Improving Energy Efficiency in Climatic Test Chambers with Deep Learning and Absolute Humidity Methods

Climatic test chambers are devices used to simulate environmental conditions for the testing and verification of products in various industries. However, these chambers can consume significant amounts of energy, resulting in high operating costs and environmental impacts. Therefore, the need to optimize the energy efficiency of climatic test chambers while maintaining their performance is becoming increasingly important. In this paper, we will discuss the control method for humidity testing by calculating the use of the LSTM algorithm instead of the classical control method PID to control climatic test chambers to improve energy efficiency and the control method based on absolute humidity instead of relative humidity. In particular, we harness the power of artificial neural networks to reduce energy consumption and improve control of climatic test chambers based on various input parameters such as temperature, humidity, and test duration. By changing the control methods, we aim to increase efficiency and make it more suitable and efficient for smart grid systems.