An IoT-Based Prediction Technique for Efficient Energy Consumption in Buildings

Shidrokh Goudarzi, Mohammad Hossein Anisi, Seyed Ahmad Soleymani, Masri Ayob, Sherali Zeadally

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

Today, there is a crucial need for precise monitoring and prediction of energy consumption at the building level using the latest technologies including Internet of Things (IoT) and data analytics to determine and enhance energy usage. Data-driven models could be used for energy consumption prediction. However, due to high non-linearity between the inputs and outputs of energy consumption prediction models, these models need improvement in terms of accuracy and robustness. Therefore, this work aims to predict energy usage for the optimum outline of building-extensive energy distribution strategies based on a lightweight IoT monitoring framework. To calculate accurate energy consumption, an enhanced hybrid model was developed based on Auto-Regressive Integrated Moving Average (ARIMA) and Imperialist Competitive Algorithm (ICA). The parameters of the ARIMA model were optimized by adapting the ICA technique that improved fitting accuracy while preventing over-fitting on the acquired data. Then, Exponentially Weighted Moving Average (EWMA) was applied to monitor the predicted values. The proposed AIK-EWMA hybrid model was assessed based on the actual power consumption data and validated using mathematical tests. As compared to previous works, the findings revealed that the hybrid model could accurately predict power consumption for green building automation applications.

Original languageEnglish
Pages (from-to)2076-2088
Number of pages13
JournalIEEE Transactions on Green Communications and Networking
Volume5
Issue number4
DOIs
StatePublished - Dec 1 2021

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Keywords

  • Artificial intelligence
  • auto-regressive integrated moving average
  • building energy consumption
  • imperialist competitive algorithm
  • prediction

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Computer Networks and Communications

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