Uncertainty Estimation in Power Consumption of a Smart Home Using Bayesian LSTM Networks

Mostafa Zaman, Sujay Saha, Nasibeh Zohrabi, Sherif Abdelwahed

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Smart cities and smart houses have recently seen a rise in power and energy consumption. Household energy demand is likewise ameliorating with population growth and enhanced living standards. For this reason, it is vital to predict power consumption demand for a smart home. Predicting the power consumption is a multivariate time series issue with multiple factors impacting power usage. The variables fluctuate depending on how the user uses them, impacting the amount of utilized power consumption. Predicting power consumption and estimating the uncertainty of these values provide a good idea to the residents and planners to prepare for future capacity or load forecasting demand. Time series forecasting relies heavily on accurate uncertainty estimations. Classical time series models are often combined with probabilistic formulations to estimate uncertainty. Adding exogenous variables and fine-tuning such models are complex tasks. As a response to the current resurrection of Long Short Term Memory networks, we provide a Bayesian deep neural network model that enables time series prediction and uncertainty estimation concurrently. We consider a real-world dataset (individual household electric power consumption data set) to test the effectiveness of the proposed approach.

Original languageEnglish (US)
Title of host publication2022 IEEE International Symposium on Advanced Control of Industrial Processes, AdCONIP 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages120-125
Number of pages6
ISBN (Electronic)9781665471749
DOIs
StatePublished - 2022
Event7th IEEE International Symposium on Advanced Control of Industrial Processes, AdCONIP 2022 - Vancouver, Canada
Duration: Aug 7 2022Aug 9 2022

Publication series

Name2022 IEEE International Symposium on Advanced Control of Industrial Processes, AdCONIP 2022

Conference

Conference7th IEEE International Symposium on Advanced Control of Industrial Processes, AdCONIP 2022
Country/TerritoryCanada
CityVancouver
Period8/7/228/9/22

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering
  • Control and Optimization

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