformers (ViT) and EfficientNets have emerged as a competitive alternatives to conventional CNNs. Progress in leaf detection has not proceeded at the same pace. In addressing the development of an operational LDD framework, the goal of this study is threefold: (1) provide a comparison of CNN, ViT and EfficientNet LDD algorithms using datasets totaling 48,546 leaf images from the lab and the field, (2) develop a YOLO-based detection model that identifies leaves in plant images, and (3) detail the development of a drone-based LDD framework that integrates models of leaf detection and leaf disease detection. Our experimental results show that while CNN, ViT and EfficientNet LDD models all perform well on standard LDD datasets, with EfficientNets showing a marginal advantage, the ViT classifier demonstrates superior performance when run in conjunction with leaf detection in an operational setting.

With the increasing ubiquity of Renewable Energy Communities (RECs), peer-to-peer energy trading is gradually entering its global operational phase. In addition to existing peer-to-grid energy trading programs, several countries have developed special regulatory frameworks that reward prosumers who share the excess energy they produce within RECs that are virtually defined over the national grid. In addition, peer-to-peer energy sharing can be independently regulated within RECs established as microgrids that have their own operating structure. This study examines the impact of regulatory frameworks on peer-to-peer energy sharing and prosumer rewards in diverse RECs. Using agent-based modeling, we create social simulations for RECs where prosumer rewards are diversely regulated and then measure the success of energy sharing as financial benefits to prosumers. Results show that the removal or reduction of fuel subsidies and the adoption of net metering to reward prosumers are the most effective measures to ensure the highest rewards for prosumers. The use of batteries to store excess energy produced for later self-consumption can also be effective, especially in microgrid energy communities.

This research introduces a groundbreaking approach that combines distributed generation and blockchain technology in microgrid systems, transforming the energy market. The key innovation lies in the creation of a peer-to-peer (P2P) energy trading system within community microgrids. By digitizing power distribution and harnessing the transparency, security, and efficiency of blockchain technology, this system enables a seamless and secure energy exchange. To optimize the performance of microgrids, distributed energy resources, battery storage, and smart meters are integrated. This integration empowers traditional power consumers to become prosumers by utilizing renewable resources such as wind power. This shift promotes local self-sufficiency while enhancing energy sustainability. Existing literature highlights several challenges associated with implementing market principles in low-voltage/medium-voltage systems, coordinating distribution operators, and developing user-friendly platforms for microgrid adoption. Furthermore, voltage and frequency fluctuations during periods of high electricity generation pose difficulties along with managing surplus energy effectively. Additionally, current feed-in tariffs for prosumers present notable obstacles. To overcome these challenges, this study proposes the implementation of a self-sustaining community microgrid system that facilitates energy trading. Utilizing blockchain technology allows it to mimic a decentralized microgrid energy market. In this peer-to-peer market model, the clearing price is determined based on customers' reactions to price fluctuations as an incentive for them to adjust their consumption patterns. To ensure seamless and automatic transactions among participants, a unique cryptocurrency named “Cosmos” is introduced based on blockchain technology.

This research shows that prosumer consortium transactive models are useful for lowering the price of energy and increasing the stability and reliability of power in localized areas. The decentralized nature of blockchain and solar power prediction helps keep control within local areas, such as Education City Community Housing (ECCH), where many households have access to resources that would otherwise be expensive or otherwise impossible to manage. This could ultimately lead to the wider adoption of such models to reduce energy costs and create energy-conscious communities. Overall, prosumer consortium energy transactive models can create a win-win situation for the parties involved. Increasing communication between the participants, lowering costs, and eliminating intermediary organizations, allows prosumers to take control of their electricity usage, become self-sufficient, and contribute to an economy powered by distributed energy generation. Furthermore, it also allows institutions to become more sustainable, better manage their energy demand, and enjoy more reliable energy with reduced costs.

The rise of distributed energy generation through solar panels in homes and businesses sparks the creation of fresh energy markets. This shift removes the old boundaries between energy suppliers and users, leading to the emergence of energy “prosumers.” Blockchain technology enhances safe and affordable direct energy swaps within a decentralized setup, employing encryption and consensus checks. The research utilized a unique approach called “Agent-Based Modeling (ABM) along with Geographic Information System (GIS)” to assess energy trading within the real estate sector. This process encompassed gathering and analyzing data about daily energy consumption to grasp market dynamics and construct a decentralized energy trading approach. The initial simulation involved five key stages: collecting, processing, predicting, analyzing, confirming, and evaluating performance. The primary actors in this model were individuals, consumers, energy providers, and producers. The outcomes from the experiments indicated that one could assess the distinct households' features by incorporating GIS data and an agent-centric model. Harnessing high-performance computing makes it possible to manage large-scale simulations involving multiple participants. Generally, this approach is anticipated to enhance the model's efficiency and offer a flexible environment for scrutinizing how energy blockchain impacts finance, technology, and society.

The growth of decentralized energy production, especially through solar PV systems in homes and businesses, has introduced the concept of an “energy prosumer.” This term combines the roles of energy producers and consumers, challenging traditional categorizations. The key factor in this transformation is blockchain technology. By utilizing its encrypted database structure built on consensus, blockchain provides a novel solution for direct energy trading. It serves a wide range of users, including everyday consumers and prosumers, as well as larger energy suppliers and utility companies, ensuring secure and cost-effective energy transactions. This research aims to introduce and apply an Agent-Based Model (ABM) that simulates electricity trade. The goal is to predict household power consumption patterns and validate blockchain procedures. A specially designed multi-agent system, specifically created for Transactive Energy (TE) in Distributed Energy Resources (DER), was developed and tested within the ECCH microgrid, relying on blockchain principles. Emerging concepts like blockchain-driven Local Energy Markets (LEM) suggest the use of auction mechanisms to balance future energy supply and demand. These models require accurate short-term predictions of individual household energy generation and usage. This study focuses on improving the accuracy of household energy forecasts using advanced techniques. It also examines the impact of prediction errors across three different supply scenarios. This research significantly diverges from previous studies that mainly tracked smart meter timelines.

With the emergence of distributed energy generation through residential and commercial solar PV applications, new energy markets are created where consumers and producers are no longer separated, giving rise to the concept of energy prosumers. In a distributed database architecture that utilizes cryptographic hashing and consensus-based verification, blockchain technology offers utilities, consumers and prosumers with a novel, secure, and cost-effective energy-trading solution that automates direct energy transactions. A blockchain-based energy trading simulation environment integrated with a Geographic Information System (GIS) is proposed in this study for Qatar’s Education City Community Housing (ECCH). A comprehensive amount of daily energy activity data is collected and analyzed as part of the approach for recreating spatiotemporal characteristics of trading in a small marketplace. Through this type of simulation, stakeholders can better understand the dynamics of a real trading market, and thus make better decisions for developing a decentralized energy market. Using GIS information and an agent-based design, the results indicate that the characteristics of transactions executed in a local housing market can be easily tailored by adjusting parameters. It is possible to improve model performance by employing high-performance computing to conduct large-scale simulations with many agents to provide more realistic outcomes. The model offers a scalable environment for analyzing an energy blockchain from the perspective of Qatari society, finance, and technology.

With the emergence of distributed energy generation through residential and commercial solar PV applications, new energy markets are created where consumers and producers are no longer separated, giving rise to the concept of energy prosumers. In a distributed database architecture that utilizes cryptographic hashing and consensus-based verification, blockchain technology offers utilities, consumers and prosumers with a novel, secure, and cost-effective energy-trading solution that automates direct energy transactions. A blockchain-based energy trading simulation environment integrated with a Geographic Information System (GIS) is proposed in this study for Qatar’s Education City Community Housing (ECCH). A comprehensive amount of daily energy activity data is collected and analyzed as part of the approach for recreating spatiotemporal characteristics of trading in a small marketplace. Through this type of simulation, stakeholders can better understand the dynamics of a real trading market, and thus make better decisions for developing a decentralized energy market. Using GIS information and an agent-based design, the results indicate that the characteristics of transactions executed in a local housing market can be easily tailored by adjusting parameters. It is possible to improve model performance by employing high-performance computing to conduct large-scale simulations with many agents to provide more realistic outcomes. The model offers a scalable environment for analyzing an energy blockchain from the perspective of Qatari society, finance, and technology.

Based on the dissemination of knowledge in social networks within urban communities, this study proposes an agent-based model of innovation diffusion for Renewable Energy Technologies (RET1). To model the pace of RET innovation diffusion, the information flow patterns across social media networks including Twitter and home networks are merged. The resulting strategy offers a framework for capturing how RET innovation dissemination in urban neighborhood networks and online social networks may affect household uptake of renewable energy technology. The introduction of solar PV in Qatar serves as an example of how this strategy is applied.

The ability to examine the behavior of biological systems in silico has the potential to greatly accelerate the pace of discovery in diseases, such as stroke, where in vivo analysis is time intensive and costly. In this paper we describe an approach for in silico examination of responses of the blood transcriptome to neuroprotective agents and subsequent stroke through the development of dynamic models of the regulatory processes observed in the experimental gene expression data. First, we identified functional gene clusters from these data. Next, we derived ordinary differential equations (ODEs) from the data relating these functional clusters to each other in terms of their regulatory influence on one another. Dynamic models were developed by coupling these ODEs into a model that simulates the expression of regulated functional clusters. By changing the magnitude of gene expression in the initial input state it was possible to assess the behavior of the networks through time under varying conditions since the dynamic model only requires an initial starting state, and does not require measurement of regulatory influences at each time point in order to make accurate predictions. We discuss the implications of our models on neuroprotection in stroke, explore the limitations of the approach, and report that an optimized dynamic model can provide accurate predictions of overall system behavior under several different neuroprotective paradigms.

The increasing asymmetric nature of threats to the security, health and sustainable growth of our society requires that anticipatory reasoning become an everyday activity. Currently, the use of anticipatory reasoning is hindered by the lack of systematic methods for combining knowledge- and evidence-based models, integrating modeling algorithms, and assessing model validity, accuracy and utility. The workshop addresses these gaps with the intent of fostering the creation of a community of interest on model integration and evaluation that may serve as an aggregation point for existing efforts and a launch pad for new approaches.

The ability to support creation and parallel analysis of competing scenarios is perhaps the greatest single challenge for today's intelligence analysis systems. Dempster-Shafer theory provides an evidentiary reasoning methodology for scenario construction and analysis that offers potential advantages when compared to other approaches such as Bayesian nets as it places less conceptual load on the analyst by not requiring the complete specification of joint probability distributions. This paper presents a method that can further reduce the conceptual load by taking advantage of hierarchically structured indicators. We present a novel interface for this layered, Dempster-Shafer evidentiary reasoning approach and demonstrate the utility of this interface with reference to analysis problems focusing on comparing distinct hypotheses.

We present the design and implementation of InfoStar, an adaptive visual analytics platform for mobile devices such as PDAs, laptops, Tablet PCs and mobile phones. InfoStar extends the reach of visual analytics technology beyond the traditional desktop paradigm to provide ubiquitous access to interactive visualizations of information spaces. These visualizations are critical in addressing the knowledge needs of human agents operating in the field, in areas as diverse as business, homeland security, law enforcement, protective services, emergency medical services and scientific discovery. We describe an initial real world deployment of this technology, in which the InfoStar platform has been used to offer mobile access to scheduling and venue information to conference attendees at Supercomputing 2004.