Power generation forecasting for solar plants based on Dynamic Bayesian networks by fusing multi-source information

Zhang, Qiongfang; Yan, Hao; Liu, Yongming

doi:10.1016/j.rser.2024.114691

Power generation forecasting for solar plants based on Dynamic Bayesian networks by fusing multi-source information

Journal Article · Fri Jun 28 00:00:00 EDT 2024 · Renewable and Sustainable Energy Reviews

DOI:https://doi.org/10.1016/j.rser.2024.114691· OSTI ID:3026693

Zhang, Qiongfang ^[1]; Yan, Hao ^[2]; Liu, Yongming ^[1]

Arizona State Univ., Tempe, AZ (United States)
Arizona State Univ., Phoenix, AZ (United States)

A Dynamic Bayesian network (DBN) model for solar power generation forecasting in 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. Additionally, an error compensation mechanism 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 result 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 one hour to one week ahead forecasting.

View Accepted Manuscript (DOE)

Research Organization:: Arizona State Univ., Tempe, AZ (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office

Grant/Contract Number:: EE0009354

OSTI ID:: 3026693

Alternate ID(s):: OSTI ID: 2404459
OSTI ID: 2475456

Journal Information:: Renewable and Sustainable Energy Reviews, Journal Name: Renewable and Sustainable Energy Reviews Vol. 202; ISSN 1364-0321

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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