Advanced Planning of Home Appliances with Consumer’s Preference Learning

Bazenkov, Nikolay; Goubko, Mikhail

doi:10.1007/978-3-030-00617-4_23

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…Moreover, the researchers employed Q-learning to exploit the projected 65% potential energy savings for small houses through effective device scheduling, and they demonstrated enhancements according to the baseline. Also, inverse reinforcement learning was applied by Bazenkov et al (Bazenkov and Goubko, 2018) to forecast consumer appliance consumption, and it was observed that IRL outperformed other machine learning approaches like random forest. In a study by Jiang et al, a hierarchical multi-agent Q-learning technique was implemented in a microgrid for responding to the dynamic demand as well as manage distributed energy sources (Jiang and Fei, 2015).…”

Section: Reinforcement Learning Techniques For Intelligent Energy Man...mentioning

confidence: 99%

Harnessing artificial intelligence for data-driven energy predictive analytics: A systematic survey towards enhancing sustainability

Le,

Priya,

et al. 2024

Int. J. Renew. Energy Dev.

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The escalating trends in energy consumption and the associated emissions of pollutants in the past century have led to energy depletion and environmental pollution. Achieving comprehensive sustainability requires the optimization of energy efficiency and the implementation of efficient energy management strategies. Artificial intelligence (AI), a prominent machine learning paradigm, has gained significant traction in control applications and found extensive utility in various energy-related domains. The utilization of AI techniques for addressing energy-related challenges is favored due to their aptitude for handling complex and nonlinear data structures. Based on the preliminary inquiries, it has been observed that predictive analytics, prominently driven by artificial neural network (ANN) algorithms, assumes a crucial position in energy management across various sectors. This paper presents a comprehensive bibliometric analysis to gain deeper insights into the progression of AI in energy research from 2003 to 2023. AI models can be used to accurately predict energy consumption, load profiles, and resource planning, ensuring consistent performance and efficient resource utilization. This review article summarizes the existing literature on the implementation of AI in the development of energy management systems. Additionally, it explores the challenges and potential areas of research in applying ANN to energy system management. The study demonstrates that ANN can effectively address integration issues between energy and power systems, such as solar and wind forecasting, power system frequency analysis and control, and transient stability assessment. Based on the comprehensive state-of-the-art study, it can be inferred that the implementation of AI has consistently led to energy reductions exceeding 25%. Furthermore, this article discusses future research directions in this field.

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Section: Reinforcement Learning Techniques For Intelligent Energy Man...mentioning

confidence: 99%

Harnessing artificial intelligence for data-driven energy predictive analytics: A systematic survey towards enhancing sustainability

Le,

Priya,

et al. 2024

Int. J. Renew. Energy Dev.

View full text Add to dashboard Cite

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“…This content-based approach can face difficulties in the early stages when there is not enough data for training. Some solutions to this cold-start problem have been proposed based on Bayesian optimization and inverse reinforcement learning [39], [40]. In both cases, it is necessary to use a prior distribution of a function or its parameters and update it when new observations are available.…”

Section: Recommender Systemmentioning

confidence: 99%

A Recommender System for Predictive Control of Heating Systems in Economic Demand Response Programs

Cárdenas

Agbossou

Malhamé

et al. 2022

IEEE Open J. Ind. Applicat.

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Flexibility from demand-side resources is increasingly required in modern power systems to maintain the dynamic balance between demand and supply. This flexibility comes from elastic users managing controllable loads. In this context, controlling Electric Space Heaters (ESHs) is of particular interest because it can leverage building inner thermal storage capacity to shift consumption while maintaining comfort conditions. Some economic Demand Response (DR) programs have considered exploiting EHSs flexibility potentials in recent years. However, these programs still struggle to engage customers due to the complexity of processing price signals for inexpert users. Therefore, it is necessary to develop automated tools for helping users to operate their loads. Accordingly, this paper presents a recommender system based on Gaussian processes to discover users' valuations of thermal comfort and perform the predictive control of their ESHs. The proposed method enables customers to participate in DR programs and impose their preferences through straightforward queries instead of directly changing control parameters. Validation results demonstrate that users maximize their utility by supplying noiseless and consistent data to the recommender system. Additionally, the suggested approach achieves a higher acceptance rate than other methods from the literature, such as persistency and support vector machines.

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“…The authors implement Q learning with the aim of taking advantage of the estimated 65% of potential energy savings for small buildings by efficient device scheduling and report improvements upon the baseline. Bazenkov and Goubko utilize inverse reinforcement learning (IRL) to predict consumer appliance usage and report a higher accuracy using IRL than other machine learning methods such as random forest [86]. A 2018 study by Mocanu et al implement both Deep Q Learning (DQL) and Deep Policy Gradients (DPG) to optimize the energy management system for 10, 20 and 48 houses [87].…”

Section: Home Management Systemsmentioning

confidence: 99%

A review of reinforcement learning for autonomous building energy management

Mason

Grijalva

2019

Computers & Electrical Engineering

214

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The area of building energy management has received a significant amount of interest in recent years. This area is concerned with combining advancements in sensor technologies, communications and advanced control algorithms to optimize energy utilization. Reinforcement learning is one of the most prominent machine learning algorithms used for control problems and has had many successful applications in the area of building energy management. This research gives a comprehensive review of the literature relating to the application of reinforcement learning to developing autonomous building energy management systems. The main direction for future research and challenges in reinforcement learning are also outlined.

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Advanced Planning of Home Appliances with Consumer’s Preference Learning

Cited by 3 publications

References 11 publications

Harnessing artificial intelligence for data-driven energy predictive analytics: A systematic survey towards enhancing sustainability

Harnessing artificial intelligence for data-driven energy predictive analytics: A systematic survey towards enhancing sustainability

A Recommender System for Predictive Control of Heating Systems in Economic Demand Response Programs

A review of reinforcement learning for autonomous building energy management

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