Load Disaggregation Based on Time Window for HEMS Application

Lemes, Dimas Augusto Mendes; Cabral, Thales Wulfert; Fraidenraich, Gustavo; Meloni, Luís Geraldo Pedroso; Lima, Eduardo Rodrigues de; Neto, Fernando Bauer

doi:10.1109/access.2021.3078340

Cited by 27 publications

(26 citation statements)

References 39 publications

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“…Unlike previous reviews, the applications of load monitoring are addressed based on technical challenges faced by the residential systems available. Contribution [86] proposed non-intrusive load monitoring based on the time window for HEMS application. The authors examined three machine learning algorithms (Decision Tree, k-Nearest Neighbor (kNN), and Random Forest).…”

Section: Future Applications For Schedulable and Non-schedulable Appliance Consumption Forecasting Using Nilmmentioning

confidence: 99%

Design of Ensemble Forecasting Models for Home Energy Management Systems

et al. 2021

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The increasing levels of energy consumption worldwide is raising issues with respect to surpassing supply limits, causing severe effects on the environment, and the exhaustion of energy resources. Buildings are one of the most relevant sectors in terms of energy consumption; as such, efficient Home or Building Management Systems are an important topic of research. This study discusses the use of ensemble techniques in order to improve the performance of artificial neural networks models used for energy forecasting in residential houses. The case study is a residential house, located in Portugal, that is equipped with PV generation and battery storage and controlled by a Home Energy Management System (HEMS). It has been shown that the ensemble forecasting results are superior to single selected models, which were already excellent. A simple procedure was proposed for selecting the models to be used in the ensemble, together with a heuristic to determine the number of models.

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Section: Future Applications For Schedulable and Non-schedulable Appliance Consumption Forecasting Using Nilmmentioning

confidence: 99%

Design of Ensemble Forecasting Models for Home Energy Management Systems

et al. 2021

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“…In this work, as shown in Eq. ( 7), F1 score [15], [31], [39] is used to evaluate the prediction performance of the energy decomposition and time-series load forecasting approaches.…”

Section: ) Using F1 Score To Evaluate Time-series Load Forecasting Mo...mentioning

confidence: 99%

A Smart Home Energy Management System Utilizing Neurocomputing-Based Time-Series Load Modeling and Forecasting Facilitated by Energy Decomposition for Smart Home Automation

et al. 2022

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The key advantage of using power-utility-owned smart meters is the ability to transmit electrical energy consumption data to power utilities' remote data centers for various purposes, such as billing. Several useful consumer-centric use cases can also be identified for the collection and further analysis of consumers' electrical energy consumption data from smart meters. One of the use cases is home automation. Recent related solutions for home automation involving home security and healthcare depend on the installation of sensors and/or other devices such as video cameras, which have high costs for installation and annual maintenance. Because the electrical energy consumption patterns mined from smart meter data are indicative of residents' daily life, it is possible to develop a new home automation approach based on energy decomposition for smart home automation. Accordingly, in this work, a smart home energy management system (SHEMS) utilizing a parallel-processing-implemented, GPU-accelerated neurocomputing-based time-series load modeling and forecasting mechanism is proposed for smart home automation. Energy decomposition is used to facilitate the time-series load modeling and forecasting mechanism, which tracks appliance-level electrical energy consumption to be quantitatively modeled from circuit-level consumption, with no intrusive deployment of networked plug-level power meters for individual electrical home appliances. For the neurocomputing approach applied in this mechanism, an autoregressive multilayer perceptron methodology is compared against a stacked long short-term memory methodology. The presented neurocomputing-based time-series load modeling and forecasting mechanism facilitated by energy decomposition is capable of predicting residents' daily behavioral patterns by nonintrusively analyzing and modeling relevant electrical home appliances based on their past trends for smart home automation.

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“…This application uses artificial intelligence algorithms to determine the devices connected to outlets, using information of aggregate energy consumed by the house. More details on this application are found in [32].…”

Section: B Control and Application Managementmentioning

confidence: 99%