「英中对照」AutomatedBuildings编辑委员会探讨：当建筑运行数据出错时AI应当如何应对？

From the AutomatedBuildings Editorial Board来自 AutomatedBuildings 编辑委员会

AI in Real Buildings: When the Data Doesn’t Agree
From the AutomatedBuildings Editorial Board

Real buildings rarely provide perfect information.

Sensors drift, occupancy changes, forecasts move, equipment is overridden and systems sometimes disagree. Some data is missing, while other data looks perfectly reasonable even when it is not.真实建筑中的 AI：当数据不一致时
来自 AutomatedBuildings 编辑委员会

真实建筑很少能提供完美的信息。

传感器会漂移，人员占用情况会变化，预报会改变，设备会被强制接管，各系统有时也会出现分歧。有些数据存在缺失，而另一些数据即使并不合理，看起来也完全合理。

As AI takes on a larger role in analysing and acting on building data, the important test is how it performs under those normal operating conditions.随着 AI 在分析建筑数据并据此采取行动方面发挥更大作用，重要的考验是它在这些正常运行条件下的表现如何。

Take weather as a simple example. A system might use a forecast to decide when to pre-cool a building or shift a load. The forecast may change, local conditions may differ, occupancy may not be what was expected, and a sensor inside the building may be giving a believable reading that does not accurately represent the space.以天气作为一个简单例子。系统可能利用预报来决定何时对建筑进行预冷或转移负荷。预报可能发生变化，当地条件可能有所不同，人员占用情况可能不及预期，而建筑内的传感器可能给出可信的读数，但该读数并不能准确反映相应空间。

The system then has to decide what to do with information that does not line up neatly.随后，系统必须决定如何处理这些无法完全吻合的信息。

Can it recognise the conflict? Can it seek another source, compare the information with previous behaviour, reduce its confidence or adjust the decision? At what point should it fall back to a known operating condition or ask someone to intervene?它能否识别冲突？能否寻找其他来源，将信息与先前行为进行比较，降低其置信度或调整决策？应在何时回退到已知运行状态，或请求人工介入？

Those are important questions for any system expected to act autonomously.对于任何预期自主行动的系统而言，这些都是重要问题。

Real buildings will always contain some uncertainty, so the answer cannot simply be to wait until every input is perfect. The more useful question is whether the system can recognise uncertainty and respond appropriately when it encounters it.真实建筑中总会存在一定的不确定性，因此答案不能只是等待每一项输入都完美无缺。更有意义的问题是，系统能否识别不确定性，并在遇到它时作出适当响应。

It also changes what should count as proof.这也改变了什么才应被视为证据。

A successful demonstration shows that something worked in that instance. A pilot provides more evidence. Deployment across different buildings, seasons and operating conditions provides more again.一次成功的演示表明某件事在该特定情况下奏效。试点可提供更多证据。而在不同建筑、季节和运行条件下进行部署，则会提供更多证据。

Trust develops over time, as owners and operators see how a system performs when conditions change, equipment develops problems, sensors drift, overrides are introduced and the many exceptions of day-to-day building operation begin to appear.信任会随时间逐步建立，因为业主和运营方会看到，在条件变化、设备出现问题、传感器漂移、出现强制接管，以及建筑日常运行中的诸多异常开始显现时，系统的表现如何。

AI may also be able to do things an operator cannot easily do, such as compare large amounts of information continuously, detect inconsistencies across systems or identify patterns that would otherwise be difficult to see. Those capabilities could be valuable, but they still need to be demonstrated in operation.AI 也许还能完成运行人员不易做到的事情，例如持续比较大量信息、检测各系统之间的不一致，或识别原本难以发现的模式。这些能力可能很有价值，但仍需在运行中得到验证。

Owners and operators should be able to understand enough about the system’s behaviour to answer some practical questions. What information did it rely on? Did it recognise uncertainty? How did it respond when sources disagreed? What happened as a result?业主和运营方应当能够充分理解系统的行为，从而回答一些实际问题。它依赖了哪些信息？它是否识别出了不确定性？当不同来源不一致时，它如何响应？结果发生了什么？

Trust in an autonomous system will come partly from the technology itself, but also from the evidence accumulated through real use.对自主系统的信任，一部分来自技术本身，另一部分则来自实际使用中积累的证据。

As AI moves from analysing building data to influencing or making operational decisions, that evidence will matter more.随着 AI 从分析建筑数据发展到影响或制定运行决策，这类证据将更加重要。

The question is not simply whether a system can perform when the conditions are right. It is how well it responds when they are not.问题不仅仅在于系统能否在条件适宜时正常运行，而在于条件不佳时它的响应能力如何。

If you are already using AI in live building operations, we are interested in the exceptions. When inputs conflicted, a sensor was wrong or reality did not match the model, what did the system actually do? We would like to hear the examples that worked, and the ones that did not.如果您已经在建筑的实际运行中使用 AI，我们对其中的异常情况很感兴趣。当输入相互冲突、传感器出错，或实际情况与模型不匹配时，系统究竟做了什么？我们希望听到行之有效的例子，也希望听到不奏效的例子。

Join us in Chicago at AHR2027 to continue the conversation. 欢迎加入我们，在 芝加哥参加 AHR2027继续交流。 

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