Open AccessMulti‐GRU prediction system for electricity generation's planning and operation
Author(s) -
Li Weixian,
Logenthiran Thillainathan,
Woo Wai Lok
Publication year - 2019
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2018.6081
Subject(s) - electricity , electric power system , electricity generation , electricity market , computer science , process (computing) , data mining , power (physics) , engineering , physics , quantum mechanics , electrical engineering , operating system
Electricity generation's planning and operation have been key factors for any economic development in the power industries but it can only be achieved if the generation was accurately forecasted. This made forecasting systems essential to planning and operation in the electricity market. In this study, a novel system called multi‐GRU (gated recurrent unit) prediction system was developed based on GRU models. It has four level of prediction process which consists of data collection and pre‐processed module, multi‐features input model, multi‐GRU forecast model and mean absolute percentage error. The data collection and pre‐processed module collect and reorganise the real‐time data using the window method. Multi‐features input model uses single input feeding method, double input feeding method, and multiple feeding method for features input to the multi‐GRU forecast model. Multi‐GRU forecast model integrates GRU variation such as regression model, regression with time steps model, memory between batches model, and stacked model to predict the future electricity generation and uses mean absolute percentage error to evaluate the prediction accuracy. The proposed systems achieved high accuracy prediction results for electricity generation.