Short Term Electric Load Prediction Using Fuzzy BP

Hari Seetha, R. Saravanan


The privatization of electricity industry in various parts of the world has increased the significance of the load prediction problem and in particular there is a need to understand and predict the demand for power with greater accuracy, even in case of imprecise input data. Prediction of power demand is essential for an efficient operation of any utility company. In this paper, a fuzzy version of neural network, namely Fuzzy back propagation network (Fuzzy BP) has been developed for short-term electric load prediction. The load is predicted using fuzzy back propagation algorithm. This model is capable of handling imprecise information in input data. The proposed architecture consists of a module with 51 inputs and 24 outputs. The inputs are fuzzified and the outputs are crisp values representing the predicted load. The proposed method is implemented in MATLAB. The simulation results are presented for each day (24 hours) of the week. Besides this, a multi layer perceptron (MLP) was also implemented separately and the load was predicted using back propagation algorithm. The results obtained from Fuzzy BP were found to be satisfactory when compared to those of MLP network.

Full Text:



Creative Commons License
This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.

Crossref Similarity Check logo

Crossref logologo_doaj

 Hrvatski arhiv weba logo