In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique is to maintain a balance between user comfort and energy requirements, such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gaps in the literature are due to advancements in technology, the drawbacks of optimization algorithms, and the introduction of new optimization algorithms. Further, many newly proposed optimization algorithms have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. Detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes.

Highly efficient and remarkable selective acetone conversion to isopropanol has been achieved via a heterogeneous catalytic hydrogenation of acetone by NaBH4 in the presence of semihollow palladium nanoparticles (PdNPs) grown on ITO substrate. PdNPs with high surface defect grown on an indium tin oxide (ITO) surface were prepared via a simple immersion of the substrate into a solution containing K2PdCl6, sodium dodecyl sulphate (SDS), and formic acid for 2 h at room temperature. The sample showed remarkably high heterogeneous catalytic efficiency by producing 99.8% of isopropanol within 6 min using only 0.28 μg of PdNPs on the ITO surface. The present system exhibits heterogenenous catalytic hydrogenation efficiency 1 × 10(6) time higher than using the conventional Raney Ni system.

Energy is considered the most costly and scarce resource, and demand for it is increasing daily. Globally, a significant amount of energy is consumed in residential buildings, i.e., 30–40% of total energy consumption. An active energy prediction system is highly desirable for efficient energy production and utilization. In this paper, we have proposed a methodology to predict short-term energy consumption in a residential building. The proposed methodology consisted of four different layers, namely data acquisition, preprocessing, prediction, and performance evaluation. For experimental analysis, real data collected from 4 multi-storied buildings situated in Seoul, South Korea, has been used. The collected data is provided as input to the data acquisition layer. In the pre-processing layer afterwards, several data cleaning and preprocessing schemes are applied to the input data for the removal of abnormalities. Preprocessing further consisted of two processes, namely the computation of statistical moments (mean, variance, skewness, and kurtosis) and data normalization. In the prediction layer, the feed forward back propagation neural network has been used on normalized data and data with statistical moments. In the performance evaluation layer, the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE) have been used to measure the performance of the proposed approach. The average values for data with statistical moments of MAE, MAPE, and RMSE are 4.3266, 11.9617, and 5.4625 respectively. These values of the statistical measures for data with statistical moments are less as compared to simple data and normalized data which indicates that the performance of the feed forward back propagation neural network (FFBPNN) on data with statistical moments is better when compared to simple data and normalized data.

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Amorphous silicon (a-Si:H) based solar cells are highly interesting in the context of hybrid (i.e. photovoltaic/ thermal) solar energy conversion. First, their large area capability and the variety of possible substrate materials permit to apply a-Si:H PV modules directly on the surface of conventional heat collectors at low cost. Further, the low temperature coefficient of a-Si:H cells (0.1%/K) allows operation of a-Si:H solar modules at temperatures as high as 100°C without substantial power loss. We focus on the thermal performance of such hybrid collectors based on a-Si:H cells, with emphasis on a ZnO coat on top of the solar cell. ZnO can be "tuned" to absorb the infrared part of the sunlight and, at the same time, its emission coefficient for heat-radiation is nearly as low as that of optimized selective surfaces used in thermal collectors. We propose a collector structure with a high potential for the thermal conversion efficiency while maintaining a high electrical conversion efficiency.