Access to an increasing amount of data opens for the application of machine learning models to predict the best combination of models and strategies for bidding of hydro power in a de-regulated market for any given day.

This data-set describe the historical performance-gap of two given bidding strategies over several years (2016-2018). Data from two different bidding strategies are presented in the data-set. The first is bidding the expected volume. The expected volumes are found by deterministic optimization against forecasted price and inflow using the SHOP software, and are submitted as fixed hourly bids to the Nord Pool power exchange. The second strategy is stochastic bidding. The stochastic model is based on the deterministic method, but allows for a stochastic representation of inflow to the reservoir and day-ahead market prices. SHOP is a software tool for optimal short-term hydropower scheduling developed by SINTEF Energy Research, used by many hydropower producers in the Nordic market.

The total performance-gap for the two strategies in the data-set are calculated as the difference between the optimum value for the relevant bidding date and the value of the investigated strategy. A high number for indicate poor performance.

In addition, a set of of relevant variables accessible prior to bidding have been collected and are published in the data-set. Realized- and prognosed prices in the data-set are prices for the NO2 area in Nordpool. The reservoir and watervalue in the data-set are associated with a river system located in south-western Norway