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.

The advancements in electronic devices have increased the demand for the internet of things (IoT) based smart homes, where the connecting devices are growing at a rapid pace. Connected electronic devices are more common in smart buildings, smart cities, smart grids, and smart homes. The advancements in smart grid technologies have enabled to monitor every moment of energy consumption in smart buildings. The issue with smart devices is more energy consumption as compared to ordinary buildings. Due to smart cities and smart homes’ growth rates, the demand for efficient resource management is also growing day by day. Energy is a vital resource, and its production cost is very high. Due to that, scientists and researchers are working on optimizing energy usage, especially in smart cities, besides providing a comfortable environment. The central focus of this paper is on energy consumption optimization in smart buildings or smart homes. For the comfort index (thermal, visual, and air quality), we have used three parameters, i.e., Temperature (°F), illumination (lx), and CO ₂ (ppm). The major problem with the previous methods in the literature is the static user parameters (Temperature, illumination, and CO ₂ ); when they (parameters) are assigned at the beginning, they cannot be changed. In this paper, the Alpha Beta filter has been used to predict the indoor Temperature, illumination, and air quality and remove noise from the data. We applied a deep extreme learning machine approach to predict the user parameters. We have used the Bat algorithm and fuzzy logic to optimize energy consumption and comfort index management. The predicted user parameters have improved the system’s overall performance in terms of ease of use of smart systems, energy consumption, and comfort index management. The comfort index after optimization remained near to 1, which proves the significance of the system. After optimization, the power consumption also reduced and stayed around the maximum of 15-18wh.

In the smart home environment, efficient energy management is a challenging task. Solutions are needed to achieve a high occupant comfort level with minimum energy consumption. User comfort is measured in terms of three fundamental parameters: (a) thermal comfort, (b) visual comfort and (c) air quality. Temperature, illumination and CO ${}_2$ sensors are used to collect indoor contextual information. In this paper, we have proposed an improved optimization function to achieve maximum user comfort in the building environment with minimum energy consumption. A comprehensive formulation is done for energy optimization with detailed analysis. The Kalman filter algorithm is used to remove noise in sensor readings by predicting actual parameter values. For optimization, we have used genetic algorithm (GA) and particle swarm optimization (PSO) algorithms and performed comparative analysis with a baseline scheme on real data collected for a one-month duration in our lab’s indoor environment. Experimental results show that the proposed optimization function has achieved a $27.32\%$ and a $31.42\%$ reduction in energy consumption with PSO and GA, respectively. The user comfort index was also improved by $10\%$ i.e., from $0.86$ to $0.96$ . GA-based optimization results were better than PSO, as it has achieved almost the same user comfort with $4.19\%$ reduced energy consumption. Results show that the proposed optimization function gives better results than the baseline scheme in terms of user comfort and the amount of consumed energy. The proposed system can help with collecting the data about user preferences and energy consumption for long-term analysis and better decision making in the future for efficient resource utilization and overall profit maximization.

In this paper, we have proposed a learning to prediction based novel approach for improving the accuracy of prediction algorithms in dynamic conditions. The proposed model is composed of two modules, including the prediction module and the learning module. The learning module is responsible to regularly examine the prediction module and tune its performance by assessing its outcomes together with any other external parameters that can affect its performance. In order to determine the effectiveness of the proposed idea, a learning module based on the artificial neural network (ANN) is developed for improving the accuracy of the Kalman filter algorithm. Experimental investigations are conducted in a greenhouse indoor environment to accurately predict indoor climate parameters (temperature, CO2, and humidity) from noisy sensors readings using the Kalman filter algorithm. Among the various components of the Kalman filter algorithm includes a fixed value of R (observation error covariance), which significantly degrades the performance of the Kalman filter algorithm in dynamic conditions. Greenhouse sensor readings are affected by changing external environmental conditions and internal greenhouse actuators operations. The amount of error in current readings is estimated using ANN-based learning modules to update the parameter R in the corresponding Kalman filter module. Performance evaluation of the proposed model based on learning is conducted in two different case studies using average and maximum based error models. Experimental results show that the prediction accuracy of the conventional Kalman filter is significantly improved by proposed learning to prediction scheme.