Improving End-Use Load Modeling Using Machine Learning and Smart Meter Data

Thayer, Brandon; Engel, Dave; Chakraborty, Indrasis; Schneider, Kevin P.; Ponder, Leslie; Fox, Kevin M.

doi:10.24251/hicss.2020.373

Cited by 2 publications

(3 citation statements)

References 13 publications

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“…Time series modeling has historically been a key area of academic research, forming an integral part of applications in topics such as climate modeling (Mudelsee, 2019), biological sciences (Stoffer and Ombao, 2012), and medicine (Topol, 2019), as well as commercial decision making in retail (Böse et al, 2017), finance (Andersen et al, 2005), and net-load consumption for customer (Thayer et al, 2020) to name a few. While traditional methods have focused on parametric models informed by domain expertise such as autoregressive (AR) (Box and Jenkins, 1976), exponential smoothing (Winters, 1960;Gardner, 1985), or structural time series models (Harvey, 1990), modern machine learning methods provide a means to learn temporal dynamics in a purely data-driven manner (Ahmed et al, 2010).…”

Section: Related Workmentioning

confidence: 99%

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Device Classification for Industrial Control Systems Using Predicted Traffic Features

Chakraborty

Kelley

Gallagher

2022

Front. Comput. Sci.

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To achieve a secure interconnected Industrial Control System (ICS) architecture, security practitioners depend on accurate identification of network host behavior. However, accurate machine learning based host identification methods depends on the availability of significant quantities of network traffic data, which can be difficult to obtain due to system constraints such as network security, data confidentiality, and physical location. In this work, we propose a network traffic feature prediction method based on a generative model, which achieves high host identification accuracy. Furthermore, we develop a joint training algorithm to improve host identification performance compared to separate training of the generative model and the classifier responsible for host identification.

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Section: Related Workmentioning

confidence: 99%

“…Prediction accuracy is defined in the literature(Thayer et al, 2020) based on several performance metrics, such as root mean square error, mean bias error etc.…”

mentioning

confidence: 99%

Device Classification for Industrial Control Systems Using Predicted Traffic Features

Chakraborty

Kelley

Gallagher

2022

Front. Comput. Sci.

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“…The machine learning-based smart meter system contributes effectively to the Ambient Assistive Living (AAL) area for detecting daily living activities [2]. Machine learning has been used with smart meters for improving end-user load modeling machine learning [3]. AI also combined with edge computing and edge analytics in smart power meters [4].…”

Section: Introductionmentioning

confidence: 99%

HSIC Bottleneck Based Distributed Deep Learning Model for Load Forecasting in Smart Grid With a Comprehensive Survey

et al. 2020

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Load forecasting is a vital part of smart grids for predicting the required electrical power using artificial intelligence (AI). Deep learning is broadly used for load forecasting in the smart grid using the artificial neural network (ANN). Generally, computing the deep learning in the smart grid requires massive data aggregation or centralization and significant computational time. This paper presents a survey of deep learning-based load forecasting techniques from 2015 to 2020. This survey discusses the studies based on their deep learning techniques, Distributed Deep Learning (DDL) techniques, Back Propagation (BP) based works, and non-BP based works in the load forecasting process. Consequent to the survey, it was determined that data aggregation dependency would be beneficial for reducing computational time in load forecasting. Therefore, a conceptual model of DDL for smart grids has been presented, where the HSIC (Hilbert-Schmidt Independence Criterion) Bottleneck technique has been incorporated to provide higher accuracy.

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Improving End-Use Load Modeling Using Machine Learning and Smart Meter Data

Cited by 2 publications

References 13 publications

Device Classification for Industrial Control Systems Using Predicted Traffic Features

Device Classification for Industrial Control Systems Using Predicted Traffic Features

HSIC Bottleneck Based Distributed Deep Learning Model for Load Forecasting in Smart Grid With a Comprehensive Survey

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