Deep Learning-Based Socio-Demographic Information Identification From Smart Meter Data

Wang, Yi; Chen, Qixin; Gan, Dahua; Yang, Jingwei; Kirschen, Daniel S.; Chen, Kang

doi:10.1109/tsg.2018.2805723

“…current and power factor, with date and time stamp [26,27]. The Learn part is the machine learning process which could learn the most efficient power factor at particular time using deep learning [28,29]. The Do part is the actual implementation of what we have learned from the timeseries database to lower the electricity cost and more efficient electrical appliance operation.…”

Section: Iot Framework Analysismentioning

confidence: 99%

Design of power factor meter using internet of things for power factor improvement, remote monitoring and data logging

Gunawan

¹

,

Anuar

²

,

Kartiwi

³

et al. 2020

IJEECS

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Nowadays, many residential and commercial buildings that used electricity needs to take care the power factor to avoid penalty from the utility companies. A power factor that is close to one provides a good indicator for the overall power quality. Therefore, power factor improvement plays a significant role to reduce electricity consumption and more efficient system operation. In this paper, the design of power factor meter using Internet of Things will be discussed. Voltage and current sensors outputs were interfaced to Arduino, in which the real power and apparent power were calculated to determine the power factor. Results showed the effectiveness of our proposed device in measuring power factor. Moreover, the measured data points were logged in an SD card and can be accessed by computer with Matlab graphical user interface (GUI). In addition, IoT framework analysis for smart meter which can provide power factor improvement, remote monitoring, and data logging was further discussed in this paper.

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“…Ref. [42,43] are interested in determining household characteristics or customers information based on temporal load profiles of household electricity demand. They use sophisticated deep learning algorithm for the first one and more classical tools for the second one.…”

Section: Multiscale Modeling and Forecastingmentioning

confidence: 99%

Scalable Clustering of Individual Electrical Curves for Profiling and Bottom-Up Forecasting

Auder

¹

,

Cugliari

²

,

Goude

³

et al. 2018

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Smart grids require flexible data driven forecasting methods. We propose clustering tools for bottom-up short-term load forecasting. We focus on individual consumption data analysis which plays a major role for energy management and electricity load forecasting. The first section is dedicated to the industrial context and a review of individual electrical data analysis. Then, we focus on hierarchical time-series for bottom-up forecasting. The idea is to decompose the global signal and obtain disaggregated forecasts in such a way that their sum enhances the prediction. This is done in three steps: identify a rather large number of super-consumers by clustering their energy profiles, generate a hierarchy of nested partitions and choose the one that minimize a prediction criterion. Using a nonparametric model to handle forecasting, and wavelets to define various notions of similarity between load curves, this disaggregation strategy gives a 16% improvement in forecasting accuracy when applied to French individual consumers. Then, this strategy is implemented using R-the free software environment for statistical computing-so that it can scale when dealing with massive datasets. The proposed solution is to make the algorithm scalable combine data storage, parallel computing and double clustering step to define the super-consumers. The resulting software is openly available.

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“…In general, exogenous variables are used to forecast electricity consumptions, but some authors focus on the reverse. [41] and [42] are interested in determining household characteristics or customers information based on temporal load profiles of household electricity demand. They use sophisticated deep learning algorithm for the first one and more classical tools for the second one.…”

Section: Multiscale Modeling and Forecastingmentioning

confidence: 99%

Scalable Clustering of Individual Electrical Curves for Profiling and Bottom-up Forecasting

Auder¹,

Cugliari²,

Goude³

et al. 2018

Preprint

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Smart grids require flexible data driven forecasting methods. We propose clustering tools for bottom-up short-term load forecasting. We focus on individual consumption data analysis which plays a major role for energy management and electricity load forecasting. The two first sections are dedicated to the industrial context and a review of individual electrical data analysis. We are interested in hierarchical time-series for bottom-up forecasting. The idea is to disaggregate the signal in such a way that the sum of disaggregated forecasts improves the direct prediction. The 3-steps strategy defines numerous super-consumers by curve clustering, builds a hierarchy of partitions and selects the best one minimizing a forecast criterion. Using a nonparametric model to handle forecasting, and wavelets to define various notions of similarity between load curves, this disaggregation strategy applied to French individual consumers leads to a gain of 16% in forecast accuracy. We then explore the upscaling capacity of this strategy facing massive data and implement proposals using R, the free software environment for statistical computing. The proposed solutions to make the algorithm scalable combines data storage, parallel computing and double clustering step to define the super-consumers.Keywords: Clustering; Forecasting; Hierarchical Time-Series; Individual Electrical Consumers; Scalable; Short Term; Smart Meters; Wavelets Industrial contextEnergy systems are facing a revolution and many challenges. On the one hand, electricity production is moving to more intermittency and complexity with the increase of renewable energy and the development of small distributed production units such as photovoltaic panels or wind farms. On the other hand, consumption is also changing with plug-in (hybrid) electric vehicles, heat pumps, the development of new technologies such as smart phones, computers, robots that often come with batteries. To maintain the electricity quality, energy stakeholders are developing smart grids (see [1,2] This results into new opportunities such as local optimisation of the grid, demand side management and smart control of storage devices. Exploiting the smart grid efficiently requires advanced data analytics and optimisation techniques to improve forecasting, unit commitment, and load planning at different geographical scales. Massive data sets are and will be produced as Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted:

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Deep Learning-Based Socio-Demographic Information Identification From Smart Meter Data

Cited by 174 publications

References 34 publications

Design of power factor meter using internet of things for power factor improvement, remote monitoring and data logging

Design of power factor meter using internet of things for power factor improvement, remote monitoring and data logging

Scalable Clustering of Individual Electrical Curves for Profiling and Bottom-Up Forecasting

Scalable Clustering of Individual Electrical Curves for Profiling and Bottom-up Forecasting

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