Online Energy Management for a Sustainable Smart Home With an HVAC Load and Random Occupancy

Yu, Liang; Jiang, Tao; Zou, Yulong

doi:10.1109/tsg.2017.2775209

Cited by 110 publications

(61 citation statements)

References 42 publications

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“…A machine learning-based DR approach was developed in [10,11], for circulation of loads mainly focused on home heating, ventilation, and air conditioning (HVACs). For the prediction of different residential load responses to various price signals, an ANN and wavelet transform (WT) approach was in employed in [12][13][14]. The HEMS made the existing home much smarter by enabling consumer participation.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Objective Approach for Optimal Energy Management in Smart Home Using the Reinforcement Learning

Diyan

Silva

Han

2020

Sensors

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Maintaining a fair use of energy consumption in smart homes with many household appliances requires sophisticated algorithms working together in real time. Similarly, choosing a proper schedule for appliances operation can be used to reduce inappropriate energy consumption. However, scheduling appliances always depend on the behavior of a smart home user. Thus, modeling human interaction with appliances is needed to design an efficient scheduling algorithm with real-time support. In this regard, we propose a scheduling algorithm based on human appliances interaction in smart homes using reinforcement learning (RL). The proposed scheduling algorithm divides the entire day into various states. In each state, the agents attached to household appliances perform various actions to obtain the highest reward. To adjust the discomfort which arises due to performing inappropriate action, the household appliances are categorized into three groups i.e., (1) adoptable, (2) un-adoptable, (3) manageable. Finally, the proposed system is tested for the energy consumption and discomfort level of the home user against our previous scheduling algorithm based on least slack time phenomenon. The proposed scheme outperforms the Least Slack Time (LST) based scheduling in context of energy consumption and discomfort level of the home user.

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Section: Introductionmentioning

confidence: 99%

A Multi-Objective Approach for Optimal Energy Management in Smart Home Using the Reinforcement Learning

Diyan

Silva

Han

2020

Sensors

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“…In this study, WH and AC, which are the most critical TCAs in a smart home, are modeled in detail. The model of WH can be expressed as constraints (12)- (16). Constraints in (12) ensure the power of WH change in a permitted range.…”

Section: ) Interruptible Appliancesmentioning

confidence: 99%

Pricing-Based Demand Response for a Smart Home With Various Types of Household Appliances Considering Customer Satisfaction

et al. 2019

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A home energy management system (HEMS) can potentially enable demand response (DR) applications for residential customers. This paper presents a detailed pricing-based DR for a smart home with various types of household appliances considering customer satisfaction. A wide variety of household appliances with different characteristics, together with energy storage units (ESUs) and distributed energy resources (DERs) can be flexibly incorporated in the proposed scheme. Besides, with a developed satisfaction model suitable for different types of household appliances, the proposed HEMS can provide multiple flexible solutions with different user satisfaction levels to occupants. In addition, other different DR strategies such as demand-limit-based DR and injection-limit-based DR can be easily adapted to the formulated scheme in this work. The numerical results reported in this paper demonstrate the effectiveness of the proposed scheme. The proposed scheme is generally applicable and valuable for any other kinds of the smart home.

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“…The problem of optimal residential energy management in both thermal and electrical zones is also tackled in Ref. [14] where the HEMS is designed to minimize the sum of energy cost and thermal discomfort cost in a long-term horizon. The authors of Ref.…”

Section: Related Workmentioning

confidence: 99%

Integrated Management of Energy, Wellbeing and Health in the Next Generation of Smart Homes

Jaouhari

García

Anvari‐Moghaddam

et al. 2019

Sensors

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This contribution proposes an implementation for next generation smart homes, where heterogeneous data, coming from multiple sensors (medical, wellbeing, energy, contextual, etc.) and house equipment (smart fridge, smart TV, etc.), need to be managed, secured and visualized. As a first step, it focuses only on energy and health data. However, it aims to lay the foundations to manage any type of information towards the development of smart interactions with the house, which might include artificial intelligence and machine learning. These data are securely collected using a central Web of Things gateway, located inside the smart home. For the e-health part, a set of possible use-cases is provided, along with the current progress of the implantation. In this regard, the main idea is to link the next generation smart homes with external medical entities in order to provide, first, quick intervention in the event of an abnormality being detected, and to be able to provide basic medical services such as remote consultations with a doctor for a particular health issue. This vision can be very promising, particularly in rural areas, where access to medical services is difficult. As for the energy part, the aim is to collect users’ energy consumption inside the smart home, which can be supplied from different sources (heat, water, gas, or electricity), and to enable the use of advanced algorithms to predict and manage local energy consumption and production (if any). This approach combines data collected from smart meters, operational information of the smart energy devices (the status of smart plugs), user’s requests and external network signals such as energy prices. By using a home energy management system that accepts such input parameters, the operation of in-home devices and appliances can be optimally controlled according to different objectives (e.g., minimizing energy costs and maximizing user’s comfort level).

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Online Energy Management for a Sustainable Smart Home With an HVAC Load and Random Occupancy

Cited by 110 publications

References 42 publications

A Multi-Objective Approach for Optimal Energy Management in Smart Home Using the Reinforcement Learning

A Multi-Objective Approach for Optimal Energy Management in Smart Home Using the Reinforcement Learning

Pricing-Based Demand Response for a Smart Home With Various Types of Household Appliances Considering Customer Satisfaction

Integrated Management of Energy, Wellbeing and Health in the Next Generation of Smart Homes

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