Investigation of Energy Management and Optimization Using Penalty Based Reinforcement Learning Algorithms for Textile Industry

Energy, today, has become one of the most important factors in almost every industry domain. The energy reserves of a country largely influences its growth rate. Particularly in case of the textile industry, this contributes 2% to the world GDP. This paper aims at demonstrating through experimentation, how reinforcement learning, a branch of machine learning, can be adopted to help manage and optimize energy usage in a cotton textile mill. The models explored in this paper are established as nonlinear programming mode with the experiment result showing the performance of the penalty based reinforcement-learning algorithms in comparison with results obtained in [1] per annum. It has been calculated that there is a reduction of approximately 1.3% using Thompson Sampling and UCB algorithms, and 1.26% in energy consumption using random selection.