This study aims to develop a novel forecasting system that optimizes linear time-series with nonlinear machine learning models to identify the historical pattern of regional energy consumption. The linear time-series model, Seasonal AutoRegressive Integrated Moving Average (SARIMA), was applied to simulate the linear component, while the least squares support vector regression (LSSVR) model was used to capture the nonlinear component of time series energy data and combine the linear and nonlinear components. Several optimization algorithms were investigated using high-dimension mathematical benchmark functions to compare their outcomes for applying in the proposed forecasting system. Wilcoxon rank-sum test and convergence graphs for Jellyfish Search (JS) and other parameter-less algorithms (ie, Teaching-Learning-Based Optimization (TLBO) and Symbiotic Organisms Search, SOS algorithms) indicate that the JS optimizer finds the best global solutions in mathematical tests. Then, three real energy time-series datasets from a regional transmission organization that coordinates the movement of wholesale electricity were used to evaluate various forecasting methods. The analytical results confirm that the proposed system, JS-LSSVR(SARIMA, LSSVR), can predict multi-step ahead time series energy consumption with higher accuracy than the linear model (ie, SARIMA), nonlinear model (ie, LSSVR), hybrid model (ie, JS-LSSVR), hybrid systems (ie, TLBO-LSSVR(SARIMA, LSSVR) and TLBO-LSSVR(SARIMA, LSSVR)), and prior studies. Numerical experiments show that the JS-LSSVR(SARIMA, LSSVR) system can forecast energy consumption 1 week ahead efficiently (from 9.8 to 21.4 seconds on average). Notably, the proposed technique only requires 4 inputs vs 7 to 32 inputs of other models in the literature. Thus, a power company can apply the proposed system to forecast energy consumption to efficiently dispatch regional energy capacity and keep the electricity supply and demand in balance for residential buildings in sustainable cities.