In this paper, we have proposed a methodology for energy consumption prediction in residential buildings. The proposed method consists of four different layers, namely data acquisition, preprocessing, prediction, and performance evaluation. For experimental analysis, we have collected real data from four multi-storied residential building. The collected data are provided as input for the acquisition layer. In the pre-processing layer, several data cleaning and preprocessing schemes were deployed to remove abnormalities from the data. In the prediction layer, we have used the deep extreme learning machine (DELM) for energy consumption prediction. Further, we have also used the adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) in the prediction layer. In the DELM different numbers of hidden layers, different hidden neurons, and various types of activation functions have been used to achieve the optimal structure of DELM for energy consumption prediction. Similarly, in the ANN, we have employed a different combination of hidden neurons with different types of activation functions to get the optimal structure of ANN. To obtain the optimal structure of ANFIS, we have employed a different number and type of membership functions. In the performance evaluation layer for the comparative analysis of three prediction algorithms, we have used the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE). The results indicate that the performance of DELM is far better than ANN and ANFIS for one-week and one-month hourly energy prediction on the given data.

Energy management in residential buildings has grabbed the attention of many scientists for the last few years due to the fact that the residential sector consumes the highest amount of total energy produced by different energy resources. To manage the energy in residential buildings effectively, an efficient energy control system is required, capable of decreasing the total energy consumption without compromising the user-preferred environment inside the building. In the literature, many approaches have been proposed to achieve the goals of minimizing the energy consumption and maximizing the user preferred comfort by keeping different parameters under consideration, but all these methods face some problems in resolving the issue properly. The bat algorithm is one of the most recently introduced optimization approaches that has drawn the attention of researchers to apply it for solving different types of optimization problems. In this paper, the bat algorithm is applied for energy optimization in residential buildings, which is one of the most focused optimization problems in recent years. Three environmental parameters, namely temperature, illumination and air quality are bat algorithm inputs and optimized values of these parameters are the outputs. The error difference between the environmental parameters and optimized parameters are inputs of the fuzzy controllers which give energy as output which in turn change the status of the concerned actuators. It is proven from the experimental results that the proposed approach has been effectively successful in managing the whole energy consumption management system.

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.

Prediction algorithms enable computers to learn from historical data in order to make accurate decisions about an uncertain future to maximize expected benefit or avoid potential loss. Conventional prediction algorithms are usually based on a trained model, which is learned from historical data. However, the problem with such prediction algorithms is their inability to adapt to dynamic scenarios and changing conditions. This paper presents a novel learning to prediction model to improve the performance of prediction algorithms under dynamic conditions. In the proposed model, a learning module is attached to the prediction algorithm, which acts as a supervisor to monitor and improve the performance of the prediction algorithm continuously by analyzing its output and considering external factors that may have an influence on its performance. To evaluate the effectiveness of the proposed learning to prediction model, we have developed the artificial neural network (ANN)-based learning module to improve the prediction accuracy of the Kalman filter algorithm as a case study. For experimental analysis, we consider a scenario where the Kalman filter algorithm is used to predict actual temperature from noisy sensor readings. the Kalman filter algorithm uses fixed process error covariance R, which is not suitable for dynamic situations where the error in sensor readings varies due to some external factors. In this study, we assume variable error in temperature sensor readings due to the changing humidity level. We have developed a learning module based on ANN to estimate the amount of error in current readings and to update R in the Kalman filter accordingly. Through experiments, we observed that the Kalman filter with the learning module performed better (4.41%–11.19%) than the conventional Kalman filter algorithm in terms of the root mean squared error metric.

Available space in congested cities is getting scarce due to growing urbanization in the recent past. The utilization of underground space is considered as a solution to the limited space in smart cities. The numbers of underground facilities are growing day by day in the developing world. Typical underground facilities include the transit subway, parking lots, electric lines, water supply and sewer lines. The likelihood of the occurrence of accidents due to underground facilities is a random phenomenon. To avoid any accidental loss, a risk assessment method is required to conduct the continuous risk assessment and report any abnormality before it happens. In this paper, we have proposed a hierarchical fuzzy inference based model for under-ground risk assessment. The proposed hierarchical fuzzy inference architecture reduces the total number of rules from the rule base. Rule reduction is important because the curse of dimensionality damages the transparency and interpretation as it is very tough to understand and justify hundreds or thousands of fuzzy rules. The computation time also increases as rules increase. The proposed model takes 175 rules having eight input parameters to compute the risk index, and the conventional fuzzy logic requires 390,625 rules, having the same number of input parameters to compute risk index. Hence, the proposed model significantly reduces the curse of dimensionality. Rule design for fuzzy logic is also a tedious task. In this paper, we have also introduced new rule schemes, namely maximum rule-based and average rule-based; both schemes can be used interchangeably according to the logic needed for rule design. The experimental results show that the proposed method is a virtuous choice for risk index calculation where the numbers of variables are greater.

The Mamdani fuzzy inference method is one of the most important fuzzy logic control (FLC) techniques and has several applications in different fields. Despite its applications, the Mamdani fuzzy inference method has some core issues which still require solutions. The most critical issue is the selection of accurate shape and boundaries of membership functions (MFs) in the universe of discourse. In this work, we introduced a methodology called learning to control (LtC) to resolve the problem. The proposed methodology consisted of two main modules, namely, a control algorithm (CA) module and a learning algorithm (LA) module. In the CA module, the Mamdani FLC method has been used, whereas, in the LA module, we have used the artificial neural network (ANN) algorithm. Inputs into the ANN were the error difference between environmental temperature and the required temperature. The output of the ANN was the MF set to the FLC. Inputs into the fuzzy logic controller (FLC) were the error difference between environmental temperature and required temperature (D), and the output was the required power for the fan actuator. The purpose of the ANN was to tune the MFs of the FLC to improve its efficiency. The proposed learning-to-control method along with the conventional fuzzy logic controller method was applied to the data to evaluate the model’s performance. The results indicate that the proposed model’s performance is far better than that of conventional fuzzy logic techniques.