  Electricity generated from renewable energy sources has been established as
an efficient remedy for both energy shortages and the environmental pollution
stemming from conventional energy production methods. Solar and wind power are
two of the most dominant renewable energy sources. The accurate forecasting of
the energy generation of those sources facilitates their integration into
electric grids, by minimizing the negative impact of uncertainty regarding
their management and operation. This paper proposes a novel methodology for
deterministic wind and solar energy generation forecasting for multiple
generation sites, utilizing multi-location weather forecasts. The method
employs a U-shaped Temporal Convolutional Auto-Encoder (UTCAE) architecture for
temporal processing of weather-related and energy-related time-series across
each site. The Multi-sized Kernels convolutional Spatio-Temporal Attention
(MKST-Attention), inspired by the multi-head scaled-dot product attention
mechanism, is also proposed aiming to efficiently transfer temporal patterns
from weather data to energy data, without a priori knowledge of the locations
of the power stations and the locations of provided weather data. The conducted
experimental evaluation on a day-ahead solar and wind energy forecasting
scenario on five datasets demonstrated that the proposed method achieves top
results, outperforming all competitive time-series forecasting state-of-the-art
methods.
