Academic Journal
Real-time anomaly detection for very short-term load forecasting
| العنوان: | Real-time anomaly detection for very short-term load forecasting |
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| المؤلفون: | Jian LUO, Tao HONG, Meng YUE |
| المصدر: | Journal of Modern Power Systems and Clean Energy, Vol 6, Iss 2, Pp 235-243 (2018) |
| بيانات النشر: | IEEE, 2018. |
| سنة النشر: | 2018 |
| المجموعة: | LCC:Production of electric energy or power. Powerplants. Central stations LCC:Renewable energy sources |
| مصطلحات موضوعية: | Real-time anomaly detection, Very short-term load forecasting, Multiple linear regression, Data cleansing, Production of electric energy or power. Powerplants. Central stations, TK1001-1841, Renewable energy sources, TJ807-830 |
| الوصف: | Abstract Although the recent load information is critical to very short-term load forecasting (VSTLF), power companies often have difficulties in collecting the most recent load values accurately and timely for VSTLF applications. This paper tackles the problem of real-time anomaly detection in most recent load information used by VSTLF. This paper proposes a model-based anomaly detection method that consists of two components, a dynamic regression model and an adaptive anomaly threshold. The case study is developed using the data from ISO New England. This paper demonstrates that the proposed method significantly outperforms three other anomaly detection methods including two methods commonly used in the field and one state-of-the-art method used by a winning team of the Global Energy Forecasting Competition 2014. Finally, a general anomaly detection framework is proposed for the future research. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2196-5625 2196-5420 |
| Relation: | http://link.springer.com/article/10.1007/s40565-017-0351-7; https://doaj.org/toc/2196-5625; https://doaj.org/toc/2196-5420 |
| DOI: | 10.1007/s40565-017-0351-7 |
| URL الوصول: | https://doaj.org/article/ee72f89010d04c75b2cb9c10070c788d |
| رقم الانضمام: | edsdoj.72f89010d04c75b2cb9c10070c788d |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 21965625 21965420 |
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| DOI: | 10.1007/s40565-017-0351-7 |