Short-Term Load Forecasting Based on Adaptive Neuro-Fuzzy Inference System
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| Title | Short-Term Load Forecasting Based on Adaptive Neuro-Fuzzy Inference System |
| Authors | |
| Abstract | Accurate load forecasting helps stabilize the system by triggering the appropriate actions if needed such as planning for emergency dispatch and load switching for short-term solution and building or upgrading facilities for long-term solution. The Short Term Load Forecasting (STLF) provides information for utilities’ system planners so that they can come up with a short-term solution to protect the transmission and distribution systems and to better serve the customers. This article provides a way of accurately predicting one-hour-ahead load of a utility company located in the North America region (hereafter this utility will be referred to as NAUC) based on Adaptive Neuro-Fuzzy Inference System (ANFIS). The inputs to the ANFIS are the next-hour temperature, next-hour dew point, day of the week, hour of the day, and the current-hour load. The output is the next-hour load of the entire system. The ANFIS based method can accurately predict the next-hour load to an accuracy of 2.5%. |
| Publisher | ACADEMY PUBLISHER |
| Date | 2011-11-01 |
| Source | Journal of Computers Vol 6, No 11 (2011) |
| Rights | Copyright © ACADEMY PUBLISHER - All Rights Reserved.To request permission, please check out URL: http://www.academypublisher.com/copyrightpermission.html. |