H7EC-BL OMRON

Accurate forecasting of cooling load demand can help operators effectively reduce emissions while ensuring a suitable temperature in the building,and has become a key factor in improving building energy efficiency.This competition takes the start-stop combination and operating parameters of the refrigeration unit of a large commercial building as a specific scenario.The participating teams are required to develop a cooling load demand prediction model and a free path selection algorithm in this scenario to achieve the most realistic temperature prediction and the most energy-saving.chiller scheduling.With years of deep cultivation and practice in the field of smart buildings and building energy efficiency,the Schneider Electric team consists of 1 Schneider Electric energy consultant,2 Schneider Electric algorithm development engineers and 1 equipment technology expert from Swire Group.It passed the multi-dimensional evaluation of semantic AI application level,innovation ability,model design and data analysis solutions,and successfully won the gold medal in the open group of the competition..

准确的冷负荷需求预测能够帮助运营方在保证建筑内适宜温度的同时有效减排，成为提高建筑能效的关键要素。此次大赛以大型商用建筑的制冷机组的启停组合及运行参数为具体场景，要求参赛队伍开发该场景下的冷负荷需求预测模型及自由路径选择算法，实现最贴近实际的温度预测及最节能的冷机调度。凭借多年来对智能楼宇及建筑能效领域的深耕及实践，由1名施耐德电气能源咨询顾问，2名施耐德电气算法开发工程师和1名来自太古集团的设备工艺专家组成的施耐德电气参赛团队，高水平通过了语义AI应用水平、创新能力、模型设计以及数据分析解决方案等多维度评估，成功摘取大赛公开组金奖，同时凭借在建筑领域AI算法的能力获评联合承造-卓越人工智能领袖大奖。