Efficient Deterministic Renewable Energy Forecasting Guided by Multiple-Location Weather Data

• URL: http://arxiv.org/abs/2404.17276v1
• Date: Fri, 26 Apr 2024 09:30:55 GMT
• Title: Efficient Deterministic Renewable Energy Forecasting Guided by Multiple-Location Weather Data
• Authors: Charalampos Symeonidis, Nikos Nikolaidis,
• Abstract summary: This paper proposes a novel methodology for deterministic wind and solar energy generation forecasting for multiple generation sites. The method employs a U-shaped Temporal Convolutional Auto-Encoder architecture for temporal processing of weather-related and energy-related time-series. 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.
• Score: 4.048814984274799
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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