Title :
Application of the Kohonen network to short-term load forecasting
Author :
Baumann, Thomas ; Germond, Main J.
Author_Institution :
Siemens AG, Vienna, Austria
Abstract :
This paper analyses the application of Kohonen´s self-organizing feature map to short-term forecasting of daily electrical load. The aim of the paper is to study the feasibility of the Kohonen´s self-organizing feature maps for the classification of electrical loads. The network not only ´learns´ similarities of load patterns in a unsupervised manner, but it uses the information stored in the weight vectors of the Kohonen network to forecast the future load. The results are evaluated by using several months of hourly load data of a real system to train the network, and forecasting the daily loads for two periods of one month. The method is then improved by adding a second type of neural network for weather sensitive correction of the load previously calculated with the Kohonen network. This second type of network is a one-layered linear delta rule network.
Keywords :
learning (artificial intelligence); load forecasting; power engineering computing; power systems; self-organising feature maps; Kohonen network; application; classification; one-layered linear delta rule network; power engineering computing; power systems; self-organizing feature map; short-term load forecasting; training; weather sensitive correction; weight vectors; Artificial neural networks; Casting; Extrapolation; Fault tolerance; Load forecasting; Multi-layer neural network; Supervised learning; Taxonomy; Unsupervised learning; Weather forecasting;
Conference_Titel :
Neural Networks to Power Systems, 1993. ANNPS '93., Proceedings of the Second International Forum on Applications of
Conference_Location :
Yokohama, Japan
Print_ISBN :
0-7803-1217-1
DOI :
10.1109/ANN.1993.264313
