技术
- 分析与建模 - 机器学习
- 网络与连接 - 无线接入网
适用行业
- 建筑物
- 零售
适用功能
- 设施管理
用例
- 楼宇自动化与控制
- 减废预测
服务
- 系统集成
关于客户
Arbejdernes Landsbank 是一家丹麦股票上市零售银行,拥有 70 家分行。该银行拥有多元化的建筑组合,包括较旧的和较新的建筑。其中一些建筑物拥有运行良好的技术设施,这些设施是集中或分散的,并由建筑物管理系统(BMS)控制。然而,并非所有楼宇自动化都按预期运行,导致能源效率严重低下。该银行致力于能源优化,并制定了雄心勃勃的气候战略,其中包括投资新技术以降低能源消耗。
挑战
丹麦零售银行 Arbejdernes Landsbank 的 70 家分行面临严重的能源效率低下问题。该银行的建筑组合由较旧和较新的建筑混合而成,拥有一些由建筑管理系统 (BMS) 控制的运行良好的技术设施。然而,在分析其分支机构的能源消耗后发现,并非所有楼宇自动化都按预期运行。卡伦堡 Bredgade 的分行被确定为能源性能最差的建筑之一。建筑物内的能耗突然增加,闲置能耗从约 2 千瓦上升到略高于 6 千瓦。该银行有超过三分之二的时间处于关闭状态,良好和不良能源绩效之间 4 kW 的差异导致了严重的能源浪费。
解决方案
该银行求助于人工智能 (AI) 来识别和纠正能源效率低下的问题。人工智能确定了基本负载消耗的增加,并帮助银行的能源经理确定了节能情况。问题出现在卡伦堡分行通风系统的控制中。一旦发现操作不良,技术人员不需要新技术或花费很多时间来纠正问题。优化了通风系统的控制,基本负荷消耗回落至建筑物的最佳水平。作为雄心勃勃的气候战略的一部分,该银行还投资了新型节能通风电机,以进一步降低能耗。人工智能还帮助记录实施后的能源改进。
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