Developing Takagi-Sugeno Fuzzy Model-Based Estimator for Short-Term Load Forecasting

단기부하예측을 위한 Tskagi-Sugeno 퍼지 모델 기반 예측기 설계

This paper presents a new design methods of the short-term load forecasting system (STLFS) using the data mining. The proposed predictor takes form of the convex combination of the linear time series predictors for each inputs. The problem of estimating the consequent parameters is formulated by the convex optimization problem, which is to minimize the norm distance between the real load and the output of the linear time series estimator, The problem of estimating the premise parameters is to find the parameter value minimizing the error between the real load and the overall output. Finally, to show the feasibility of the proposed method, this paper provides the short-term load forecasting example.