Title

Short-term electrical load forecasting using a Fuzzy ARTMAP neural network

Abstract

Accurate electrical load forecasting is a necessary part of resource management for power generating companies. The better the hourly load forecast, the more closely the power generating assets of the company can be configured to minimize the cost. Automation of this process is a profitable goal and neural networks have shown promising results in achieving this goal. The most often used neural network to solve the electric load forecasting problem is the backpropagation neural network architecture. Although the performance of the back-propagation neural network architecture has been encouraging, it is worth noting that it suffers from the slow convergence problem and the difficulty of interpreting the answers that the architecture provides. A neural network architecture that does not suffer from the above mentioned drawbacks is the Fuzzy ARTMAP neural network, developed by Carpenter, Grossberg, and their colleagues at Boston University. In this work we applied the Fuzzy ARTMAP neural network to the electric load forecasting problem. We performed numerous experiments to forecast the electrical load. The experiments showed that the Fuzzy ARTMAP architecture yields as accurate electrical load forecasts as a back-propagation neural network with training time a small fraction of the training time required by the back-propagation neural network.

Publication Date

3-25-1998

Publication Title

Proceedings of SPIE - The International Society for Optical Engineering

Volume

3390

Number of Pages

181-191

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1117/12.304804

Socpus ID

85076951161 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85076951161

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