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<p>As distributed energy systems become increasingly prevalent, residential energy systems (RES) equipped with photovoltaics (PV) face significant challenges in maintaining supply-demand balance due to power output fluctuations. This necessitates short-term PV power prediction methods that effectively balance accuracy and deployment cost. To address this issue, this paper proposes a novel short-term PV power prediction approach based on low-cost ground-based sky image sequences: the 3DCNN-DLinear model. The method leverages fisheye camera-captured sky images to extract spatiotemporal features via a three-dimensional convolutional neural network (3DCNN), and integrates a lightweight time-series model, DLinear, to enable efficient prediction. The proposed model was evaluated using real-world data collected in Changping District, Beijing, China. A comparative analysis involving six mainstream time-series models confirmed that DLinear achieved the lowest overall prediction error. Further experiments demonstrated that the 3DCNN-DLinear model reduced RMSE by 49.28%, 9.56%, and 8.82% for 30-, 60-, and 90-minute prediction tasks, respectively, compared to the baseline 3DCNN-LSTM model. Additionally, the study examined the contribution of sky image data to prediction accuracy, revealing significant improvements under varying conditions. Notably, RMSE was reduced by 40.4% and 30.5% under sunny and cloudy conditions, respectively, for the 60-minute task. Overall, the proposed method offers an effective and economically viable solution to improve the predictive performance and intelligent scheduling of RES.</p>

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A short-term time-series prediction approach for photovoltaic power generation in residential energy systems

  • 期刊论文

  • 发表日期：

    2025年10月29日

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  • Yongyi Su,
  • Weirong Zhang,
  • Ning Zhou,
  • Gaofeng Deng,
  • Zhichao Wang

摘要

As distributed energy systems become increasingly prevalent, residential energy systems (RES) equipped with photovoltaics (PV) face significant challenges in maintaining supply-demand balance due to power output fluctuations. This necessitates short-term PV power prediction methods that effectively balance accuracy and deployment cost. To address this issue, this paper proposes a novel short-term PV power prediction approach based on low-cost ground-based sky image sequences: the 3DCNN-DLinear model. The method leverages fisheye camera-captured sky images to extract spatiotemporal features via a three-dimensional convolutional neural network (3DCNN), and integrates a lightweight time-series model, DLinear, to enable efficient prediction. The proposed model was evaluated using real-world data collected in Changping District, Beijing, China. A comparative analysis involving six mainstream time-series models confirmed that DLinear achieved the lowest overall prediction error. Further experiments demonstrated that the 3DCNN-DLinear model reduced RMSE by 49.28%, 9.56%, and 8.82% for 30-, 60-, and 90-minute prediction tasks, respectively, compared to the baseline 3DCNN-LSTM model. Additionally, the study examined the contribution of sky image data to prediction accuracy, revealing significant improvements under varying conditions. Notably, RMSE was reduced by 40.4% and 30.5% under sunny and cloudy conditions, respectively, for the 60-minute task. Overall, the proposed method offers an effective and economically viable solution to improve the predictive performance and intelligent scheduling of RES.