Previous researches about electrical load time series data forecasting showed that the result was not satisfying. This paper elaborates the enhanced neuro-fuzzy architecture for the same application. The system uses Gaussian membership function (GMF) for Takagi [2] showed that the MSE is not acceptable. For this reason, the same research but with another method, i.e. enhanced Gustafson-Kessel using evolutionary algorithm has evaluated by [3], but still the result is not acceptable. The other researches [4]-[6] also show that the result is not good. Their RMSE are 5.4%. In order to reach RMSE under 5%, the research is continued with different approach.This paper elaborates the implementation of neuro-fuzzy for electrical load time series data forecasting. The proposed method uses the same data as in [1]- [6] so that the methods can be easily compared. According to those researches, the data is from East Java-Bali from September 2005 to August 2007. The neuro-fuzzy architecture will be explored in order to get the optimum architecture. It uses MIMO Takagi-Sugeno type and Levenberg-Marquardt training algorithm to make the training efficient. The architecture of neural network uses feed-forward neural network. The forecasting system uses both short and long time forecasting. Data from