https://scholars.lib.ntu.edu.tw/handle/123456789/631053
標題: | Deep learning–based neural networks for day-ahead power load probability density forecasting | 作者: | Zhou, Yanlai Zhu, Di Chen, Hua Guo, Shenglian Xu, Chong Yu FI-JOHN CHANG |
關鍵字: | Deep learning; Energy efficiency; Monotone composite quantile regression neural network (MCQRNN); Probability density forecast; Regional power load; Smart grid | 公開日期: | 1-二月-2023 | 出版社: | SPRINGER HEIDELBERG | 卷: | 30 | 期: | 7 | 來源出版物: | Environmental Science and Pollution Research | 摘要: | Energy efficiency is crucial to greenhouse gas (GHG) emission pathways reported by the Intergovernmental Panel on Climate Change. Electrical overload frequently occurs and causes unwanted outages in distribution networks, which reduces energy utilization efficiency and raises environmental risks endangering public safety. Electrical load, however, has a dynamically fluctuating behavior with notoriously nonlinear hourly, daily, and seasonal patterns. Accurate and reliable load forecasting plays an important role in scheduling power generation processes and preventing electrical systems from overloading; nevertheless, such forecasting is fundamentally challenging, especially under highly variable power load and climate conditions. This study proposed a deep learning–based monotone composite quantile regression neural network (D-MCQRNN) model to extract the multiple non-crossing and nonlinear quantile functions while conquering the drawbacks of error propagation and accumulation encountered in multi-step-ahead probability density forecasting. The constructed models were assessed by an hourly power load series collected at the electric grid center of Henan Province in China in two recent years, along with the corresponding meteorological data collected at 16 monitoring stations. The results demonstrated that the proposed D-MCQRNN model could significantly alleviate the time-lag and biased-prediction phenomena and noticeably improve the accuracy and reliability of multi-step-ahead probability density forecasts on power load. Consequently, the proposed model can significantly reduce the risk and impact of overload faults and effectively promote energy utilization efficiency, thereby mitigating GHG emissions and moving toward cleaner energy production. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139484421&doi=10.1007%2fs11356-022-23305-0&partnerID=40&md5=7e465e27da555ab1081496c8e0fe212d https://scholars.lib.ntu.edu.tw/handle/123456789/631053 |
ISSN: | 09441344 | DOI: | 10.1007/s11356-022-23305-0 |
顯示於: | 生物環境系統工程學系 |
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