energy consumption optimization

Nowadays, the optimization of energy consumption in public transportation systems is a serious issue. Since a large part of energy in transportation systems is consumed by subways, a new approach has been proposed for optimal control of a train to reduce energy consumption. The proposed model is based on the Reinforcement Learning algorithm. It is assumed that a train moves between two stations along a line with non-constant gradient, curve, and speed limits. Moreover, the train should complete its journey within a given time interval. The Reinforcement Learning of States, Actions, and Rewards are based on the selected Actions. In the proposed method, the train States are the velocity and position of the train, and the Action is acceleration or coasting motion. Unlike the former techniques, most stages of optimization in this method are offline and implemented only once for any route. Following the formation of the reward matrix, we could use this method in an online form and then the speed profile could be produced at a minimum time. The simulations of the proposed method are implemented in MATLAB and finally compared with those of the Genetic Algorithm.

Considering the worldwide increasing rate of energy consumption and its environmental degradation effects, and considering the use of none-renewable energy sources such as the fossil fuels, consideration of energy management issues has become more important. Given the 40% share of the building industry's total energy consumption, as well as the 80% share of energy consumed during the operation period, of the total energy consumption during the life cycle of the building, attention to the areas of energy management and optimization during the operation period of the buildings can have a major impact on energy efficiency. In this research, through identifying building energy management tools and studying previous studies and assessing the effects of building energy management systems, the economic and environmental impacts of using building energy management systems on the annual energy consumption in an office building in Tehran as a case study has been investigated. The results indicate a 32 percent reduction in the energy consumption and a significant reduction in the release of the environmental pollutants in smart mode compared to the baseline. Moreover, considering the social costs associated with the emitted pollutants as well as the return period, it has been attempted to identify the factors contributing to the economic justification of using smart heating and cooling systems. According to the results, the use of smart energy management systems can be considered as an effective step in optimizing and managing energy consumption in the construction sector.