PHOTOVOLTAIC POWER INTERVAL PREDICTION BASED ON QD AND CAUSAL ATTENTION TCN

Cui Jinggang; Wang Fang; Ye Zefu; Zhu Zhujun; Yan Gaowei

doi:10.19912/j.0254-0096.tynxb.2022-1730

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (3) : 488-495. DOI: 10.19912/j.0254-0096.tynxb.2022-1730

PHOTOVOLTAIC POWER INTERVAL PREDICTION BASED ON QD AND CAUSAL ATTENTION TCN

Cui Jinggang¹, Wang Fang¹, Ye Zefu², Zhu Zhujun², Yan Gaowei¹

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Abstract

For the existing problems of short-term photovoltaic power interval prediction, a framework combining a time convolution neural network with an attention mechanism is proposed. This framework imposes strict constraints on the temporal causal order in the attention mechanism, applies residual blocks to enhance the information mining ability of the model, and utilizes model parameters for quality-driven interval loss simultaneously, which ultimately improves the short-term power interval prediction effect. The simulation experiments based on the local meteorological data and historical photovoltaic power data of a photovoltaic power station in Hebei Province, China, show that compared with the traditional sequence prediction method or interval loss, the power interval prediction method proposed in this paper is more effective for scientific dispatching and decision-making of the power grid in continuous time and different weather types.

Key words

PV power / power forecasting / deep learning / temporal convolutional network / causal attention mechanism / quality-driven loss

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Cui Jinggang, Wang Fang, Ye Zefu, Zhu Zhujun, Yan Gaowei. PHOTOVOLTAIC POWER INTERVAL PREDICTION BASED ON QD AND CAUSAL ATTENTION TCN[J]. Acta Energiae Solaris Sinica. 2024, 45(3): 488-495 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1730

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