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.

翻译：摘要：可再生能源发电已被确立为缓解传统能源生产方式导致的能源短缺与环境污染问题的有效手段。太阳能与风能是两种最主要的可再生能源。准确预测这些能源的发电量，通过最小化其管理与运营中不确定性带来的负面影响，有助于促进其接入电网。本文提出了一种面向多个发电站点的确定性风能与太阳能发电预测新方法，该方法利用多地点气象预报数据。该方法采用U型时序卷积自编码器架构，对各站点的气象相关与能源相关时间序列进行时序处理。受多头缩放点积注意力机制启发，本文还提出了多尺度核卷积时空注意力，旨在无需预先知晓电站位置与气象数据位置的前提下，高效地将气象数据中的时序模式迁移至能源数据。在五个数据集上开展的日前太阳能与风能预测场景实验评估表明，所提方法取得了顶尖结果，超越了所有具有竞争力的时序预测前沿方法。