Abstract
Load forecasting is considered vital along with many other important entities required for assessing the reliability of power system. Thus, the primary concern is not to forecast load with a novel model, rather to forecast load with the highest accuracy. Short-term load forecast accuracy is often hindered due to various load impacting factors. Two of the major impacting factors are day-ahead weather forecast and subsequent variation in electricity demand that is independent of weather. To tackle the uncertainty in short-term load forecasting, this paper presents a neural network-based load forecasting technique for short-term horizon based on data corresponding to a U.S. independent system operator. With the real life data, a better understanding of forecasting error is carried out while further identifying the time periods when the load is supposedly to be over- or under-forecast.
Original language | English |
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Title of host publication | 2016 International Joint Conference on Neural Networks (IJCNN) |
Place of Publication | Piscataway, NJ |
Publisher | IEEE |
Pages | 4970-4975 |
Number of pages | 6 |
ISBN (Print) | 978-1-5090-0620-5 |
DOIs | |
Publication status | Published - 2016 |
Event | IJCNN 2016: International Joint Conference on Neural Networks - Vancouver, Canada Duration: 24 Jul 2016 → 29 Jul 2016 |
Conference
Conference | IJCNN 2016 |
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Country/Territory | Canada |
City | Vancouver |
Period | 24/07/16 → 29/07/16 |
Keywords
- Error analysis
- forecasting
- forecast error
- load forecast uncertainty
- neural network
- short-term load forecast