NDLI: Multi-GRU prediction system for electricity generation's planning and operation

Content Provider	IET Digital Library
Author	Li, Weixian Logenthiran, Thillainathan Woo, Wai Lok
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.
Starting Page	1630
Ending Page	1637
Page Count	8
ISSN	17518687
Volume Number	13
e-ISSN	17518695
Issue Number	Issue 9, May (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-gtd/13/9
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-gtd.2018.6081
Journal	IET Generation, Transmission & Distribution
Publisher Date	2018-11-15
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Data Collection Double Input Feeding Method Economic Development Electricity Generation Planning Electricity Market Gated Recurrent Unit Load Forecasting Mean Absolute Percentage Error MultiFeature Input Model MultiGRU Forecast Model MultiGRU Prediction System Multiple Feeding Method Power Generation Economics Power Generation Planning Power Industries Power System Economics Power System Managemen Power System Operation Power System Planning And Layout Regression Analysis Regression Model Single Input Feeding Method Statistics Time Steps Model Window Method
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Energy Engineering and Power Technology Electrical and Electronic Engineering

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2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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