The operation of a renewable-cascade hydropower hybrid plant involves wind and solar power forecasting, electricity price prediction, and hydropower scheduling. Conventional approaches optimize forecasting accuracy and then perform scheduling based on predicted values; however, forecasting errors affect scheduling profits in a nonlinear and asymmetric manner, so accuracy-oriented forecasts do not necessarily maximize operational benefits. To address this issue, a value-oriented forecasting-decision integrated optimization method with variable-weight ensemble prediction is proposed. An upper-level model is developed for wind power, photovoltaic power, and electricity price forecasting, with Bayesian optimization used to determine value-oriented weights. A lower-level cascade hydropower scheduling model is constructed considering pumping-generation exclusivity, water balance, and unit constraints, forming a bi-level optimization framework. Case studies show that the proposed method outperforms accuracy-oriented approaches in coupled multi-source forecasting and cascade scheduling scenarios, enhancing prediction-decision consistency in high-value periods, reducing deviation penalties and scheduling mismatches, and improving overall operational benefits.