TY - JOUR
T1 - Data-Driven Photovoltaic Generation Forecasting Based on a Bayesian Network with Spatial-Temporal Correlation Analysis
AU - Zhang, Ruiyuan
AU - Ma, Hui
AU - Hua, Wen
AU - Saha, Tapan Kumar
AU - Zhou, Xiaofang
N1 - Funding Information:
Manuscript received March 21, 2019; revised May 21, 2019; accepted June 11, 2019. Date of publication June 26, 2019; date of current version January 16, 2020. Wen Hua was supported by the Australian Queensland Government Grant AQRF12516. Paper no. TII-19-1013. (Corresponding author: Hui Ma.) The authors are with The University of Queensland, Brisbane, QLD 4072, Australia (e-mail:, [email protected]; [email protected]. edu.au; [email protected]; [email protected]; [email protected]).
Publisher Copyright:
© 2005-2012 IEEE.
PY - 2020/3
Y1 - 2020/3
N2 - Spatiotemporal analysis has been recognized as one of the most promising techniques to improve the accuracy of photovoltaic (PV) generation forecasts. In recent years, PV generation data of a number of PV systems distributed in a geographical locale have become increasingly available. This paper conducts a thorough investigation of the spatial-temporal correlation amongst PV generation data of distributed PV systems. PV generation data of different PV systems located at different sites may exhibit similar time varying patterns. To quantify such spatial correlation, a suitable spatial similarity metric is chosen and its applicability is examined. To evaluate the temporal correlations amongst the PV generation data collected from distributed PV systems, a shape-based distance metric is proposed. A data-driven inference model, built on a Bayesian network, is developed for a very short-term PV generation forecast (less than 30 min). The model utilizes historic PV generation and weather data, and incorporates the abovementioned spatial similarity and temporal correlation to support the PV output forecast. The experiment results show that the proposed method achieves a promising performance compared to a number of baseline methods.
AB - Spatiotemporal analysis has been recognized as one of the most promising techniques to improve the accuracy of photovoltaic (PV) generation forecasts. In recent years, PV generation data of a number of PV systems distributed in a geographical locale have become increasingly available. This paper conducts a thorough investigation of the spatial-temporal correlation amongst PV generation data of distributed PV systems. PV generation data of different PV systems located at different sites may exhibit similar time varying patterns. To quantify such spatial correlation, a suitable spatial similarity metric is chosen and its applicability is examined. To evaluate the temporal correlations amongst the PV generation data collected from distributed PV systems, a shape-based distance metric is proposed. A data-driven inference model, built on a Bayesian network, is developed for a very short-term PV generation forecast (less than 30 min). The model utilizes historic PV generation and weather data, and incorporates the abovementioned spatial similarity and temporal correlation to support the PV output forecast. The experiment results show that the proposed method achieves a promising performance compared to a number of baseline methods.
KW - Bayesian networks
KW - forecast
KW - photovoltaic (PV) output
KW - spatial and temporal correlation
UR - http://www.scopus.com/inward/record.url?scp=85078696333&partnerID=8YFLogxK
U2 - 10.1109/TII.2019.2925018
DO - 10.1109/TII.2019.2925018
M3 - Journal article
AN - SCOPUS:85078696333
SN - 1551-3203
VL - 16
SP - 1635
EP - 1644
JO - IEEE Transactions on Industrial Informatics
JF - IEEE Transactions on Industrial Informatics
IS - 3
M1 - 8746200
ER -