Improving the operational effectiveness of hydropower systems is becoming more relevant considering the shift to renewable energy sources and the rising social, financial, and environmental costs associated with the construction of new hydropower facilities. This challenge is interdisciplinary as it involves technical, managerial, and institutional aspects. This paper focuses on a technical aspect, more specifically on the relationship between the quality of short-term ensemble streamflow forecasts and the energy produced by a hydropower system. A secondary objective is to measure the contribution of both the meteorological and structural uncertainties on the energy output. To achieve this, a numerical experiment comprising multiple sets of hydrologic ensemble forecasts of different quality and a suite of reservoir optimization models is developed for a case study in Canada (the hydropower system of the Gatineau River basin). These ensemble forecasts are processed by the short-term reservoir operation model in rolling-horizon mode over a planning period of 6 years. Each day, the short-term optimization model seeks to maximize the energy output over the 14-day forecast lead time considering the expected future value of the system derived from a midterm optimization model. The relationship between hydropower generation and common statistical scores characterizing the ensemble forecasts indicates that although there is a link between the quality of the forecasts and the energy production, it is not a one-to-one causal relationship. Our results also show that the diversity of hydrological models is beneficial to the production of energy, indicating that the diversity of model structures compensates the deficiencies of individuals models and adds value to the forecast.