A Review on the Efficiency Increment in a Power System Using Smart Grid Technologies

Çolak, Alperen Mustafa; Kaplan, Orhan

doi:10.1109/icsmartgrid52357.2021.9551257

Cited by 7 publications

(2 citation statements)

References 45 publications

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“…HEMS now features a variety of capabilities, from demand management to peak shaving and load control [3], because of fast technological advancement. Companies have improved their offers, and the sector has grown tremendously, with more than 50 companies now selling HEMS products throughout Europe [4].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Energy Consumption For Smart Homes Using Fused Machine-Learning Technique

AlZaabi¹,

Shaalan²,

Ghazal³

et al. 2023

Computers, Materials &Amp; Continua

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Energy is essential to practically all exercises and is imperative for the development of personal satisfaction. So, valuable energy has been in great demand for many years, especially for using smart homes and structures, as individuals quickly improve their way of life depending on current innovations. However, there is a shortage of energy, as the energy required is higher than that produced. Many new plans are being designed to meet the consumer's energy requirements. In many regions, energy utilization in the housing area is 30%-40%. The growth of smart homes has raised the requirement for intelligence in applications such as asset management, energy-efficient automation, security, and healthcare monitoring to learn about residents' actions and forecast their future demands. To overcome the challenges of energy consumption optimization, in this study, we apply an energy management technique. Data fusion has recently attracted much energy efficiency in buildings, where numerous types of information are processed. The proposed research developed a data fusion model to predict energy consumption for accuracy and miss rate. The results of the proposed approach are compared with those of the previously published techniques and found that the prediction accuracy of the proposed method is 92%, which is higher than the previously published approaches.

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Section: Introductionmentioning

confidence: 99%

Intelligent Energy Consumption For Smart Homes Using Fused Machine-Learning Technique

AlZaabi¹,

Shaalan²,

Ghazal³

et al. 2023

Computers, Materials &Amp; Continua

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“…After then, HEMS developed to incorporate more features including load control, peak shaving, and demand management 2 . More than 50 businesses in Europe sell HEMS products, indicating the enormous development and potential of this industry 3 . The anticipated surge in worldwide power needs in the future is attributed to economic development, transformative shifts, and extended use of electric power 4 .…”

Section: Introductionmentioning

confidence: 99%

Analysis and comparison of household energy consumption based on deep learning

Zheng

2023

Second International Conference on Sustainable Technology and Management (ICSTM 2023)

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Effective prediction of energy consumption can greatly assist in the planning and allocation of energy resources and thus contribute to energy conservation efforts. To effectively predict energy consumption, this paper applies deep learning techniques to construct analytical models. Convolutional neural networks (CNN), long and short-term memory (LSTM) and recurrent neural networks (RNN) are simultaneously constructed as evaluation models for analysis. Each model was trained and tested on a dataset representative of the energy consumption of the device or system. The training and validation loss curves of the models were observed over a series of epochs to assess their learning ability. The results showed that the LSTM models outperformed the CNN and RNN models in all performance metrics. The superior learning ability is demonstrated due to the LSTM exhibiting a substantial learning curve and stable validation loss. Findings derived from empirical studies reveal that LSTM models exhibit proficiency in predicting energy usage in a reliable manner. Such an insight is instrumental in progressing the design and evolution of devices and systems that demonstrate improved energy efficiency. Looking ahead, there is scope to further refine hybrid LSTM models. This not only offers the potential for heightened prediction accuracy, but also extends significant contributions to the discipline of energy conservation and efficiency.

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Blockchain-Enabled Edge Computing and Sensor Integration for IoT-based Smart Grid Systems – Implementation and Energy Management: Morocco Case Study

Bounab,

Fali,

Hmidou

et al. 2023

2023 7th IEEE Congress on Information Science and Technology (CiSt)

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A Review on the Efficiency Increment in a Power System Using Smart Grid Technologies

Cited by 7 publications

References 45 publications

Intelligent Energy Consumption For Smart Homes Using Fused Machine-Learning Technique

Intelligent Energy Consumption For Smart Homes Using Fused Machine-Learning Technique

Analysis and comparison of household energy consumption based on deep learning

Blockchain-Enabled Edge Computing and Sensor Integration for IoT-based Smart Grid Systems – Implementation and Energy Management: Morocco Case Study

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