A Deep Learning-Based Dual-Scale Hybrid Model for Ultra-Short-Term Photovoltaic Power Forecasting

Yongning Zhang, Xiaoying Ren (), Fei Zhang, Yulei Liu and Jierui Li
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Yongning Zhang: School of Automation and Electrical Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China
Xiaoying Ren: School of New Energy, North China Electric Power University, Beijing 102206, China
Fei Zhang: School of Automation and Electrical Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China
Yulei Liu: Inner Mongolia Huadian Huitengxile Wind Power Co., Ulanqab 013550, China
Jierui Li: School of Automation and Electrical Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China

Sustainability, 2024, vol. 16, issue 17, 1-22

Abstract: Ultra-short-term photovoltaic (PV) power forecasting is crucial in the scheduling and functioning of contemporary electrical systems, playing a key role in promoting renewable energy integration and sustainability. In this paper, a novel hybrid model, termed AI_VMD-HS_CNN-BiLSTM-A, is introduced to tackle the challenges associated with the volatility and unpredictability inherent in PV power output. Firstly, Akaike information criterion variational mode decomposition (AI_VMD) integrates the Akaike information criterion with variational mode decomposition (VMD) and reduces data complexity, enhancing grid optimization and energy efficiency. The adaptive selection of optimal parameters enhances VMD decomposition performance, supporting sustainable energy management. Secondly, the hierarchical scale-transform convolutional architecture (HS_CNN) supplements the traditional convolutional neural network (CNN) with two channels featuring distinct dilation rates, thereby extracting dual levels of time-scale information for a more comprehensive data representation. Finally, a bidirectional long short-term memory neural network (BiLSTM) with an attentional mechanism combines past and future data to enable more accurate forecasts, aiding in carbon reduction and smart grid advancements. Experimentation with data from the Alice Springs PV plant in Australia demonstrates that the proposed AI_VMD-HS_CNN-BiLSTM-A model exhibits superior adaptability and accuracy in multiple time-scale forecasting compared to the baseline models. This approach is important for decision-making and scheduling in grid-connected photovoltaic systems, enhancing energy resilience and promoting the sustainable development of renewable energy.

Keywords: ultra-short-term photovoltaic power forecasting; AI_VMD-HS_CNN-BiLSTM-A; multiple time scales forecasting; sustainable development (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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