A home energy management system incorporating data-driven uncertainty-aware user preference

Liu, Yinyan; Jin, Ma; Xing, Xinjie; Liu, Xinglu; Wang, Wei

doi:10.1016/j.apenergy.2022.119911

Cited by 19 publications

(5 citation statements)

References 44 publications

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“…There are lots of related work have been conducted based on HEMS. Liu et al in (Liu et al, 2022) proposes a HEMS for residential users that incorporates the uncertainty of data-driven results to achieve the best trade-off between electricity cost and the preference level. Tostada-Veliz et al in (Tostado-Véliz et al, 2022) develops a HEMS that includes three novel demand response routines focused on peak clipping and demand flattening strategies.…”

Section: Related Workmentioning

confidence: 99%

Home energy management strategy to schedule multiple types of loads and energy storage device with consideration of user comfort: a deep reinforcement learning based approach

Pan,

Zhu,

Luo

et al. 2024

Front. Therm. Eng.

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With the increase in the integration of renewable sources, the home energy management system (HEMS) has become a promising approach to improve grid energy efficiency and relieve network stress. In this context, this paper proposes an optimization dispatching strategy for HEMS to reduce total cost with full consideration of uncertainties, while ensuring the users’ comfort. Firstly, a HEMS dispatching model is constructed to reasonably schedule the start/stop time of the dispatchable appliances and energy storage system to minimize the total cost for home users. Besides, this dispatching strategy also controls the switching time of temperature-controlled load such as air conditioning to reduce the energy consumption while maintaining the indoor temperature in a comfortable level. Then, the optimal dispatching problem of HEMS is modeled as a Markov decision process (MDP) and solved by a deep reinforcement learning algorithm called deep deterministic policy gradient. The example results verify the effectiveness and superiority of the proposed method. The energy cost can be effectively reduced by 21.9% at least compared with other benchmarks and the indoor temperature can be well maintained.

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Section: Related Workmentioning

confidence: 99%

Home energy management strategy to schedule multiple types of loads and energy storage device with consideration of user comfort: a deep reinforcement learning based approach

Pan,

Zhu,

Luo

et al. 2024

Front. Therm. Eng.

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“…The majority of the available literature addressed transmission-level risks from the perspectives of system planners, power providers, and electricity merchants. System stability and security issues were the emphasis of the literature from the standpoint of the system planner (Yinyan Liu et al, 2022). Financial and political volatility, as well as increased transaction and operational complexity, as well as stricter regulatory compliance requirements, have highlighted the importance of implementing a system for risk management that works (Thalassinos & Thalassinos, 2018).…”

Section: Hedgingmentioning

confidence: 99%

Strategic Utilization and Factors Influencing Financial Derivatives in Pakistan's Financial Landscape

Ali,

Naji,

Ali

2023

JEMI

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The purpose of this study was to investigate the derivatives usage available in Pakistan. The investigation encompassed an in-depth analysis of the diverse services offered by Pakistani financial institutions, with a focused assessment aimed at understanding the impact of these services on derivative usage. The data was gathered using a customized questionnaire. Several banks' statistical tools were used to examine the data after different users of derivatives filled out questionnaires. The study employed cross-sectional data, drawn from diverse demographic categories within a single locality, facilitating the potential replication of the model in other countries through longitudinal financial institution data. The study has several practical consequences for financial service providers that aim to grow the derivative market in Pakistan by providing high-quality services for derivatives. This research contributes to the existing literature by delineating the determinants and patterns of derivative usage in the Pakistani context, thereby establishing a cognitive motivation relationship. It uses a cognitive motivation relationship. This study is useful in the financial institutions/banking industry for a better understanding of this concept.

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“…By identifying individual appliance-level energy usage patterns, homeowners can make informed decisions on how to manage their energy use, reduce their carbon footprint, and save money on energy bills [2]. The identification of appliance consumption has been successfully applied to improve the householders' quality of life in many different scenarios, such as scheduling the use of large consumption appliances [3], detecting appliance malfunctions [4], or providing early preventive maintenance [5], among many others.…”

Section: Introductionmentioning

confidence: 99%

A Novel Non-Intrusive Load Monitoring Algorithm for Unsupervised Disaggregation of Household Appliances

Criado-Ramón,

Ruiz,

Iruela

et al. 2024

Information

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This paper introduces the first completely unsupervised methodology for non-intrusive load monitoring that does not rely on any additional data, making it suitable for real-life applications. The methodology includes an algorithm to efficiently decompose the aggregated energy load from households in events and algorithms based on expert knowledge to assign each of these events to four types of appliances: fridge, dishwasher, microwave, and washer/dryer. The methodology was developed to work with smart meters that have a granularity of 1 min and was evaluated using the Reference Energy Disaggregation Dataset. The results show that the algorithm can disaggregate the refrigerator with high accuracy and the usefulness of the proposed methodology to extract relevant features from other appliances, such as the power use and duration from the heating cycles of a dishwasher.

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A home energy management system incorporating data-driven uncertainty-aware user preference

Cited by 19 publications

References 44 publications

Home energy management strategy to schedule multiple types of loads and energy storage device with consideration of user comfort: a deep reinforcement learning based approach

Home energy management strategy to schedule multiple types of loads and energy storage device with consideration of user comfort: a deep reinforcement learning based approach

Strategic Utilization and Factors Influencing Financial Derivatives in Pakistan's Financial Landscape

A Novel Non-Intrusive Load Monitoring Algorithm for Unsupervised Disaggregation of Household Appliances

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