A Survey on Home Energy Management

Leitão, Jorge; Gil, Paulo; Ribeiro, Bernardete; Cardoso, Alberto

doi:10.1109/access.2019.2963502

Cited by 125 publications

(113 citation statements)

References 224 publications

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“…This dataset was divided into a training set, used to train the SVM algorithm, and a test set, used in the inference phase. The support vectors x , described in Equation (8), were computed during the training phase. In particular, each instance was labelled as belonging to an activity and was processed to extract a vector of nine features representing statistical values (mean, standard deviation, etc.)…”

Section: Evaluation Of the Proposed Model In A Practical Context: Thementioning

confidence: 99%

“…In recent years, the concept of a smart home has been extended from the simple automation and automatic control of the home appliances to a more complex management of the user interaction with several sensors and actuators deployed in the home environment in order to pursue the users' wellbeing and energy sustainability [1][2][3][4][5][6][7][8][9]. The development of wearable sensors has expanded the possibilities available in this context, pushing research towards new solutions based on behavioral monitoring [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

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A Model-Based Design Floating-Point Accumulator. Case of Study: FPGA Implementation of a Support Vector Machine Kernel Function

Bassoli

Bianchi

Munari

2020

Sensors

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Recent research in wearable sensors have led to the development of an advanced platform capable of embedding complex algorithms such as machine learning algorithms, which are known to usually be resource-demanding. To address the need for high computational power, one solution is to design custom hardware platforms dedicated to the specific application by exploiting, for example, Field Programmable Gate Array (FPGA). Recently, model-based techniques and automatic code generation have been introduced in FPGA design. In this paper, a new model-based floating-point accumulation circuit is presented. The architecture is based on the state-of-the-art delayed buffering algorithm. This circuit was conceived to be exploited in order to compute the kernel function of a support vector machine. The implementation of the proposed model was carried out in Simulink, and simulation results showed that it had better performance in terms of speed and occupied area when compared to other solutions. To better evaluate its figure, a practical case of a polynomial kernel function was considered. Simulink and VHDL post-implementation timing simulations and measurements on FPGA confirmed the good results of the stand-alone accumulator.

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Section: Evaluation Of the Proposed Model In A Practical Context: Thementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Model-Based Design Floating-Point Accumulator. Case of Study: FPGA Implementation of a Support Vector Machine Kernel Function

Bassoli

Bianchi

Munari

2020

Sensors

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“…Compared to the method with the fixed OoE threshold in [16], a fuzzy logic controller was designed to dynamically adjust the QoE threshold for optimizing the consumer QoE in [17]. A recent work on HEMS was summarized in [18].…”

Section: Introductionmentioning

confidence: 99%

Energy Management of Smart Home with Home Appliances, Energy Storage System and Electric Vehicle: A Hierarchical Deep Reinforcement Learning Approach

Lee

Choi

2020

Sensors

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This paper presents a hierarchical deep reinforcement learning (DRL) method for the scheduling of energy consumptions of smart home appliances and distributed energy resources (DERs) including an energy storage system (ESS) and an electric vehicle (EV). Compared to Q-learning algorithms based on a discrete action space, the novelty of the proposed approach is that the energy consumptions of home appliances and DERs are scheduled in a continuous action space using an actor–critic-based DRL method. To this end, a two-level DRL framework is proposed where home appliances are scheduled at the first level according to the consumer’s preferred appliance scheduling and comfort level, while the charging and discharging schedules of ESS and EV are calculated at the second level using the optimal solution from the first level along with the consumer environmental characteristics. A simulation study is performed in a single home with an air conditioner, a washing machine, a rooftop solar photovoltaic system, an ESS, and an EV under a time-of-use pricing. Numerical examples under different weather conditions, weekday/weekend, and driving patterns of the EV confirm the effectiveness of the proposed approach in terms of total cost of electricity, state of energy of the ESS and EV, and consumer preference.

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“…Shakeri et al [7] have given an overview of demand response programs, their practices along with home energy management system (HEMS) and load management techniques. Leitão et al [8] have presented a thorough review of HEMS with insights on the household appliance management. In [9], authors have explained mathematical models and heuristic optimization techniques integrated into HEMS for computing optimal appliance schedules and minimizing the energy usage by shifting the home appliances from peak to off-peak periods without hampering the user comfort.…”

Section: Introductionmentioning

confidence: 99%

An Overview of Demand Response in Smart Grid and Optimization Techniques for Efficient Residential Appliance Scheduling Problem

et al. 2020

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Smart grid (SG) is a next-generation grid which is responsible for changing the lifestyle of modern society. It avoids the shortcomings of traditional grids by incorporating new technologies in the existing grids. In this paper, we have presented SG in detail with its features, advantages, and architecture. The demand side management techniques used in smart grid are also presented. With the wide usage of domestic appliances in homes, the residential users need to optimize the appliance scheduling strategies. These strategies require the consumer’s flexibility and awareness. Optimization of the power demand for home appliances is a challenge faced by both utility and consumers, particularly during peak hours when the consumption of electricity is on the higher side. Therefore, utility companies have introduced various time-varying incentives and dynamic pricing schemes that provides different rates of electricity at different times depending on consumption. The residential appliance scheduling problem (RASP) is the problem of scheduling appliances at appropriate periods considering the pricing schemes. The objectives of RASP are to minimize electricity cost (EC) of users, minimize the peak-to-average ratio (PAR), and improve the user satisfaction (US) level by minimizing waiting times for the appliances. Various methods have been studied for energy management in residential sectors which encourage the users to schedule their appliances efficiently. This paper aims to give an overview of optimization techniques for residential appliance scheduling. The reviewed studies are classified into classical techniques, heuristic approaches, and meta-heuristic algorithms. Based on this overview, the future research directions are proposed.

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A Survey on Home Energy Management

Cited by 125 publications

References 224 publications

A Model-Based Design Floating-Point Accumulator. Case of Study: FPGA Implementation of a Support Vector Machine Kernel Function

A Model-Based Design Floating-Point Accumulator. Case of Study: FPGA Implementation of a Support Vector Machine Kernel Function

Energy Management of Smart Home with Home Appliances, Energy Storage System and Electric Vehicle: A Hierarchical Deep Reinforcement Learning Approach

An Overview of Demand Response in Smart Grid and Optimization Techniques for Efficient Residential Appliance Scheduling Problem

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