@article{Su2025, 
author = {Yongyi Su and Weirong Zhang and Ning Zhou and Gaofeng Deng and Zhichao Wang},
title = {A short-term time-series prediction approach for photovoltaic power generation in residential energy systems},
year = {2025},
journal = {Building Simulation},
volume = {18},
number = {10},
pages = {2757-2776},
keywords = {time-series prediction, PV, short-term prediction, residential energy systems},
url = {https://www.sciopen.com/article/10.1007/s12273-025-1336-5},
doi = {10.1007/s12273-025-1336-5},
abstract = {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.}
}