Renewable Generation (Wind/Solar) and Load Modeling through Modified Fuzzy Prediction Interval. (11th January 2018)

• Record Type: Journal Article
• Authors: Rafique, Syed Furqan; Jianhua, Zhang; Rafique, Rizwan; Guo, Jing; Jamil, Irfan
• Other Names: Poggi Philippe Academic Editor.
• Is Part Of: International journal of photoenergy. Volume 2018(2018)
• Publication Date: 2018-01-11
• Subjects: Photochemistry -- Periodicals; Photobiology -- Periodicals; Chemistry, Physical and theoretical -- Periodicals; Photochimie; Photobiologie; Chimie physique et théorique; Chemistry, Physical and theoretical; Photobiology; Photochemistry; Electronic journals; Periodicals; 541.35
• DOI: 10.1155/2018/4178286
• Languages: English
• ISSNs: 1110-662X

Abstract

Abstract : The accuracy of energy management system for renewable microgrid, either grid-connected or isolated, is heavily dependent on the forecasting precision such as wind, solar, and load. In this paper, an improved fuzzy prediction horizon forecasting method is developed to address the issue of intermittence and uncertainty problem related to renewable generation and load forecast. In the first phase, a Takagi-Sugeno type fuzzy system is trained with many evolutionary optimization algorithms and established coverage grade indicator to check the accuracy of interval forecast. Secondly, a wind, solar, and load forecaster is developed for renewable microgrid test bed which is located in Beijing, China. One day and one step ahead results for the proposed forecaster are expressed with lowest RMSE and training time. In order to check the efficiency of the proposed method, a comparison is carried out with the existing models. The fuzzy interval-based model for the microgrid test bed will help to formulate the energy management problem with more accuracy and robustness.