1. Power generation forecasting for solar plants based on Dynamic Bayesian networks by fusing multi-source information.
- Author
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Zhang, Qiongfang, Yan, Hao, and Liu, Yongming
- Subjects
- *
ARTIFICIAL neural networks , *PHOTOVOLTAIC power generation , *SOLAR power plants , *BAYESIAN analysis , *SOLAR energy - Abstract
A Dynamic Bayesian network (DBN) model for solar power generation forecasting in photovoltaic (PV) solar plants is proposed in this paper. The key idea is to fuse sensor data, operational indicators, meteorological data, lagged output power information, and model errors for more accurate short-term (e.g., hours) and mid-term (e.g., days to weeks) power generation forecasting. The proposed DBN augments automated data-driven structure learning with expert knowledge encoding using continuous and categorical data given constraints to represent causal relationships within a solar inverter system. In addition, an error compensation mechanism that uses an error node to capture the temporal fluctuation caused by system degradation or failures is proposed to capture temporal fluctuation. The effectiveness of the DBN on solar power generation forecasting was evaluated by rolling window analysis with one-year testing data collected from a local solar plant. The proposed DBN is compared with four state-of-art methods including support-vector regression (SVR), k-nearest neighbors (kNN), artificial neural network (ANN), and long short-term memory (LSTM) models. The results show that the proposed DBN achieves better accuracy in general, and it is not as data-hungry as some neural network-based models. The proposed DBN is also shown to have robust and consistent forecasting power with different forecasting horizons. The accuracy is 92 %–95 % from 1 h to one week ahead forecasting. • A Dynamic Bayesian Network (DBN) with multi-source information fusion is proposed. • Automated data-driven structure learning is augmented with expert knowledge. • Error compensation mechanism increases forecasting accuracy. • Proposed DBN outperforms other state-of-the-art methods with fewer data. • Proposed DBN delivers multi-horizon solar power generation forecasts. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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