NDLI: Next day electric load forecasting using Artificial Neural Networks

Content Provider	IEEE Xplore Digital Library
Author	Velasco, L.C.P. Palahang, P.N.C. Villezas, C.R. Dagaang, J.A.A.
Copyright Year	2015
Description	Author affiliation: Convergys Corp., Cebu City, Philippines (Dagaang, J.A.A.) \|\| WebForest IT Consultancy, Cagayan de Oro, Philippines (Palahang, P.N.C.) \|\| Dept. of Inf. Technol., Mindanao State Univ.-Iligan Inst. of Technol., Iligan City, Philippines (Velasco, L.C.P.) \|\| Adv. World Syst., Inc., Cebu City, Philippines (Villezas, C.R.)
Abstract	The use of Artificial Neural Networks (ANN) by power distribution companies has gained a wide reception due to its ability to predict close to accurate forecasted electric load consumption. A local power utility company in the Philippines has existing data of its electric load consumption however there is no ANN model that can process this data to produce close to accurate forecasted load which is the requirement of their electric market in nominating electric load. To solve this problem, this study developed an electric load forecasting model using ANN. Electric load data preparation, neural network model integration using the Fast Artificial Neural Network (FANN) library and testing using Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) as error measures were conducted. Results showed that the electric load forecasting model yielded a MAPE of less than 1% and a RMSE that is close to 0. The results obtained clearly suggest that ANN model is a viable forecasting technique for a next day electric load forecasting system.
Starting Page	1
Ending Page	6
File Size	440037
Page Count	6
File Format	PDF
e-ISBN	9781509003600
DOI	10.1109/HNICEM.2015.7393166
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-12-09
Publisher Place	Philippines
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Electric Power Load Forecasting Load forecasting Artificial Neural Network Artificial neural networks Companies Predictive models Neural Networks Electric Load Libraries Load modeling
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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