Dynamic weather-based scheduling for achieving energy savings in factories

Abstract

This study optimized heating, ventilation, and air-conditioning (HVAC) operations in an industrial setting on the basis of weather forecasts to achieve energy savings and reduce carbon emissions without the need for infrastructural modifications. Weather forecasts obtained from the Weather Research and Forecasting Model for an electronics factory in Northern Taiwan were integrated into a Python-based scheduling system for adjusting HVAC parameters dynamically for scheduling optimization. Compared with the original scheduling plan, the optimized scheduling plan, which was implemented from July 2023 to June 2024, resulted in electricity consumption reduction of 213,395 kWh, carbon emissions reduction of 105.4 metric tons, an energy-savings rate of 9.67%, and total cost savings of approximately 27,100 USD. Although cooling demand increased, the adopted dynamic scheduling approach resulted in optimized chiller load adjustments, strategic ice melting, and tailored HVAC operations for different seasons; thus, energy efficiency was enhanced. Seasonal variations in weather forecast accuracy were noted, with errors being larger in summer. Such variations affected HVAC operations. These findings highlight the necessity of applying statistical corrections to weather forecasts to better align them with actual conditions before they are used for HVAC scheduling. Overall, this study indicates the potential of leveraging weather forecasts for sustainable energy management and underscores the importance of reducing forecast errors for enhancing HVAC control. By integrating weather forecasts into real-time operations, factories can not only optimize daily energy usage but also plan power allocation for production processes three to seven days in advance.