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

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.

Prediction-Learning Algorithm for Efficient Energy Consumption in Smart Buildings Based on Particle Regeneration and Velocity Boost in Particle Swarm Optimization Neural Networks

“…A recent study [11] performed on energy consumption prediction also used residential buildings data from Seoul, South Korea. In this paper, the proposed model is based on the hidden Markov model based on an algorithm.…”

“…The importance of building energy consumption predictions in order to make the most informed real-time decisions has been highlighted in many recent studies; and some contributions have come up with prediction models through data-driven or machine-learning approaches [9,10]. According to [11], these predictions are equally important and effective for both the user of a residential building and power-generating companies. These predictions can also help us optimize electricity usage at peak hours, enabling the user to be aware of his energy usage patterns.…”

“…A user can use these systems to take decisions e.g., to switch between energy sources (e.g., electrical to solar), according to his requirements. In [11], it has also been shown that in South Korea commercial and residential sectors cover around 40% of national energy consumption. Therefore, predicting the energy consumption will not just help a smart home user to effectively utilize resources, it will also have a growing impact on the national economy as well.…”

Occupant Comfort Management Based on Energy Optimization Using an Environment Prediction Model in Smart Homes

“…For forecasting of time series, historical data are required as the inputs of the model, to predict future data in the time sequence. A variety of ML algorithms have been proposed for forecasting, such as the artificial neural network (ANN), hidden Markov model (HMM), support vector machine (SVM), and many other ML algorithms [46]. The ANNs have been used for solving nonlinear methods, which can be trained to learn the relationship for recognizing patterns from given data, through the functions of self-organizing, data-driven, self-study, self-adaptive, and associated memory [47].…”

Game theory based distributed clustering approach to maximize wireless sensors network lifetime

“…This approach is divided in two phases. The first one consists to select dynamically the clusters and their clusters heads based on sensors energy and location (Ullah et al, 2018). In the second phase, the sensor node aggregates the sensing messages by a compression method to save sensor's energy and memory and decided to stay out of the communication to charge its battery in the sleeping mode or to enter the market game and send the message to its neighbors.…”