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Large space buildings, such as airport terminal buildings, consume high energy due to the cooling demand; especially in hot arid climates; to achieve passenger thermal comfort. Evaluation of thermal environment and energy analysis inside airports is an important issue, in terms of reducing energy and controlling indoor environments. This work aims to analyze electrical energy consumption and the indoor environment of the terminal building at Assiut International Airport, Egypt as a case study. Indoor environmental conditions, and energy consumption with HVAC systems, were monitored inside five multiple functional zones within the terminal building (Departure hall, Public area, Duty-free hall, Gate, and Arrival hall) during summer (August 2017). The indoor environmental parameters monitored include: temperature, relative humidity, and illuminance. The results indicate the HVAC system consumed the largest portion (70%) of the overall building electricity demand, and the Energy Use Intensity (EUI) of ATZ terminal building is 300 kWh/m2/year. Also, indoor temperatures were found to be within the acceptable range of comfort, with a high energy consumption of 6,000,000 kWh for the year 2017. A significant reduction of monthly energy consumption was achieved based on increasing HVAC set temperature from 25 °C to 27 °C; a reduction equal to 24.5% of total energy consumption during the hot months. The results of this study help search for sustainable solutions for reducing energy consumption and improving passenger thermal comfort inside the terminal building (phase 2).

In this paper, a novel framework is presented for chaotic image encryption. The proposed method is based on integrating multiple chaotic maps (e.g., logistic, tent, quadratic, cubic, and Bernoulli) to generate more robust chaotic maps in order to increase the security and privacy needed by applying variable keys. The latter are generated by computing the sine square logistic map and are then applied to generate the chaotic maps employed in our framework. For this, we have performed many experiments to achieve the best period for each chaotic map in which it performed the best encryption. Here, we combine multiple chaotic maps to get a new map that works well when X ∈ [0, 1]. For using a chaotic map in the encryption process, it was necessary to find a way to choose the best of those chaotic maps for encryption. This selection was done with the lowest value for the correlation factor because the smaller value of correlation has an impression of good encryption. We have also noted a clear difference in the influence of one of these maps on some pictures from the others. We chose one of those maps according to the correlation value for each encoding process and compared them. Then, we used a chaotic map of the best of these values for encryption and decryption. Numerical results on various gray images showed the robustness of the proposed method to encrypt and decrypt the images based on the evaluation using different performance analyses. We compared our methods against other well-known approaches, e.g., circular mapping, S-boxes, and S-box with Arnold transform. Our pipeline outperforms those methods. Moreover, our results documented that the proposed scheme has an excellent security level with very low correlation coefficients and good information entropy.