Multi-GRU Prediction System for Electricity Generation's Planning and Operation

Li, Weixian, Thillainathan, Logenthiran and Woo, Wai Lok (2019) Multi-GRU Prediction System for Electricity Generation's Planning and Operation. IET Generation, Transmission & Distribution, 13 (9). pp. 1630-1637. ISSN 1751-8687

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Official URL: https://doi.org/10.1049/iet-gtd.2018.6081

Abstract

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.

Item Type: Article
Uncontrolled Keywords: power generation economics; regression analysis; power generation planning; load forecasting
Subjects: H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Paul Burns
Date Deposited: 27 Mar 2019 12:07
Last Modified: 10 Oct 2019 18:30
URI: http://nrl.northumbria.ac.uk/id/eprint/38565

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