چكيده به لاتين
In recent years, energy savings, especially in homes, have become an important issue due to rising fuel prices and the tightening of environmental pollution laws. Also, as the planet warms and climate change becomes a real threat, efforts to reduce energy consumption and reduce energy waste in homes and offices have become increasingly important. Saving will not only help users financially and reduce their costs throughout the year, but will also have a positive impact on the environment. On the other hand, today, with the increasing progress of science and technology, new and efficient technologies have emerged. One of these technologies is the Internet of Things. In the Internet of Things, every object can be identified, accessed, and even remotely controlled through the Internet platform and by an Internet Protocol address. Among the most widely used sciences of the day, we can mention the science of optimization and related algorithms, which play an effective role in reducing energy consumption. The purpose of this study is to study the energy systems of homes and obtain the best optimization system based on selected algorithms that can be implemented on the IoT platform and framework. After testing and comparing different methods, the energy system Optimized and selected to advance the research results. Compared algorithms are: genetic algorithm, particle swarm algorithm, ant colony algorithm and .... This comparison was done by MATLAB software. In this comparison, 1000 replications of the program with an initial population of 30 were performed. The results show that the system is 73 minutes faster than the candle and butterfly algorithm in achieving the specified response in the air conditioning system every morning to reach the specified response range. In an electric water heater system, this saves time compared to the ant colony, 52 minutes per day. For the hybrid car, the frog mutation algorithm is selected, which is 79 minutes faster than genetics. For dishwashers, washing machines and tumble dryers, the whale algorithm responds 51 minutes a day faster than genetics, and finally for the storage battery system, the best algorithm is the firefly with 33 minutes less run time than the mutant. The frog acts. These figures will be significant in the year.
