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An Artificial Neural Network in Short-Term Electrical Load Forecasting of a University Campus: A Case Study

[+] Author Affiliations
David Palchak, Siddharth Suryanarayanan, Daniel Zimmerle

Colorado State University, Fort Collins, CO

Paper No. ES2012-91300, pp. 709-717; 9 pages
doi:10.1115/ES2012-91300
From:
  • ASME 2012 6th International Conference on Energy Sustainability collocated with the ASME 2012 10th International Conference on Fuel Cell Science, Engineering and Technology
  • ASME 2012 6th International Conference on Energy Sustainability, Parts A and B
  • San Diego, California, USA, July 23–26, 2012
  • Conference Sponsors: Advanced Energy Systems Division, Solar Energy Division
  • ISBN: 978-0-7918-4481-6
  • Copyright © 2012 by ASME

abstract

This paper presents an artificial neural network (ANN) for forecasting the short-term electrical load of a university campus using real historical data from Colorado State University. A spatio-temporal ANN model with multiple weather variables as well as time identifiers, such as day of week and time of day, are used as inputs to the network presented. The choice of the number of hidden neurons in the network is made using statistical information and taking into account the point of diminishing returns. The performance of this ANN is quantified using three error metrics: the mean average percent error (MAPE); the error in the ability to predict the occurrence of the daily peak hour; and the difference in electrical energy consumption between the predicted and the actual values in a 24-hour period. These error measures provide a good indication of the constraints and applicability of these predictions. In the presence of some enabling technologies such as energy storage, rescheduling of non-critical loads, and availability of time of use (ToU) pricing, the possible DSM options that could stem from an accurate prediction of energy consumption of a campus include the identification of anomalous events as well the management of usage.

Copyright © 2012 by ASME

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