Artificial neural network based controller for home energy management considering demand response events

Ahmed, Maytham S.; Mohamed, Azah; Shareef, Hussain; Homod, Raad Z.; Ali, Jamal Abd

doi:10.1109/icaees.2016.7888097

Cited by 51 publications

(25 citation statements)

References 13 publications

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“…The AI scheduler and controller is incorporated with a programming code that mocks the human nerves system [7]. ANN consists of input, output, and hidden layers (in some cases) as well as data processing algorithms which model the nonlinearity of the systems and mimic the human brain, put to use as a smart scheduler and controller to schedule smart home appliances [8]. ANN-based controller and scheduling models can be used for prediction and controlling instead of other conventional simulation-based methods to predict and control the cost of electricity.…”

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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“…Reference [16] (Category I) focused on the uncertainty issue of effective participation period, which was one of the critical boundary parameters. Reference [17] (Category II) estimated a better initial ON/OFF status of responsive appliances. While [18] (Category III) applied reinforcement learning for optimal control, [19] designed a hybrid framework to integrate neural network and optimization models together.…”

Section: Taxonomymentioning

confidence: 99%

“…Similar technique was also applicable in demand response. Reference [17] used a neural network to estimate the optimal ON/OFF status of home appliances, which could be regarded as an efficient warm start setting.…”

Section: B Category 2 Optimization Option Selectionmentioning

confidence: 99%

Review of Learning-Assisted Power System Optimization

Ruan,

Zhong,

Zhang

et al. 2020

Preprint

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Machine learning, with a dramatic breakthrough in recent years, is showing great potential to upgrade the power system optimization toolbox. Understanding the strength and limitation of machine learning approaches is crucial to answer when and how to integrate them in various power system optimization tasks. This paper pays special attention to the coordination between machine learning approaches and optimization models, and carefully evaluates to what extent such data-driven analysis may benefit the rule-based optimization. A series of typical references are selected and categorized into four kinds: the boundary parameter improvement, the optimization option selection, the surrogate model and the hybrid model. This taxonomy provides a novel perspective to understand the latest research progress and achievements. We further discuss several key challenges and provide an in-depth comparison on the features and designs of different categories. Deep integration of machine learning approaches and optimization models is expected to become the most promising technical trend.

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“…[58] Big Data techniques are used to gather and process the information from multiple residential users to include them in DR programs. [59] An application of artificial neural networks is proposed in the management of domestic energy considering the events of DR.…”

Section: References Highlighted Aspectsmentioning

confidence: 99%

A Review and Analysis of Trends Related to Demand Response

et al. 2018

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This paper provides a review and analysis of trends related to demand response (DR). The authors have considered six different topics for the analysis of DR trends: Users, Network Services, Markets, Complementary Programs and Distributed Energy Resources (DER). A brief summary of the consulted articles is included and the behavior of the different DR trend-related topics is shown up to the year 2017 and their projections for 2020. As a result, the characterization of the main DR topics is obtained as well as its current and future trends. Based on the results of the study, it is concluded that the topic of complementary programs is a trendsetter for current trends and it is expected that there is a future change of focus towards the users and new services.

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Artificial neural network based controller for home energy management considering demand response events

Cited by 51 publications

References 13 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

Review of Learning-Assisted Power System Optimization

A Review and Analysis of Trends Related to Demand Response

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