US 12,271,170 B1
AI-based real-time energy management system for high energy efficiency in logistics centers based on accuracy of forecast data
Jaeyoung Oh, Seoul (KR); Kyunghoon Jang, Seoul (KR); Gilhwan Cha, Seoul (KR); Jaehong Yoo, Seoul (KR); Jaehyun Yoo, Seoul (KR); and Kyungsu Choi, Seoul (KR)
Assigned to KOREA CONFORMITY LABORATORIES, Seoul (KR); and ALGORIGO INC., Seoul (KR)
Filed by Korea Conformity Laboratories, Seoul (KR); and Algorigo Inc., Seoul (KR)
Filed on Nov. 26, 2024, as Appl. No. 18/960,729.
Claims priority of application No. 10-2023-0188956 (KR), filed on Dec. 21, 2023.
Int. Cl. G05B 19/042 (2006.01)
CPC G05B 19/042 (2013.01) [G05B 2219/2639 (2013.01)] 8 Claims
OG exemplary drawing
 
1. An energy management system for enhancing energy efficiency in a logistics center based on accuracy of forecast data, comprising:
a data collection server configured to collect data related to energy management of the logistics center; and
an energy management server configured to manage energy within the logistics center using the collected data,
wherein the energy management server is configured to:
predict future power demand and future external temperature by using historical external temperature data and historical power usage data included in the collected data,
determine a difference value of the future power demand by using a difference between the predicted external temperature at a current point in time and an actual external temperature included in the collected data,
predict power demand by correcting the future power demand using the difference value of the future power demand,
input time-specific cloud forecasts included in the collected data into a pre-trained AI model to cluster time-specific solar radiation, and predict solar power generation based on the clustered time-specific solar radiation,
calculate prediction accuracy by comparing the predicted power demand and the predicted solar power generation with the collected data,
update a first peak power to a second peak power by applying the prediction accuracy to the first peak power,
calculate a Mean Squared Error (MSE) between the clustered time-specific solar radiation and actual solar radiation,
calculate accuracy of solar power generation prediction based on comparison of the MSE with a preset reference value, and
determine the second peak power based on the first peak power, accuracy of power demand prediction, the accuracy of the solar power generation prediction, and a preset ratio constant.