A Prediction Mechanism of Energy Consumption in Residential Buildings Using Hidden Markov Model
AbstractInternet 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
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Ullah, I.; Ahmad, R.; Kim, D. A Prediction Mechanism of Energy Consumption in Residential Buildings Using Hidden Markov Model. Energies 2018, 11, 358.
Ullah I, Ahmad R, Kim D. A Prediction Mechanism of Energy Consumption in Residential Buildings Using Hidden Markov Model. Energies. 2018; 11(2):358.Chicago/Turabian Style
Ullah, Israr; Ahmad, Rashid; Kim, DoHyeun. 2018. "A Prediction Mechanism of Energy Consumption in Residential Buildings Using Hidden Markov Model." Energies 11, no. 2: 358.
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