A Smart Home Energy Management System Using Two-Stage Non-Intrusive Appliance Load Monitoring over Fog-Cloud Analytics Based on Tridium’s Niagara Framework for Residential Demand-Side Management

Chen, Yung-Yao; Chen, Ming-Hung; Chang, Che-Ming; Hsiang, K.-Y.; Lin, Yu‐Hsiu

doi:10.3390/s21082883

Cited by 20 publications

(6 citation statements)

References 42 publications

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“…Compared with AI, the applications of AI methods including machine learning and deep learning are usually used to process and analyze data for decision-making, such as electric load forecasting [ 9 , 10 ], electric consumer categorization [ 11 ], and anomaly detection [ 12 ]. In such a smart grid system, the smart meter is an essential IoT device that records energy consumption data for further understanding, managing, planing, and optimizing power demands of electric consumers [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

An Incremental Clustering Algorithm with Pattern Drift Detection for IoT-Enabled Smart Grid System

Jiang

Lin

Yang

2021

Sensors

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The IoT-enabled smart grid system provides smart meter data for electricity consumers to record their energy consumption behaviors, the typical features of which can be represented by the load patterns extracted from load data clustering. The changeability of consumption behaviors requires load pattern update for achieving accurate consumer segmentation and effective demand response. In order to save training time and reduce computation scale, we propose a novel incremental clustering algorithm with probability strategy, ICluster-PS, instead of overall load data clustering to update load patterns. ICluster-PS first conducts new load pattern extraction based on the existing load patterns and new data. Then, it intergrades new load patterns with the existing ones. Finally, it optimizes the intergraded load pattern sets by a further modification. Moreover, ICluster-PS can be performed continuously with new coming data due to parameter updating and generalization. Extensive experiments are implemented on real-world dataset containing diverse consumer types in various districts. The experimental results are evaluated by both clustering validity indices and accuracy measures, which indicate that ICluster-PS outperforms other related incremental clustering algorithm. Additionally, according to the further case studies on pattern evolution analysis, ICluster-PS is able to present any pattern drifts through its incremental clustering results.

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Section: Introductionmentioning

confidence: 99%

An Incremental Clustering Algorithm with Pattern Drift Detection for IoT-Enabled Smart Grid System

Jiang

Lin

Yang

2021

Sensors

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“…In that paper, consumers' comfort was not considered, and no investigation of the renewable energy impacts on energy savings. Y. Y. Chen et al [30] presented a methodology for residential demand-side management in Nigeria by applying Fog-Cloud analytics to monitor nonintrusive appliance load in a smart home. The embedded IoT controllers connected IoT end devices, such as meters and sensors, and serve as a gateway in a smart building for residential demand-side management.…”

Section: Literature Reviewmentioning

confidence: 99%

IoT-based Optimal Energy Management in Smart Homes using Harmony Search Optimization Technique

Saadawi

Abohamama

Alrahmawy

2022

Preprint

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The Internet of things (IoT) has a variety of application domains including smart homes. A smart home is an automated intelligent home where IoT technologies are used to remotely control home appliances, manage home energy, enhance home security, and increase the comfort of home residents. On the other hand, renewable energy resources are highly speared to cover huge electricity demand. To save more energy and maximize energy efficiency, smart homes are engaged with energy management systems. Energy management and optimization solutions can help in decreasing the overall cost of energy while optimizing all operational performance.This paper presents an IoT-based optimal energy management approach depending on the Harmony Search optimization technique to save energy usage in smart homes. PV and wind renewable energy systems are used to feed the home with electricity. These on-site energy sources help smart homes to decrease their dependence on power from the electricity grid. Energy storage systems are used to maintain energy system reliability due to the inherent randomness and intermittence of solar radiation and wind speed. The proposed algorithm depends on-demand response to the price variations in the electric energy over time and applies the Time-of-Use pricing principle for controlling household appliances. IoT technology is applied to exchange data between household appliances and the control center, in addition, to communicating both energy management systems and security systems with the control center. In this paper, the IoT system is based on ZigBee wireless technology which is described as the lowest power consumed wireless technology. The algorithm is applied to a proposed building consisting of five floors, each floor contains two apartments with a total area of 200 meters for one apartment. The obtained results prove the efficacy of the proposed Harmony Search-based optimization method in saving energy and reducing electricity bills in smart homes while satisfying the required constraints. Additionally, the performance of the proposed algorithm is validated against four AI algorithms including Genetic Algorithm, Artificial Immune System, Ant Lion Optimization, and Bat Algorithm. In addition to its simplicity in programming and formulation, the conducted comparison demonstrates a similarity in the results with an advantage in the proposed algorithm in improving both the electricity cost-saving and elapsed runtime

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“…Limited by the network connectivity where the Internet is not always available, transmitting all media data from end devices to the cloud for data processing is no longer a wise approach for real-time video surveillance applications (latency-sensitive video surveillance applications) [9], [10]. Edge computing, a developed complement of cloud computing, can perform real-time media data processing at the edge of the Internet, while relieving computing and storage pressure of the cloud; this results in remedying high network latency and congestion [6],[8], [11], [12]. By leveraging storage and computing resources at the edge of the Internet, edge computing can provide distributed, real-time media data processing and analysis for surveillance applications [5],[8], [13].…”

Section: Introductionmentioning

confidence: 99%

Distributed Real-Time Object Detection Based on Edge-Cloud Collaboration for Smart Video Surveillance Applications

Chen

Lin

et al. 2022

IEEE Access

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Internet of Things (IoT) and artificial intelligence (AI) can realize the concept of "smart city." Video surveillance in smart cities is, usually, based on a centralized framework in which large amounts of real-time media data are transmitted to and processed in the cloud. However, the cloud relies on network connectivity of the Internet that is sometimes limited or unavailable; thus, the centralized framework is not sufficient for real-time processing of media data needed for smart video surveillance. To tackle this problem, edge computing -a technique for accelerating the development of AIoT (AI across IoT) in smart cities -can be conducted. In this paper, a distributed real-time object detection framework based on edge-cloud collaboration for smart video surveillance is proposed. When collaborating with the cloud, edge computing can serve as converged computing through which media data from distributed edge devices of the network are consolidated by AI in the cloud. After AI discovers global knowledge in the cloud, it to be shared at the edge is deployed remotely on distributed edge devices for real-time smart video surveillance. First, the proposed framework and its preliminary implementation are described. Then, the performance evaluation is provided regarding potential benefits, real-time responsiveness and lowthroughput media data transmission.

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A Smart Home Energy Management System Using Two-Stage Non-Intrusive Appliance Load Monitoring over Fog-Cloud Analytics Based on Tridium’s Niagara Framework for Residential Demand-Side Management

Cited by 20 publications

References 42 publications

An Incremental Clustering Algorithm with Pattern Drift Detection for IoT-Enabled Smart Grid System

An Incremental Clustering Algorithm with Pattern Drift Detection for IoT-Enabled Smart Grid System

IoT-based Optimal Energy Management in Smart Homes using Harmony Search Optimization Technique

Distributed Real-Time Object Detection Based on Edge-Cloud Collaboration for Smart Video Surveillance Applications

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