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.
Highlights
Electrical energy is a very important but scarce resource available to humans
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
We have evaluated the performance of other algorithms (ANN, SVM, and Classification and Regression Trees (CART)) for daily and weekly predictions and their results were lower than proposed Hidden Markov Models (HMM) scheme, not reported in the paper
Summary
Electrical energy is a very important but scarce resource available to humans. It has become an integral part of our lives and we cannot think of a world without electricity. To meet the rising worldwide demand for energy, efforts are made in two main directions: (a) increase electric power production capacity by exploiting existing and renewable energy sources and solutions (b) effectively utilize existing resources of energy by avoiding energy wastage and unnecessary usage. Both of these approaches are important and complement each other. Improper utilization of resources results in energy wastage which can Energies 2018, 11, 358; doi:10.3390/en11020358 www.mdpi.com/journal/energies
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