Internet of Things Applications for Energy Management in Buildings Using Artificial Intelligence—A Case Study

Energies, Год журнала: 2025, Номер 18(7), С. 1706 - 1706

Опубликована: Март 28, 2025

IoT applications for building energy management, enhanced by artificial intelligence (AI), have the potential to transform how is consumed, monitored, and optimized, especially in distributed systems. By using sensors smart meters, buildings can collect real-time data on usage patterns, occupancy, temperature, lighting conditions.AI algorithms then analyze this identify inefficiencies, predict demand, suggest or automate adjustments optimize use. Integrating renewable sources, such as solar panels wind turbines, into systems uses IoT-based monitoring ensure maximum efficiency generation These also enable dynamic pricing load balancing, allowing participate grids storing selling excess energy.AI-based predictive maintenance ensures that systems, inverters batteries, operate efficiently, minimizing downtime. The case studies show AI are driving sustainable development reducing consumption carbon footprints residential, commercial, industrial buildings. Blockchain further secure transactions increasing trust, sustainability, scalability. combination of IoT, AI, sources line with global trends, promoting decentralized greener study highlights adopting management offers not only environmental benefits but economic benefits, cost savings independence. best achieved accuracy was 0.8179 (RMSE 0.01). overall effectiveness rating 9/10; thus, AI-based solutions a feasible, cost-effective, approach office management.

Язык: Английский

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Enhancing And Optimization In Exisiting HVAC Equipment Using Tools Of Industry 4.0

Journal of Building Engineering, Год журнала: 2025, Номер unknown, С. 111924 - 111924

Опубликована: Янв. 1, 2025

Язык: Английский

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Smart Buildings Using Web of Things with .NET Core: A Framework for Inter-Device Connectivity and Secure Data Transfer

Information, Год журнала: 2025, Номер 16(2), С. 123 - 123

Опубликована: Фев. 8, 2025

The Internet of Things (IoT) is experiencing rapid growth, with an increasing number devices connected to the Internet. By 2020, approximately 54% 21.7 billion active internet-connected worldwide were IoT devices. This projected reach 30 by 2025, average four per person globally. use communication protocols, such as Bluetooth, Wi-Fi, and RFID, facilitate data exchange. However, absence standardized protocols reprogrammable architectures presents significant challenges for applications. Smart buildings, which heavily depend on technology, are particularly affected diversity standards used different Web (WoT) framework has been introduced address these challenges, enabling interoperability among heterogeneous enhancing system programmability. adoption necessitates more efficient integrated meet demands modern innovative building systems. study a WoT-based modular architecture designed ensure compatibility while providing scalable, flexible, secure solutions tailored current trends. In this study, Application Programming Interface (API) Worker Service developed using .NET Core technology WoT intelligent automation. integrates various subsystems, leveraging hardware seamless functionality. API facilitates device monitoring control, manages scheduling database operations. supports asynchronous employing HTTP WebSocket provides multi-user access role-based authorization. proposed automation was implemented evaluated, demonstrating its practical applicability effectiveness in managing complex, environments.

Язык: Английский

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Sustainable Development Goals and Energy Efficiency in Educational Institutions Through Smart Buildings and Machine Learning

Journal of Lifestyle and SDGs Review, Год журнала: 2025, Номер 5(3), С. e05010 - e05010

Опубликована: Фев. 28, 2025

Objective: This study aims to identify key characteristics of smart buildings that enhance sustainability and energy efficiency in educational institutions. By leveraging artificial intelligence, particularly machine learning, the seeks optimize consumption improve learning environments. Theoretical Framework: Smart integrate digital communication technologies infrastructure. Despite their benefits, adoption developing countries remains limited. builds on theories sustainable management computational optimization, emphasizing learning’s role predictive modeling. Method: A model was developed predict The k-nearest neighbor (k-NN) algorithm applied using open-access data from University Durrës Building Energy Management System. Model validation conducted through comparative analysis, assessing prediction accuracy energy-saving potential. Results Discussion: achieved an average relative error 18.26%, confirming its capability. features enabled savings between 44.8% 58.3%, depending analyzed spaces. Additionally, AI-powered interactive dashboard proposed for real-time monitoring aiding facility managers. Research Implications: underscores promoting reducing operational costs. AI-driven systems can significantly institutional practices. Originality/Value: research demonstrates effectiveness proposes AI-based solution buildings. findings provide valuable insights into integrating efficiency.

Язык: Английский

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Adaptive Jamming Mitigation for Clustered Energy-Efficient LoRa-BLE Hybrid Wireless Sensor Networks

Sensors, Год журнала: 2025, Номер 25(6), С. 1931 - 1931

Опубликована: Март 20, 2025

Wireless sensor networks (WSNs) are fundamental for modern IoT applications, yet they remain highly vulnerable to jamming attacks, which significantly degrade communication reliability and energy efficiency. This paper proposes a novel adaptive cluster-based mitigation algorithm designed heterogeneous WSNs that integrate LoRa Bluetooth Low Energy (BLE) technologies. The proposed strategy dynamically switches between protocols, optimizes consumption, reduces retransmissions under interference conditions by leveraging real-time network topology adjustments transmission power control. Through extensive experimental validation, we demonstrate our mechanism consumption up 38% lowers packet retransmission rates 47% compared single-protocol conditions. Additionally, results indicate the hybrid LoRa-BLE approach outperforms standalone BLE configurations in terms of resilience, adaptability, sustained data attack scenarios. work advances state-of-the-art introducing multi-protocol interference-resilient strategy, paving way more robust, energy-efficient, secure WSN deployments smart cities, industrial IoT, critical infrastructure monitoring.

Язык: Английский

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Gender Perceptions of IoT Technologies in Smart Cities

Smart Cities, Год журнала: 2025, Номер 8(2), С. 60 - 60

Опубликована: Апрель 6, 2025

The rapid integration of Internet Things (IoT) technologies in smart cities enhances urban management, yet public acceptance remains crucial for successful deployment. This study examined gender-based differences IoT through a survey 288 respondents from Warsaw and Plock, analyzed using structural equation modeling (SEM). results revealed that women demonstrated significantly higher trust (+0.93, p < 0.001), greater perceived safety (+0.24, = 0.013), stronger support environmental applications (+0.48, 0.007) than men. While usefulness was the strongest predictor men (β 0.523, 0.286, 0.001) awareness 0.507, drove among women. These findings highlight need gender-sensitive technology policies, emphasizing sustainability to foster inclusive city development. research can be used by authorities learn about requirements concerns residents design meets all residents’ better communicates technology. Furthermore, underscores importance targeted education campaigns address privacy promote broader adoption IoT-driven solutions environments.

Язык: Английский

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Development and Implementation of a User-Centric Real-time Energy Monitoring and Feedback System For Energy Conservation

Energy and Buildings, Год журнала: 2025, Номер unknown, С. 115787 - 115787

Опубликована: Апрель 1, 2025

Язык: Английский

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Adaptive Scheduling in Cognitive IoT Sensors for Optimizing Network Performance Using Reinforcement Learning

Applied Sciences, Год журнала: 2025, Номер 15(10), С. 5573 - 5573

Опубликована: Май 16, 2025

Cognitive sensors are embedded in home appliances and other surrounding devices to create a connected, intelligent environment for providing pervasive ubiquitous services. These frequently massive amounts of data with many redundant repeating bit values. always restricted resources, if careful strategy is not applied at the time deployment, become disconnected, degrading system’s performance terms energy, reconfiguration, delay, latency, packet loss. To address these challenges establish connected network, there need system evaluate contents detected values dynamically switch sensor states based on their function. Here this article, we propose reinforcement learning-based mechanism called “Adaptive Scheduling IoT Sensors Optimizing Network Performance using Reinforcement Learning (ASC-RL)”. For learning, proposed scheme uses three types parameters: internal parameters (states), environmental (sensing values), history (energy levels, roles, number switching states) derives function state-changing policy. Based policy, adjust adapt different energy states. minimize extensive sensing, reduce costly processing, lessen frequent communication. The reduces network traffic optimizes energy. main factors evaluated joint Gaussian distributions event correlations, derived results signal strengths, noise, prediction accuracy, efficiency combined reward score. Through comparative analysis, ASC-RL enhances overall by 3.5% detection transition probabilities. false alarm probabilities reduced 25.7%, transmission success rate increased 6.25%, reliability threshold 35%.

Язык: Английский

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From Design to Deployment: A Comprehensive Review of Theoretical and Experimental Studies of Multi-Energy Systems for Residential Applications

Electronics, Год журнала: 2025, Номер 14(11), С. 2221 - 2221

Опубликована: Май 29, 2025

Multi-energy systems (MESs) use more than one energy vector to fulfil users’ electrical, thermal, and cooling demands. This paper examines the recent developments in design, optimisation, implementation of MESs, focusing on residential applications. Firstly, advances design optimisation MESs are explained analysed. The field is characterised by proliferation bespoke methods suitable for this kind problem. Secondly, practical laboratory microgrids supplying electrical thermal loads discussed. hardware requirements, terms controllers converters, critically contrasted with real-world or multi-output real world. A description communication infrastructure required Finally, a critical review entire process, areas challenge, potential research opportunities presented.

Язык: Английский

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Enhancing energy efficiency in educational institutions: a scalable, integrated framework for cost-effective and sustainable energy management

Environment Development and Sustainability, Год журнала: 2025, Номер unknown

Опубликована: Май 31, 2025

Язык: Английский

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