Clearly it is important to reduce the current power consumption trajectory associated with the build out of AI infrastructure. As reported NERC has identified the growth of AI demand on the power system as an emerging issue. Load forecasting to meet the instantaneous, hourly and day ahead demand for power across North America based on historical information has become very accurate. Recognizing and quantifying how weather conditions impact both local and regional control areas has become very predictable as it applies to power demand. The importance to accurately forecast the demand (in all time frames) has been magnified by the growth/reliance of intermittent/variable solar and wind generation  on the North American grid. They have become a much larger component of the generation mix. There are additional serious issues associated with these Inverter Based Resources (including battery storage) which are beyond the scope of this post. 

Long story short the emergence of AI load on the power system is introducing a variable load which is not accurately predictable. Not yet. It will in fact require the anylitical power of AI to solve these forcasting issues (IMO). How ironic is that.

Here is what CHAT GPT says about this topic.

The North American instantaneous power demand of AI can be influenced by event-driven factors, but it is not entirely event-driven. Here are a few points to consider:

Event-Driven Workloads: Certain events, such as product launches, significant AI-driven projects, or increased usage due to real-time analytics, can lead to spikes in power demand. For instance, during peak usage times for specific applications or during major AI events, the demand for power can increase.

Scheduled Tasks: Many AI tasks may be scheduled for specific times, such as data processing or model training, contributing to fluctuations in demand based on the timing of these tasks.

User Behavior: In consumer-facing AI applications, user behavior can lead to event-driven demand. For example, if many users access a service simultaneously, it may drive an increase in power usage.

Static vs. Dynamic Demand: Overall, while there are event-driven components that affect AI power demand, there are also baseline power requirements for continuous operations. Predicting instantaneous power demand can be complex, incorporating both planned activities and reactive adjustments to changing workloads.

In summary, while there are event-driven impacts on power demand for AI, other consistent factors also play a significant role.