Parameter Optimization of Refrigeration Chiller by Machine Learning

Abstract

The implementation of machine learning in a chiller system provides several benefits. It can improve energy efficiency by optimizing chiller operation based on predicted load requirements. It can enhance system reliability and reduce maintenance costs by detecting and diagnosing faults in advance. Furthermore, it can enable data-driven decision-making, enabling operators to make informed choices based on accurate predictions and insights. This implementation aims to leverage machine learning techniques to optimize the performance and energy efficiency of a chiller system. Chiller systems are widely used in various industries for cooling purposes, and their efficient operation is critical to reducing energy consumption and operational costs. By employing machine learning algorithms, this implementation aims to analyze historical data, understand patterns, and develop predictive models to optimize chiller system performance. The implementation process involves several steps. First, historical data from the chiller system, including sensor measurements, operating parameters and energy consumption, is collected and preprocessed. The data is then split into training and testing sets. Next, suitable machine learning algorithms, such as regression, classification, or time-series forecasting models, are selected based on the specific goals and requirements of the chiller system. Overall, this implementation demonstrates the potential of machine learning to optimize chiller system performance, reduce energy consumption, and improve operational efficiency. By leveraging historical data and advanced analytics, machine learning can play a crucial role in transforming traditional chiller systems into intelligent, adaptive, and energy-efficient cooling solutions.