技术
- 功能应用 - 计算机化维护管理系统 (CMMS)
- 传感器 - 电表
适用行业
- 电网
- 可再生能源
适用功能
- 物流运输
- 维护
用例
- 资产健康管理 (AHM)
- 预测性维护
服务
- 云规划/设计/实施服务
- 数据科学服务
关于客户
AES 是一家全球运营的可再生能源公司,为私人、公共和政府组织生产和分配能源。该公司连续九年被评为全球最具商业道德企业之一,并因对电力行业进步的贡献而荣获爱迪生电气研究所 (EEI) 的爱迪生奖。 AES 已成功将其业务从化石燃料转向可再生能源,并通过成功的数字和人工智能转型加速这一变革。该公司运营着众多风力涡轮机发电厂,拥有并运营着众多全球水力发电厂,并利用超过一百万个智能电表来监控能源消耗。
挑战
AES 是一家领先的可再生能源公司,面临着加速大规模向可再生能源转型的挑战。这种业务转型需要数字和人工智能转型,以更好地预测和优化可再生能源的能源输出、预测故障并优化负载分配。该公司必须处理风力涡轮机预测性维护、水力发电厂能源招标策略和智能电表的复杂性。风力涡轮机有许多运动部件,需要承受恶劣的环境,其维护成本特别高且耗时。该公司还需要优化其能源招标策略,以最大限度地提高水力发电厂的收入。此外,该公司必须管理超过一百万台智能电表,这些电表有时会出现维护问题或被滥用。
解决方案
AES 首先制定人工智能策略,然后与业务合作伙伴密切合作,以确定最佳的起始用例。他们选择 H2O AI Cloud 来加速他们制作最先进的 AI 模型并将其投入生产的能力。对于风力涡轮机的预测性维护,他们构建了大约十几个模型,准确率超过 90%。该举措节省了成本并提供了更一致的电力输送,从而为多个风力涡轮机组件添加了更多传感器并实现了油采样自动化。对于能源竞价策略,AES 使用 H2O AI Cloud 制作和运行的模型来帮助设定发电厂的每日价格和金额。对于智能电表,该团队根据智能电表的数据创建了人工智能,以预测实际的维护问题与误用问题。该公司还在扩大其预测性维护工作,包括使用无人机图像来评估叶片损坏情况,并在太阳能发电场上进行工作以识别性能不佳的面板和清洁计划。
运营影响
数量效益
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