This paper proposes a demand-side management (DSM) platform adapted for the residential sector in Qatar that integrates features of human-system interaction (HSI). The paper addresses the problem of Qatar's high cooling load challenges by designing a DSM platform that prioritizes user engagement, comfort, and behavioral insights. The initial platform shows significant potential for peak load reduction, improved user satisfaction, and improved grid responsiveness.

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.

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 study embarks on a critical examination of future energy demand forecasting by utilizing a unique dataset that encapsulates energy usage patterns within the Education City Community Housing (ECCH). As urban areas grapple with the challenges of energy distribution and consumption, the integration of advanced predictive analytics becomes paramount. By adopting cutting-edge deep learning techniques, this research aims not only to enhance the efficiency of peer-to-peer (P2P) energy systems but also to mitigate issues associated with congestion and energy losses often seen in conventional grids. The methodology encompasses three sophisticated machine learning algorithms—Bidirectional Long-Short-Term Memory (Bi-LSTM), Random Forest (RF), and Gated Recurrent Unit (GRU)—each meticulously chosen to forecast energy consumption metrics specific to the community's needs. Evaluating model performance through metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), the research provides an insightful comparison of these algorithms in predicting energy demand. Findings underscore a significant predictive capability, particularly highlighting the GRU and Bi-LSTM models, which demonstrate exceptional accuracy when applied as univariate models focused solely on energy consumption data. This emphasis on energy-centric modeling not only contributes to a deeper understanding of consumption patterns within ECCH but also enhances the potential for implementing tailored energy solutions and fostering sustainable practices within the community. The implications of this study extend beyond mere prediction; they pave the way for smarter energy management strategies that could ultimately lead to a more sustainable and efficient future for urban living environments.

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.

Graphics recognition is an important task in many real-life applications. In this article, we propose a new approach to recognize graphical symbols by the use of a frequent Galois lattice. We propose to build a concept lattice not in terms of graphical patterns but in terms of frequent graphical patterns. The purpose of this paper is twofold : first, we try to identify the best primitives from a given graphical symbol based on a descriptor invariant to rotation, translation and scaling. Each symbol is decribed using a feature vector computed on stable neighborhood for a set of points chosen randomly from the symbol. Secondly, we propose a new recognition approach based on a frequent Galois lattice. The obtained concept lattice based on frequent patterns is used as a classifier. The retrieval performance and behavior of the method have been tested for graphics recognition. We have compared our method with others based on different descriptors and classifiers. Our approach proves that the symbol description method and the algorithm used to extract frequent attributes to build the frequent Galois lattice are suitable to the recognition process.

This paper presents a new approach for graphical symbols recognition by combining a concept lattice with a bag of words representation. Visual words define the properties of a graphical symbol that will be modeled in the Galois Lattice. The algorithm of classification is based on the Galois lattice where intentions of its concepts are visual words. The words as visual primitives allow to evaluate the classifier with a symbolic approach that no longer need a signature discretization step to build the Galois Lattice. Our approach is compared to classical approaches on different graphical symbols and we show the relevance and the robustness of our proposal for the classification task.