Coordinated Multisite Wind and Photovoltaic Power Forecasting with Shared Multiscale Representations

As wind and photovoltaic (PV) generation expands, power systems increasingly need coordinated forecasts for multiple sites. Sites share weather influences but respond differently, so a forecasting model must learn common temporal patterns while preserving differences between energy types and individual sites. We propose a method based on shared multiscale representations. A bidirectional gated recurrent unit (BiGRU) extracts historical features. We combine daily variation with recent fluctuations, then use direction-specific information exchange and site-specific heads to produce coordinated predictions. An energy-balanced objective coordinates training across wind and PV tasks. We evaluate the method using simulated power series generated from the fifth-generation atmospheric reanalysis (ERA5) of the European Centre for Medium-Range Weather Forecasts at the geographic coordinates of seven wind and PV sites in the Wujiang region. Rolling annual evaluations show lower mean absolute error than the tested graph and Transformer baselines in every test year under common experimental settings. Component comparisons in a compact model support retaining fine temporal resolution for recent changes. A separate analysis finds that within-energy dependence modeling reduces Brier scores for simultaneous low-output events. These results support shared temporal modeling for coordinated multisite wind–PV forecasting and provide simulation evidence for regional joint-output analysis.