Name
Abstract | ||
|---|---|---|
This paper aims to study the short-term load forecasting of electricity by using an extended self-organizing map. We first adopt a traditional Kohonen self-organizing map (SOM) to learn time-series load data with weather information as parameters. Then, in order to improve the accuracy of the prediction, an extension of SOM algorithm based on error-correction learning rule is used, and the estimation of the peak load is achieved by averaging the output of all the neurons. Finally, as an implementation example, data of electricity demand from New York Independent System Operator (ISO) are used to verify the effectiveness of the learning and prediction for the proposed methods. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11427469_102 | ISNN (3) |
Keywords | Field | DocType |
new york independent system,short-term load forecasting,electricity demand,peak load,traditional kohonen self-organizing map,extended self-organizing map,peak load forecasting,som algorithm,time-series load data,implementation example,time series,error correction | Mean absolute percentage error,Electricity,Computer science,Self-organization,Electric power system,Self-organizing map,Error detection and correction,Learning rule,Operator (computer programming),Artificial intelligence,Machine learning | Conference |
Volume | ISSN | ISBN |
3498 | 0302-9743 | 3-540-25914-7 |
Citations | PageRank | References |
2 | 0.46 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shu Fan | 1 | 3 | 1.46 |
| Chengxiong Mao | 2 | 19 | 11.90 |
| Luonan Chen | 3 | 1485 | 145.71 |