Due to the nonlinearity and nonstationary of hydropower market data, a novel hybrid learning paradigm is proposed to predict hydropower consumption, by incorporating firefly algorithm (FA) into least square support vector regression (LSSVR), i.e., FA-based LSSVR model. In the novel model, the powerful and effective artificial intelligence (AI) technique, i.e., LSSVR, is employed to forecast hydropower consumption. Furthermore, a promising AI optimization tool, i.e., FA, is especially introduced to address the crucial but difficult task of parameters determination in LSSVR (e.g., hyper and kernel function parameters). With the Chinese hydropower consumption as sample data, the empirical study has statistically confirmed the superiority of the novel FA-based LSSVR model to other benchmark models (including existing popular traditional econometric models, AI models and similar hybrid LSSVRs with other popular parameter searching tools), in terms of level and directional accuracy. The empirical results also imply that the hybrid FA-based LSSVR learning paradigm with powerful forecasting tool and parameters optimization method can be employed as an effective forecasting tool for not only hydropower consumption but also other complex data.