Optimizing Machine Learning Algorithms for Temperature Forecasting and Energy Management Systems

Cloud data centers strive to provide efficient, scalable, and reliable computing resources while addressing challenges related to security, privacy, cost, and more. Their objectives include delivering value to users through flexibility, availability, innovation, and environmental responsibility. These Data centers have observed tremendous growth, utilizing massive energy for computer resources and cooling systems. A combination of time series regression models, machine learning, and other optimization techniques may be used to develop more robust and adaptive resource management policies for complex data center environments. Additionally, continuous monitoring and feedback loops are essential to ensure that the models remain accurate and effective as conditions evolve. This paper studied existing machine learning algorithms and methods for temperature forecasting and adequate energy consumption for data centers. The investigations have been analyzed that the impact of energy consumption on computer resource workloads significantly improved, and Virtual Machines scheduling algorithms will optimize the forecasting of thermal effects. This paper also compares the regression model with the Machine Learning algorithms, which will provide the best methodology for forecasting thermal effects and improving efficient energy.