PV Forecasting Model Development and Impact Assessment via Imputation of Missing PV Power Data

Abstract

Photovoltaics (PV) have attracted considerable attention owing to their longer lifespans and higher generation potentials compared with other renewable energy sources. However, the intermittent nature of PV systems can degrade the power quality, hindering their widespread adoption. To mitigate the power-quality degradation resulting from the proliferation of PV, high forecasting accuracy is essential. However, missing data during the development of forecasting models can degrade performance. Therefore, appropriate imputation procedures are required. Typically, linear imputation is used. However, there is a tendency for the performance of the forecasting model to decline owing to errors between the actual and imputed values. In this study, we addressed missing PV power data using direct deletion, linear imputation, k-nearest neighbors imputation, and Generative Adversarial Imputation Nets. Subsequently, to assess the impact of weather variability on the imputation performance, we employed the “sky status” to categorize the replaced data and analyze whether differences in imputation performance emerged. Finally, we developed a PV forecasting model using the replaced data and evaluated its forecasting performance.

Keywords

• CNN-GRU
• GAIN
• KNN
• missing data imputation
• PV forecasting model