A Prosumer-Oriented, Interoperable, Modular and Secure Smart Home Energy Management System Architecture

Gonzalez-Gil, Pedro; Martı́nez, Juan Antonio; Skarmeta-Gómez, Antonio F.

doi:10.3390/smartcities5030053

Cited by 7 publications

(3 citation statements)

References 34 publications

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“…The goal of smart EMSs is to have a system that continuously monitors the energy demand and how the system reacts to that demand. This helps to optimize the entire system [29]. This optimization can achieve various goals like saving energy, reducing emissions, and cutting costs.…”

Section: Energy Management System (Ems)mentioning

confidence: 99%

Optimizing Energy Consumption in Agricultural Greenhouses: A Smart Energy Management Approach

Jamshidi,

Ghiasi,

Mehrandezh

et al. 2024

Smart Cities

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Efficient energy management is crucial for optimizing greenhouse (GH) operations and promoting sustainability. This paper presents a novel multi-objective optimization approach tailored for GH energy management, aiming to minimize grid energy consumption while maximizing battery state of charge (SOC) within a specified time frame. The optimization problem integrates decision variables such as network power, battery power, and battery energy, subject to constraints based on battery capacity and initial energy, along with minimum and maximum energy from the battery storage system. Through the comparison of a smart energy management system (EMS) with traditional optimization algorithms, the study evaluates its efficiency. Key hyperparameters essential for the optimization problem, including plateau time, prediction time, and optimization time, are determined using the ellipse optimization method. Treating the GH as a microgrid, the analysis encompasses energy management indicators and loads. A simulation conducted via Simulink in MATLAB software (R2021b) demonstrates a significant enhancement, with the smart EMS achieving a more than 50% reduction in the objective function compared to conventional EMS. Moreover, the EMS exhibits robust performance across variations in the load power and irradiation profile. Under partial shading conditions, the EMS maintains adaptability, with a maximum objective function increase of 0.35553%. Aligning the output power of photovoltaic (PV) systems with real-world conditions further validates the EMS’s effectiveness in practical scenarios. The findings underscore the efficiency of the smart EMS in optimizing energy consumption within GH environments, offering promising avenues for sustainable energy management practices. This research contributes to advancing energy optimization strategies in agricultural settings, thereby fostering resource efficiency and environmental stewardship.

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Section: Energy Management System (Ems)mentioning

confidence: 99%

Optimizing Energy Consumption in Agricultural Greenhouses: A Smart Energy Management Approach

Jamshidi,

Ghiasi,

Mehrandezh

et al. 2024

Smart Cities

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“…(2022), the architecture integrates modular and secure components for smart home energy management [10]. Additionally, Laayati et al ( 2022) provide insights into an AI-layered architecture for smart grid-ready systems, which is considered for managing smart transformers [18].…”

Section: Technical Frameworkmentioning

confidence: 99%

Empowering Prosumers in The Energy Sector: Integrating Ai And Blockchain For Enhanced Photovoltaic and Battery Systems

2024

Eng OA

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In a world where energy is as precious as the finest diamonds, and the quest for sustainable living is akin to a high-stakes spy game, a new era of energy prosumers emerges from the shadows. This tale unfolds in the quiet corners of ordinary neighborhoods, where unsuspecting homeowners hold the key to a revolution in the energy sector. Our protagonists are private clients, each equipped differently in this energy escapade: some with no photovoltaics (PV) or stationary batteries, others with either PV or batteries, and a few with both, harnessing the sun’s power and the might of modern storage. Like a mastermind plotting in a secret lair, Artificial Intelligence (AI) enters the scene. It’s the unseen hero, working tirelessly to analyze, predict, and optimize. AI, with its intricate web of algorithms, becomes the brain behind the operation, determining the most efficient use of energy, managing storage, and turning every joule of electricity into a strategic asset. But what’s a thriller without a twist? Enter blockchain, the enigmatic and unbreakable technology, ensuring every energy transaction is secure, transparent, and immutable. In this world, blockchain isn’t just a ledger; it’s the trusty sidekick, the keeper of secrets, ensuring that every peer-to-peer energy exchange is a discreet affair, away from prying eyes. Our narrative takes a deeper dive in the ’Implementation’ chapter, a dossier of technical know-how and cunning strategies. Here, the AI and blockchain technologies are not just concepts but active agents in a grander scheme, integrated with PV systems and batteries. They’re not just tools; they’re characters in this play, each with a role, each with a purpose. As the plot thickens, we see the dance of electrons, the exchange of watts and volts, in a marketplace that’s as dynamic as the stock exchange. Homeowners, once mere spectators, are now key players in this game of energy and power. This paper isn’t just a collection of data and theories. It’s a narrative of change, a story of a future where every home is a powerhouse, every homeowner a prosumer. It’s a tale of technology and humanity converging to rewrite the script of energy consumption and production. So, dear reader, brace yourself for a journey into the heart of the energy sector, where AI and blockchain are not just the heroes but the harbingers of a new era. Welcome to the thrilling world of sustainable energy, where every decision, every watt, every battery, tells a story

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“…Engineers have designed power sources to meet the requirements of their applications. Modularity, interoperability, and remote control are defining features for developing smart home devices (Gonzalez‐Gil et al (2022)). Devices will have a range of choices open to the user.…”

Section: Machine Learning and Internet Of Thingsmentioning

confidence: 99%

Machine learning and internet of things applications in enterprise architectures: Solutions, challenges, and open issues

Rehman,

Tariq,

Moqurrab

et al. 2023

Expert Systems

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SummaryThe rapid growth of the Internet of Things (IoT) has led to its widespread adoption in various industries, enabling enhanced productivity and efficient services. Integrating IoT systems with existing enterprise application systems has become common practice. However, this integration necessitates reevaluating and reworking current Enterprise Architecture (EA) models and Expert Systems (ES) to accommodate IoT and cloud technologies. Enterprises must adopt a multifaceted view and automate various aspects, including operations, data management, and technology infrastructure. Machine Learning (ML) is a powerful IoT and smart automation tool within EA. Despite its potential, a need for dedicated work focuses on ML applications for IoT services and systems. With IoT being a significant field, analyzing IoT‐generated data and IoT‐based networks is crucial. Many studies have explored how ML can solve specific IoT‐related challenges. These mutually reinforcing technologies allow IoT applications to leverage sensor data for ML model improvement, leading to enhanced IoT operations and practices. Furthermore, ML techniques empower IoT systems with knowledge and enable suspicious activity detection in smart systems and objects. This survey paper conducts a comprehensive study on the role of ML in IoT applications, particularly in the domains of automation and security. It provides an in‐depth analysis of the state‐of‐the‐art ML approaches within the context of IoT, highlighting their contributions, challenges, and potential applications.

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A Prosumer-Oriented, Interoperable, Modular and Secure Smart Home Energy Management System Architecture

Cited by 7 publications

References 34 publications

Optimizing Energy Consumption in Agricultural Greenhouses: A Smart Energy Management Approach

Optimizing Energy Consumption in Agricultural Greenhouses: A Smart Energy Management Approach

Empowering Prosumers in The Energy Sector: Integrating Ai And Blockchain For Enhanced Photovoltaic and Battery Systems

Machine learning and internet of things applications in enterprise architectures: Solutions, challenges, and open issues

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