Machine Readable

Here at Halcyon we're attuned to how both the volume and frequency of new energy information publishing demand an alerts and notifications system. After Halcyon’s platform ingests documents from federal, state, and regional entities, we can send alerts about new filings from specific entities (like the California Public Utilities Commission or the Federal Energy Regulatory Commission) and on new specific types of documents (such as proposed regulatory decisions, or the signing of a power plant...

There is a healthy debate about the impacts of artificial intelligence on infrastructure, the global power grid, and business productivity. We at Halcyon welcome the discussion, which is earnest, data-driven, thoughtful and constructive. This week, I thought it worth reviewing the state of AI challenges, its impact on productivity, and why we need to remember that building AI infrastructure is not the same as what we build on top of it.

Energy asset developers have information needs in multiple timelines. Some needs are nearly instantaneous, such as price(s) and volume(s), and are ‘structured’ in data parlance. They are numbers, in forms easily accessed via download or API, and easily integrate into other structured data or models.

There are many challenges with energy transition information today. Verity is one: is the data that you have collected correct, and from a definitive source? Validity is another: is the data that you are seeking the right data for your strategic or analytical purposes?