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A Prediction Mechanism of Energy Consumption in Residential Buildings Using Hidden Markov Model

Author

Listed:
  • Israr Ullah

    (Computer Engineering Department, Jeju National University, Jeju-si 63243, Korea)

  • Rashid Ahmad

    (Computer Science Department, COMSATS Institute of Information Technology, Attock Campus 43600, Pakistan)

  • DoHyeun Kim

    (Computer Engineering Department, Jeju National University, Jeju-si 63243, Korea)

Abstract

Internet of Things (IoT) is considered as one of the future disruptive technologies, which has the potential to bring positive change in human lifestyle and uplift living standards. Many IoT-based applications have been designed in various fields, e.g., security, health, education, manufacturing, transportation, etc. IoT has transformed conventional homes into Smart homes. By attaching small IoT devices to various appliances, we cannot only monitor but also control indoor environment as per user demand. Intelligent IoT devices can also be used for optimal energy utilization by operating the associated equipment only when it is needed. In this paper, we have proposed a Hidden Markov Model based algorithm to predict energy consumption in Korean residential buildings using data collected through smart meters. We have used energy consumption data collected from four multi-storied buildings located in Seoul, South Korea for model validation and results analysis. Proposed model prediction results are compared with three well-known prediction algorithms i.e., Support Vector Machine (SVM), Artificial Neural Network (ANN) and Classification and Regression Trees (CART). Comparative analysis shows that our proposed model achieves 2.96 % better than ANN results in terms of root mean square error metric, 6.09 % better than SVM and 9.03 % better than CART results. To further establish and validate prediction results of our proposed model, we have performed temporal granularity analysis. For this purpose, we have evaluated our proposed model for hourly, daily and weekly data aggregation. Prediction accuracy in terms of root mean square error metric for hourly, daily and weekly data is 2.62, 1.54 and 0.46, respectively. This shows that our model prediction accuracy improves for coarse grain data. Higher prediction accuracy gives us confidence to further explore its application in building control systems for achieving better energy efficiency.

Suggested Citation

  • Israr Ullah & Rashid Ahmad & DoHyeun Kim, 2018. "A Prediction Mechanism of Energy Consumption in Residential Buildings Using Hidden Markov Model," Energies, MDPI, vol. 11(2), pages 1-20, February.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:2:p:358-:d:130115
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    References listed on IDEAS

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    7. Sehrish Malik & DoHyeun Kim, 2018. "Prediction-Learning Algorithm for Efficient Energy Consumption in Smart Buildings Based on Particle Regeneration and Velocity Boost in Particle Swarm Optimization Neural Networks," Energies, MDPI, vol. 11(5), pages 1-21, May.
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