Optimization of energy consumption in smart homes using firefly algorithm and deep neural networks

Electronic gadget advancements have increased the demand for IoT-based smart homes as the number of connected devices grows rapidly. The most prevalent connected electronic devices are smart environments in houses, grids, structures, and metropolises. Smart grid technology advancements have enabled smart structures to cover every nanosecond of energy use. The problem with smart, intelligent operations is that they use a lot more energy than traditional ones. Because of the growing growth of smart cities and houses, there is an increasing demand for efficient resource management. Energy is a valuable resource with a high unit cost. Consequently, authors are endeavoring to decrease energy usage, specifically in smart urban areas, while simultaneously ensuring a consistent terrain. The objective of this study is to enhance energy efficiency in intelligent buildings for both homes and businesses. For the comfort indicator ("thermal, visual, and air quality"), three parameters are used: temperature, illumination, and CO2. A hybrid rule-based Deep Neural Network (DNN) and Fire Fly (FF) algorithm are used to read the sensor parameters and to operate the comfort indication, as well as optimize energy consumption, respectively. The anticipated user attributes contributed to the system's enhanced performance in terms of the ease of use of the smart system and its energy usage. When compared to traditional approaches in expressions of Multi View with 98.23%, convolutional neural network (CNN) with 99.17%, and traffic automatic vehicle (AV) with 98.14%, the activities of the contributed approach are negligibly commanding

The integration of pumped hydro storage systems into PV microgrids in rural areas

Photovoltaic (PV) systems are popular in rural areas because they provide low cost and clean electricity for homes and irrigation systems. The primary challenge of PV systems is their intermittent nature. The typical solution is storing energy in batteries; however, they are expensive and possess a short lifespan. This research proposes a new type of pumped hydro storage (PHS) which can be implemented as an alternative to batteries. The components of the system are modelled to consider losses of the system accurately. The mathematic model developed in this project assists the management system to make more efficient decisions. The proposed storage is integrated into a farmhouse that has a PV pumping system where economic aspects of implementing the proposed storage is investigated. The integration of the proposed PHS into a microgrid needs a management system to make this system efficient and 3 cost-effective. This research proposes a multi-stage management system to schedule and control the microgrid components for optimal integration of the PHS. The designed management system is able to manage the pump, turbine, and irrigation time on real-time taking into account both present and future conditions of the microgrid. This study investigates the technical aspects of the proposed system. The PHS and the management system are tested experimentally in a setup installed at smart energy laboratory at Edith Cowan university. Data used in this project are real data collected in the laboratory in order to have a realistic analysis. Economic analysis is done in different sizes with different conditions. Results indicate that the proposed system has a short payback period and a large lifetime benefit, featuring as a cost-effective and sustainable energy storage system for use in rural areas. Video abstract: https://youtu.be/VuyEvHRY7W