Energy storage such as battery and thermal energy storage is an effective approach to shift building peak load and alleviate grid stress at a building cluster level. However, due to the heterogeneous performance of different types of storage (e.g., response speed, charge/discharge efficiency and rate, storage capacity) and highly diversified energy use patterns of individual buildings, the multi-energy storage should be properly selected and optimally designed for individual buildings to achieve effective load shifting. The optimal deployment of multi-energy storage at a cluster level is a challenging optimization problem due to the nonlinear dynamic performance of the multi-energy storage and the high dimensionality as a result of a large number of buildings. To tackle the challenges, this study proposes a data-driven surrogate optimization method that optimally deploys multi-energy storage at a cluster level to minimize the building cluster energy bill under demand response programs.
In this study, the deployment of distributed Internet of Things (IoT) acoustic sensors using a real-time scenario is rigorously analysed. Simulation results indicate that in an ideal situation, with no obstacles, the proposed sensor count remained constant with a 99% efficiency, even with an increase in the count of dangerous events. We observed a rise in the count of sensors with the increase in obstacles and the total area under study. Our study also confirms that any increment beyond the obtained optimal count of the sensors will not necessarily affect efficiency. Moreover, the total detection rate verified that the optimal placement of the acoustic sensors requires fewer sensors than any randomly placed sensors, to achieve a 99% efficiency. To our knowledge, the proposed research work in this paper specific to acoustic surveillance using a non-traditional IoT cloud architecture has not been previously investigated in the literature.