What question did this study set out to answer?

This research aims to develop a robust framework for forecasting power outputs from multiple renewable energy sites, especially with limited data.

Joint Wind–Photovoltaic Power Forecasting Based on MultiTask Learning With Partial Site Observations

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Forecasting performance improves for renewable energy sites using advanced data techniques, suggesting better reliability in power generation.

This research aims to develop a robust framework for forecasting power outputs from multiple renewable energy sites, especially with limited data.
Utilized the Kriging method for data interpolation at sites without measurements.
Employed the measure correlate–predict (MCP) method for sites with only short-term data.
Compared three joint forecasting architectures within a multi-task learning model.
Kriging and MCP methods effectively estimated missing data for renewable generation sites.
Multi-task regression using least absolute shrinkage and selection operator achieved highest forecasting accuracy among models tested.

Zhao et al. (2026) studied this question.

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Forecasting performance improves for renewable energy sites using advanced data techniques, suggesting better reliability in power generation.