Historical PV-output characteristic extraction based weather-type classification strategy and its forecasting method for the day-ahead prediction of PV output

Lingwei Zheng, Ran Su, Xinyu Sun and Siqi Guo

Energy, 2023, vol. 271, issue C

Abstract: With photovoltaic (PV) penetration increasing, PV-output prediction has become a research hotspot. Due to the close correlation between PV-output fluctuation and weather conditions, PV-output prediction models often vary different weather types, while the historical/forecast weather types for modeling are mostly obtained from weather-service providers. However, weather-service providers generally have deficiencies in forecast accuracy, spatio-temporal resolution, and investment/operating costs. Based on the above, this paper changes the current acquisition way of the weather types, and proposes a framework of reversely determining weather types from historical PV-output data. First, the symbol-sequence histograms (SSH) are used to describe the PV-output volatility in a coarse-grained manner. Then, the SSHs are partitionally clustered and a classification rule for weather-types is proposed to label the historical weather types. Next, considering the chaotic characteristics of PV output, a prediction method combining phase-space reconstruction with an extremely learning machine based single-layer forward net is developed to predict the SSH. Finally, the day-ahead weather type is forecasted. Simulations were implemented on the weather-type classification and forecasting using a campus PV-system in East China. The PV-output prediction results show that, compared with weather information from a weather-service supplier, 75-day mean errors are significantly reduced by 15.55% (MAPE) and 12.69% (rRMSE), respectively.

Keywords: Weather-type classification; Weather-type forecast; Symbol-sequence histograms clustering; Phase-space reconstruction; Extremely learning machine; Photovoltaic output prediction (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:271:y:2023:i:c:s0360544223004036

DOI: 10.1016/j.energy.2023.127009

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