Energy Meter for Smart Home Purposes

Slanina, Zdenek; Docekal, Tomas

doi:10.1007/978-3-319-68324-9_7

Cited by 9 publications

(2 citation statements)

References 11 publications

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“…The rationale of these standardized automation systems are to provide comfort to physically disabled and elderly people. With regard to comfort, the control elements of building automation can be adapted to the SH user's requirements [1] [2]. Home computerization and mechanization by using computer and phone technologies manage systems to reduce human labor.…”

Section: Introductionmentioning

confidence: 99%

The Enhanced Speech Recognition in Automated Home Lighting System using Adaptive Time-Frequency Domain Noise Removal Algorithm Filter

Siddik,

Wan Husin,

Muniandy

2024

IJORAS

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Numerous studies have explored speech recognition performance in Smart Home environments. However speech recognition accuracy diminishes when voice commands are captured in noisy areas of the home. This study aims to enhance speech recognition performance in such noisy environments. Instead of relying on remote control signals, a Bluetooth system is employed for short-range wireless communication to identify speech commands. Various sound levels are measured in decibels (dB) at different distances using the Smart Noise Application. A filter algorithm with Adaptive Filtering is used to minimize unwanted noise. The algorithm uses Adaptive Time-Frequency Domain Noise Removal (TFDNR) to mitigate background noise. Overall, the integrated system comprising Smartphone, Bluetooth, Arduino microcontroller, and noise detection software exhibits improved performance compared to previous studies, highlighting its potential for seamless smart home automation.

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Section: Introductionmentioning

confidence: 99%

The Enhanced Speech Recognition in Automated Home Lighting System using Adaptive Time-Frequency Domain Noise Removal Algorithm Filter

Siddik,

Wan Husin,

Muniandy

2024

IJORAS

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“…This experimental off-grid platform was developed by a team from the ENET Centre, which is located on the campus of VŠB-Technical University of Ostrava. The main purpose of this experimental off-grid platform is to simulate households with common household appliances, thus contributing toward the development of modern green technologies, such as vehicle to grid technology [5,6] or research on prediction and optimisation [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Forecasting of Power Quality Parameters Based on Meteorological Data in Small-Scale Household Off-Grid Systems

et al. 2022

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Off-grid power systems are often used to supply electricity to remote households, cottages, or small industries, comprising small renewable energy systems, typically a photovoltaic plant whose energy supply is stochastic in nature, without electricity distributions. This approach is economically viable and conforms to the requirements of the European Green Deal and the Fit for 55 package. Furthermore, these systems are associated with a lower short circuit power as compared with distribution grid traditional power plants. The power quality parameters (PQPs) of such small-scale off-grid systems are largely determined by the inverter’s ability to handle the impact of a device; however, this makes it difficult to accurately forecast the PQPs. To address this issue, this work compared prediction models for the PQPs as a function of the meteorological conditions regarding the off-grid systems for small-scale households in Central Europe. To this end, seven models—the artificial neural network (ANN), linear regression (LR), interaction linear regression (ILR), quadratic linear regression (QLR), pure quadratic linear regression (PQLR), the bagging decision tree (DT), and the boosting DT—were considered for forecasting four PQPs: frequency, the amplitude of the voltage, total harmonic distortion of the voltage (THDu), and current (THDi). The computation times of these forecasting models and their accuracies were also compared. Each forecasting model was used to forecast the PQPs for three sunny days in August. As a result of the study, the most accurate methods for forecasting are DTs. The ANN requires the longest computational time, and conversely, the LR takes the shortest computational time. Notably, this work aimed to predict poor PQPs that could cause all the equipment in off-grid systems to respond in advance to disturbances. This study is expected to be beneficial for the off-grid systems of small households and the substations included in existing smart grids.

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