A Compressive Receding Horizon Approach for Smart Home Energy Management

Leitão, Jorge; Fonseca, Carlos M.; Gil, Paulo; Ribeiro, Bernardete; Cardoso, Alberto

doi:10.1109/access.2021.3093277

Cited by 2 publications

(2 citation statements)

References 54 publications

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“…Many studies are available where the applications of ML-based energy systems forecasting in the HEMS context are assessed. Contribution [67] addresses the problem of residential load scheduling by using optimisation techniques in a receding horizon approach in a seven days-ahead prediction horizon. The proposed approach was compared with receding horizon and day-ahead scheduling techniques, and the obtained results were considered valid compared to the existing state-of-the-art approach.…”

Section: Applications Of Ml-based Energy Systems Forecasting In Hemsmentioning

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: Applications Of Ml-based Energy Systems Forecasting In Hemsmentioning

confidence: 99%

Design of Ensemble Forecasting Models for Home Energy Management Systems

et al. 2021

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“…Sustainable development goals are driving a deep energy transition among the demand side, to reduce the use of carbon energy (Elmorshedy et al, 2021). The rapid growth in electricity consumption has contributed to the widespread adoption of clean and renewable energy sources (Leitao et al, 2021). The use of renewable energy, mainly solar, wind and hydro power, emits almost no pollutants into the atmosphere, and produces no greenhouse gas emissions, which has the Basic framework of HEMS used in typical household (Zhao et al, 2021).…”

Section: Transitioning Renewable Energymentioning

confidence: 99%

Research on energy consumption in household sector: a comprehensive review based on bibliometric analysis

Wang,

Zhao,

Gao

et al. 2024

Front. Energy Res.

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Households are an important sector in carrying out human development activities, accounting for more than 30% of the total global energy consumption. The continued growth of household energy consumption (HEC) and carbon emissions is threatening economic and environmental sustainability. This review focuses on the research in the field of HEC and conducts a bibliometric analysis of research articles from the Web of Science Core Collection since 2000. The results show that: 1) HEC research has undergone rapid development since 2014, and interdisciplinary fusion and collaborative research have become dominant trends. 2) Keyword co-occurrence analysis clearly identifies the current urgent themes, including energy demand and its determinants, environmental impact factors and assessments, and energy-saving technologies and emission reduction measures. 3) The analysis of citations reveals that economic models, such as input-output models and life cycle assessment, are frequently employed in the field of HEC. Based on a summary of household energy-saving and emissions reduction work, this paper critically discusses the limitations of existing measures such as smart home technology, sustainable energy systems, and behavioral interventions. The main directions for promoting household energy-saving development in the future are identified: including improving the security and customer engagement of smart home technology, focusing on the availability and stability of sustainable energy, and paying more attention to low-income and aging households in behavioral intervention measures. One of the important obstacles facing research is how to reduce energy management efficiency and usage costs through technology and policy.

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A Compressive Receding Horizon Approach for Smart Home Energy Management

Cited by 2 publications

References 54 publications

Design of Ensemble Forecasting Models for Home Energy Management Systems

Design of Ensemble Forecasting Models for Home Energy Management Systems

Research on energy consumption in household sector: a comprehensive review based on bibliometric analysis

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