期刊
IEEE TRANSACTIONS ON POWER SYSTEMS
卷 35, 期 3, 页码 1881-1897出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRS.2019.2946701
关键词
Load modeling; Load forecasting; Predictive models; Forecasting; Probabilistic logic; Bagging; Boosting; Forecasting; low-voltage load; deep learning; ensemble learning; K nearest neighbor
资金
- National Key R&D Program of China [2018YFB0905000]
- National Natural Science Foundation of China [51877189, 51761135015]
- Young Elite Scientists Sponsorship Program by Chinese Association of Science and Technology [2018QNRC001]
- Hundred Talents Program of Zhejiang University
Accurate and reliable low-voltage load forecasting is critical to optimal operation and control of distribution network and smart grid. However, compared to traditional regional load forecasting at high-voltage level, it faces tough challenges due to the inherent high uncertainty of the low-capacity load and distributed renewable energy integrated in the demand side. This paper proposes a novel hybrid ensemble deep learning (HEDL) approach for deterministic and probabilistic low-voltage load forecasting. The deep belief network (DBN) is applied to low-voltage load point prediction with the strong ability of approximating nonlinear mapping. A series of ensemble learning methods including bagging and boosting variants are introduced to improve the regression ability of DBN. In addition, the differencing transformation technique is utilized to ensure the stationarity of load time series for the application bagging and boosting methods. On the basis of the integrated thought of ensemble learning, a new hybrid ensemble algorithm is developed via integrating multiple separate ensemble methods. Considering the diversity in various ensemble algorithms, an effective K nearest neighbor classification method is utilized to adaptively determine the weights of sub-models. Furthermore, HEDL based probabilistic forecasting is proposed by taking advantage of the inherent resample idea in bagging and boosting. The effectiveness of the HEDL method for both deterministic and probabilistic forecasting has been systematically verified based on realistic load data from East China and Australia, indicating its promising prospective for practical applications in distribution networks.
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