Interval-Valued Multi-Step-Ahead Forecasting of Green Electricity Supply Using Augmented Features and Deep-Learning Algorithms
學年 114
學期 1
出版（發表）日期 2025-10-06
作品名稱 Interval-Valued Multi-Step-Ahead Forecasting of Green Electricity Supply Using Augmented Features and Deep-Learning Algorithms
作品名稱（其他語言）
著者 Tzu-Chi Liu; Chih-Te Yang; I-Fei Chen; Chi-Jie Lu
單位
出版者
著錄名稱、卷期、頁數 13(19), p.1-24
摘要 Accurately forecasting the interval-valued green electricity (GE) supply is challenging due to the unpredictable and instantaneous nature of its source; yet, reliable multi-step-ahead forecasting is essential for providing the lead time required in operations, resource allocation, and system management. This study proposes an augmented-feature multi-step interval-valued forecasting (AFMIF) scheme that aims to address the challenges in forecasting interval-valued GE supply data by extracting additional features hidden within an interval. Unlike conventional methods that rely solely on original interval bounds, AFMIF integrates augmented features that capture statistical and dynamic properties to reveal hidden patterns. These features include basic interval boundaries and statistical distributions from an interval. Three effective forecasting methods, based on gated recurrent units (GRUs), long short-term memory (LSTM), and a temporal convolutional network (TCN), are constructed under the proposed AFMIF scheme, while the mean ratio of exclusive-or (MRXOR) is used to evaluate the forecasting performance. Two different real datasets of wind-based GE supply data from Belgium and Germany are used as illustrative examples. Empirical results demonstrate that the proposed AFMIF scheme with GRUs can generate promising results, achieving a mean MRXOR of 0.7906 from the Belgium data and 0.9719 from the Germany data for one-step- to three-steps-ahead forecasting. Moreover, the TCN yields an average improvement of 13% across all time steps with the proposed scheme. The results highlight the potential of the AFMIF scheme as an effective alternative approach for accurate multi-step-ahead interval-valued GE supply forecasting that offers practical benefits supporting GE management.
關鍵字 green electricity; interval-valued data; multi-step-ahead forecasting; augmented features; deep learning
語言 en
ISSN 2227-7390
期刊性質 國外
收錄於 SCI Scopus
產學合作
通訊作者 I-Fei Chen, Chi-Jie Lu
審稿制度 否
國別 CHE
公開徵稿
出版型式 ,電子版
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/128118 )