Artificial Neural Network based Artificial Intelligent Algorithms for Accurate Monthly Load Forecasting of Power Consumption

London Journal of Engineering Research
Volume | Issue | Compilation
Authored by Samuel Atuahene , Yukun Bao, Yao Yevenyo Ziggah, Patricia Semwaah Gyan
Classification: NA
Keywords: Energy, Load Forecasting, Back Propagation Neural Network, Radial Basis Function, Extreme Learning Machine.
Language: English

In this study, three artificial neural networks (ANN) techniques (backpropagation (BPNN), radial basis function network (RBFNN) and extreme learning machine (ELM)) were applied for accurate modeling and prediction of monthly load consumption. These models were trained for the first time on the data collected by the United State Energy Information Administration (USEIA) for five sectors from January 1973 to May 2017 (44 years). Performance evaluation of the methods was carried out using various statistical indicators including mean absolute percentage error (MAPE). The results revealed that the value of MAPE for BPNN which gave the optimum model for predicting the monthly load consumption were 0.999999885 and 0.999999069 for training and testing results respectively, ascertaining the accuracy and suitability of the model for monthly load consumption prediction.



author

For Authors

Author Membership provide access to scientific innovation, next generation tools, access to conferences/seminars
/symposiums/webinars, networking opportunities, and privileged benefits.
Authors may submit research manuscript or paper without being an existing member of LJP. Once a non-member author submits a research paper he/she becomes a part of "Provisional Author Membership".

Know more

institutes

For Institutions

Society flourish when two institutions come together." Organizations, research institutes, and universities can join LJP Subscription membership or privileged "Fellow Membership" membership facilitating researchers to publish their work with us, become peer reviewers and join us on Advisory Board.

Know more

subsribe

For Subscribers

Subscribe to distinguished STM (scientific, technical, and medical) publisher. Subscription membership is available for individuals universities and institutions (print & online). Subscribers can access journals from our libraries, published in different formats like Printed Hardcopy, Interactive PDFs, EPUBs, eBooks, indexable documents and the author managed dynamic live web page articles, LaTeX, PDFs etc.

Know more