Short term electric load prediction using Fuzzy BP
|
| Title | Short term electric load prediction using Fuzzy BP |
| Authors | |
| Abstract | 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. |
| Publisher | University of Zagreb, University Computing Centre - SRCE |
| Date | 1970-01-01 |
| Source | Journal of Computing and Information Technology Vol 15, No 3 (2007) |
| Rights | CIT. Journal of Computing and Information Technology is an open access journal.Authors who publish with this journal agree to the following terms:Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work´s authorship and initial publication in this journal.Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal´s published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access). |