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" Electricity price forecasting: A review of the state-of-the-art with a look into the future," This implies that it is important to incorporate CSS expectations into the storage hydropower model. It is shown that, if the right mix of power price and CSS expectations is considered, the prediction error of the model is drastically reduced. In this connection, using entropy and machine learning tools, we present a method for embedding this expectations in a model to predict storage hydropower generation, showing that, for some time horizon, expectations on CSS have a greater impact than expectations on power prices. In this paper, we investigate the impact of the variables influencing the choices of price maker producers, and, in particular we study the impact of Clean Spark Spread expectations on storage hydroelectric generation. As the latter is related to a wide range of types of variables, in order to incorporate it in a large-scale prediction model it is important to select the variables that impact most on storage hydropower generation. However, using water in reservoirs represents an opportunity cost, which is related to the evolution of plant production capacity and production profitability. As a result, it provides flexibility to the grid and helps mitigate the short-term production uncertainty that affects most green energy technologies. Storage hydropower generation plays a crucial role in the electric power system and energy transition because it is the most widespread power generation with low greenhouse gas emissions and, moreover, it is relatively cheap to ramp up and down.
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