FAQ
What data is used as inputs?
We have over 1,300 data series from which to select as inputs from data published by the ISO including scheduled generation, transmission outages, regional load, and reserve capacity. We also include commodities futures data from ICE, as well as more robust weather data from several vendors. Some of our most accurate forecasts use load forecasts, weather forecasts (temperature, wind, humidity, solar irradiance), scheduled and forecasted generation, previous days imports and exports, previous days congestion, planned/forced outages on plants and lines, time variables, price of natural gas, heat rates, ancillary services procurement, tie line flows and liquidity and pricing data from ICE
How does a trial work?
1.Personalized Demo & Trial Setup a.After submitting your request, our team will schedule a personalized demo. b.If eligible, we'll confirm your trading nodes and customize the trial forecasts accordingly 2. Customized Two-Week Trial a.You'll receive two weeks of tailored forecasts specific to your selected nodes. b.Access forecasts via our intuitive web-based platform, with optional limited API access. 3.Review Historical Performance a.You'll have access to historical forecast performance data, enabling detailed accuracy evaluation. b.We recommend reviewing our [Quick Start Guides] and [Tutorials] to effectively assess our forecasts. 4.Analyze and Evaluate a.Prepare to analyze a significant volume of data—this ensures a comprehensive understanding of forecast accuracy and usability. b.Our documentation will assist you in integrating the data into your existing analysis workflow. c.Link to sample code, documentation, etc. 5.Post-Trial Follow-up a.Upon trial completion, we'll follow up directly to gather your feedback, discuss your experience, and outline next steps should you choose to continue
Will the models improve over time?
Yes – we expect every target to be more accurate over time. We believe this for three reasons: 1) Models are automatically retrained weekly using more data with the same parameters 2) Our cloud-based architecture allows us to constantly train new models in the cloud using a slightly different dataset (for example, by adding in outages for a key backbone), and comparing those results against the current top performer 3) Forecasting power markets is our raison d’etre, and we’re constantly tweaking our engine and rolling out improvements to all forecasts
I want to use the forecasts for automatic battery dispatch – do you have an API?
Yes we have an easy-to-use API that can provide our most recent forecasts, all of which can be run at a frequency specified by the client. See our docs under "Resources" for more information.
Why would we use Enertel instead of doing it ourselves?
There are some clients with a high technical aptitude for price forecasting, and we certainly understand that companies wish to perform some modelling in-house. We don’t believe they’re mutually exclusive, given the importance of forecasting for managing assets. With that said, there are a couple advantages to also procuring forecasts from a vendor: - Yes Energy Access: Your traders are already in Yes Energy on a regular basis, so there’s no change to their day-to-day process - Economies of Scale: Forecasting vendors consistently invest their time into improving forecasts because small incremental improvements have a compound effect over many models, vs. in-house models that may be left for weeks without retraining or improvements - Attrition: In-house models can sometimes rely on a few people within the company who may leave the company. A forecasting vendor provides reliability and long-term accuracy gains
How do you evaluate accuracy?
We rigorously track the accuracy of every forecast generated by the Enertel platform to make decisions on training new models and promoting model architectures. Typically, clients care about the accuracy of our ‘best’ forecasts for a given timeseries, so we provide an easy-to-use dashboard that lets them evaluate the forecast accuracy for every time a given model was run and the forecasts it generated.