Optimizing Energy Management and Reducing Peak Demand in Buildings using Forecasting Models, Machine Learning, Factorial Design, and Least Squares Method

This research focuses on optimizing energy management and reducing peak demand in buildings through the integration of forecasting models, machine learning, factorial design, and the least squares method. By leveraging data-driven techniques, the study aims to predict energy consumption patterns and identify the factors that influence peak demand. Machine learning algorithms are employed to enhance the accuracy of energy usage forecasts, while factorial design aids in analyzing the interactions between various factors affecting energy consumption. Additionally, the least squares method is utilized to refine model predictions and optimize energy efficiency strategies. The goal is to develop a comprehensive approach that minimizes energy consumption during peak periods, leading to cost savings and improved sustainability in building energy management systems.