-
Notifications
You must be signed in to change notification settings - Fork 2
service_prediction
Thorsten Zoerner edited this page Feb 1, 2024
·
4 revisions
- prediction.epoch_of_day()
- prediction.x_epochs()
graph TD
prediction.epoch_of_day --> prediction.normalize
prediction.x_epochs --> prediction.normalize
- prediction.epoch_of_day()
- loadprofile_model.find()
- prediction.normalize()
- prediction.epoch_of_day()
graph TD
prediction.epoch_of_day --> prediction.epoch_of_day
prediction.epoch_of_day --> loadprofile_model.find
prediction.epoch_of_day --> prediction.normalize
prediction.epoch_of_day --> prediction.epoch_of_day
Simple machine learing based prediction of next epochs consumption The method operates by examining the consumption history of a meter during past epochs. An epoch in this context is defined as a distinct period during which energy consumption is measured and associated with a dynamic tariff. By assessing how much electricity was consumed during these previous intervals, the method constructs a model to forecast future usage. Limitation: Requires a sequence of readings without gaps.
- prediction.x_epochs()
- loadprofile.load()
- prediction.normalize()
- prediction.normalize()
- prediction.x_epochs()
graph TD
prediction.x_epochs --> prediction.x_epochs
prediction.x_epochs --> loadprofile.load
prediction.x_epochs --> prediction.normalize
prediction.x_epochs --> prediction.normalize
prediction.x_epochs --> prediction.x_epochs