Abstract
Load forecasting is an important component for power system energy management system. Forecasting
means estimating active loads at various load buses ahead of actual load occurrence Training data is classified using
Fuzzy Set Based Classification Method. Temperature data is classified into five fuzzy sets (Very Cold, Cold, Normal,
Hot and Very Hot). Relative Humidity is classified into four fuzzy sets (Very Dry, Dry, Humid and Very Humid). Day
Type is classified into four fuzzy sets (Post-Holiday, Weekday, Pre-Holiday and Holiday). So, depending upon the
temperature, relative humidity and day type, data is classified into eighty classes. After the classification, the neural
network is trained for various classes using the historical data. The multilayer neural network structure has been
used and the training is imparted using back propagation algorithm. . In this article, a Fuzzy Set Classified Neural
Network Approach for Short Term Load Forecasting is attempted and implemented using Matlab 6.5.
Keywords
Fuzzy Set Based Classification
Training of Neural Network
Short term load forecasting
Authors
How to Cite this Article
S.Saranya, B. Vasumathi, M.Sakthivel (2015).
"FUZZY SET CLASSIFIED NEURAL NETWORK APPROACH FOR SHORT TERM LOAD FORECASTING".
International Journal of Contemporary Research in Computer Science and Technology,
1(8), pp. 288-293.
